Google File System was innovatively created by Google engineers and it is ready for production in record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying commodity hardware. As Google run number of application then Google’s goal became to build a vast storage network out of inexpensive commodity hardware. So Google create its own file system, named as Google File System that is GFS. Google File system is one of the largest file system in operation. Generally Google File System is a scalable distributed file system of large distributed data intensive apps. In the design phase of Google file system, in which the given stress includes component failures , files are huge and files are mutated by appending data. The entire file system is organized hierarchically in directories and identified by pathnames. The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into chunks, and that is the key design parameter. Google File System also uses leases and mutation order in their design to achieve atomicity and consistency. As of there fault tolerance, Google file system is highly available, replicas of chunk servers and master exists.
Google File System (GFS) is a distributed file system designed by Google to provide efficient, scalable, and reliable storage. It is organized hierarchically with directories and files identified by pathnames. Files are divided into chunks which are replicated across multiple servers for fault tolerance. The architecture includes a single master server, multiple chunk servers, and clients. The design focuses on handling partial failures, large files, and append-only mutations. It provides features like leases to ensure consistency, atomic record appends, and snapshots. The master manages the namespace and replication while the system aims for high availability, performance, and scalability.
IRJET- An Integrity Auditing &Data Dedupe withEffective Bandwidth in Cloud St...IRJET Journal
This document proposes a system for secure cloud storage that uses data deduplication, integrity auditing by a third party auditor (TPA), and encryption to improve security, reduce storage usage, and verify data integrity. It compares different levels of data deduplication (byte-level, block-level, file-level) and proposes using a combination of SHA-512 hashing, Merkle hash trees, and AES-128 encryption. Performance analysis shows the proposed system requires less storage space than existing systems by removing duplicate data, and the third party auditor can verify data integrity more efficiently than the cloud service provider.
ANALYSIS OF ATTACK TECHNIQUES ON CLOUD BASED DATA DEDUPLICATION TECHNIQUESneirew J
ABSTRACT
Data in the cloud is increasing rapidly. This huge amount of data is stored in various data centers around the world. Data deduplication allows lossless compression by removing the duplicate data. So, these data centers are able to utilize the storage efficiently by removing the redundant data. Attacks in the cloud computing infrastructure are not new, but attacks based on the deduplication feature in the cloud computing is relatively new and has made its urge nowadays. Attacks on deduplication features in the cloud environment can happen in several ways and can give away sensitive information. Though, deduplication feature facilitates efficient storage usage and bandwidth utilization, there are some drawbacks of this feature. In this paper, data deduplication features are closely examined. The behavior of data deduplication depending on its various parameters are explained and analyzed in this paper.
An Efficient Approach to Manage Small Files in Distributed File SystemsIRJET Journal
This document proposes an efficient approach to manage small files in distributed file systems. It discusses challenges in storing and retrieving a large number of small files due to their metadata size. The proposed system aims to improve performance by designing a new metadata structure that decreases original metadata size and increases file access speed. It presents a system architecture with clients, metadata server and data servers. The system classifies files into different storage types and combines existing techniques into a single architecture. It provides algorithms for reading local metadata and file data in two phases. The performance of the approach is evaluated on a distributed file system environment.
Intelligent file virtualization allows organizations to migrate file data between storage systems in a non-disruptive manner by decoupling logical file access from physical storage locations. With ARX file virtualization, files can be migrated between heterogeneous storage systems without disrupting user access. ARX uses policies to automate file migrations based on attributes like age, type, size and directory. Migrations can be scheduled to occur during off-hours to minimize disruption. This reduces the time, overhead and costs associated with data migration projects compared to traditional approaches.
This document discusses communication between distributed systems using Google infrastructure. It provides an overview of Google's distributed systems, including the Google File System (GFS) and Google App Engine. GFS is optimized for Google's data storage and processing needs. It uses large 64MB chunks and a master-chunkserver architecture to provide reliable, scalable access to data. Google App Engine allows hosting of dynamic web applications and provides execution environments, static file serving, a scalable datastore, and access to Bigtable for large-scale data storage. It handles automatic scaling of applications in response to traffic.
The Google File System is a scalable distributed file system designed to meet the rapidly growing data storage needs of Google. It provides fault tolerance on inexpensive commodity hardware and high aggregate performance to large numbers of clients. Key aspects of its design include handling frequent component failures as the norm, managing huge files up to multiple gigabytes in size containing many objects, optimizing for file appending and sequential reads of appended data, and co-designing the file system interface to increase flexibility for applications. The largest deployment to date includes over 1,000 storage nodes providing hundreds of terabytes of storage.
Ctive directory interview question and answerssankar palla
Active Directory is a centralized database that stores information about a network. It allows for centralized management of users, computers, printers, and other network resources. A domain controller is a server that authenticates users and authorizes access to resources on the network. Active Directory uses protocols like LDAP and KCC to enable replication and management of directory data across multiple domain controllers. Application partitions allow specific Active Directory data to be replicated only to designated domain controllers, providing redundancy.
Google File System (GFS) is a distributed file system designed by Google to provide efficient, scalable, and reliable storage. It is organized hierarchically with directories and files identified by pathnames. Files are divided into chunks which are replicated across multiple servers for fault tolerance. The architecture includes a single master server, multiple chunk servers, and clients. The design focuses on handling partial failures, large files, and append-only mutations. It provides features like leases to ensure consistency, atomic record appends, and snapshots. The master manages the namespace and replication while the system aims for high availability, performance, and scalability.
IRJET- An Integrity Auditing &Data Dedupe withEffective Bandwidth in Cloud St...IRJET Journal
This document proposes a system for secure cloud storage that uses data deduplication, integrity auditing by a third party auditor (TPA), and encryption to improve security, reduce storage usage, and verify data integrity. It compares different levels of data deduplication (byte-level, block-level, file-level) and proposes using a combination of SHA-512 hashing, Merkle hash trees, and AES-128 encryption. Performance analysis shows the proposed system requires less storage space than existing systems by removing duplicate data, and the third party auditor can verify data integrity more efficiently than the cloud service provider.
ANALYSIS OF ATTACK TECHNIQUES ON CLOUD BASED DATA DEDUPLICATION TECHNIQUESneirew J
ABSTRACT
Data in the cloud is increasing rapidly. This huge amount of data is stored in various data centers around the world. Data deduplication allows lossless compression by removing the duplicate data. So, these data centers are able to utilize the storage efficiently by removing the redundant data. Attacks in the cloud computing infrastructure are not new, but attacks based on the deduplication feature in the cloud computing is relatively new and has made its urge nowadays. Attacks on deduplication features in the cloud environment can happen in several ways and can give away sensitive information. Though, deduplication feature facilitates efficient storage usage and bandwidth utilization, there are some drawbacks of this feature. In this paper, data deduplication features are closely examined. The behavior of data deduplication depending on its various parameters are explained and analyzed in this paper.
An Efficient Approach to Manage Small Files in Distributed File SystemsIRJET Journal
This document proposes an efficient approach to manage small files in distributed file systems. It discusses challenges in storing and retrieving a large number of small files due to their metadata size. The proposed system aims to improve performance by designing a new metadata structure that decreases original metadata size and increases file access speed. It presents a system architecture with clients, metadata server and data servers. The system classifies files into different storage types and combines existing techniques into a single architecture. It provides algorithms for reading local metadata and file data in two phases. The performance of the approach is evaluated on a distributed file system environment.
Intelligent file virtualization allows organizations to migrate file data between storage systems in a non-disruptive manner by decoupling logical file access from physical storage locations. With ARX file virtualization, files can be migrated between heterogeneous storage systems without disrupting user access. ARX uses policies to automate file migrations based on attributes like age, type, size and directory. Migrations can be scheduled to occur during off-hours to minimize disruption. This reduces the time, overhead and costs associated with data migration projects compared to traditional approaches.
This document discusses communication between distributed systems using Google infrastructure. It provides an overview of Google's distributed systems, including the Google File System (GFS) and Google App Engine. GFS is optimized for Google's data storage and processing needs. It uses large 64MB chunks and a master-chunkserver architecture to provide reliable, scalable access to data. Google App Engine allows hosting of dynamic web applications and provides execution environments, static file serving, a scalable datastore, and access to Bigtable for large-scale data storage. It handles automatic scaling of applications in response to traffic.
The Google File System is a scalable distributed file system designed to meet the rapidly growing data storage needs of Google. It provides fault tolerance on inexpensive commodity hardware and high aggregate performance to large numbers of clients. Key aspects of its design include handling frequent component failures as the norm, managing huge files up to multiple gigabytes in size containing many objects, optimizing for file appending and sequential reads of appended data, and co-designing the file system interface to increase flexibility for applications. The largest deployment to date includes over 1,000 storage nodes providing hundreds of terabytes of storage.
Ctive directory interview question and answerssankar palla
Active Directory is a centralized database that stores information about a network. It allows for centralized management of users, computers, printers, and other network resources. A domain controller is a server that authenticates users and authorizes access to resources on the network. Active Directory uses protocols like LDAP and KCC to enable replication and management of directory data across multiple domain controllers. Application partitions allow specific Active Directory data to be replicated only to designated domain controllers, providing redundancy.
The document discusses methods for accelerating Perforce workspace syncs and reducing network storage usage as digital assets grow rapidly. It introduces IC Manage Views, which uses dynamic virtual workspaces and local caching to achieve near-instant workspace syncs and reduce network storage by 4x. Benchmark results show IC Manage Views delivers files 2x faster than traditional methods through intelligent file redirection that separates reads from writes. IC Manage Views is compatible with existing storage technologies and scales as users and data grow.
The document describes iDedup, a system for performing inline data deduplication on primary storage systems while minimizing performance impacts. It leverages two insights about duplicated data in real-world workloads: 1) spatial locality exists where duplicated data occurs in sequences of disk blocks, and 2) temporal locality exists where duplicated data is accessed repeatedly close in time. The system performs selective deduplication of block sequences to reduce fragmentation and seeks during reads. It also replaces expensive on-disk deduplication metadata with a smaller in-memory fingerprint cache to reduce writes. Evaluation shows the system achieves 60-70% deduplication with less than 5% CPU overhead and 2-4% increased latency.
Active directory job_interview_preparation_guideabdulkalamattari
This document provides answers to common interview questions about Active Directory. It explains key Active Directory concepts such as domains, organizational units, group policy, forests, and mixed vs native modes. It also covers requirements for installing Active Directory and how to verify a proper installation.
Dr.Hadoop- an infinite scalable metadata management for Hadoop-How the baby e...Dipayan Dev
This document discusses Dr. Hadoop, a new framework proposed by authors Dipayan Dev and Ripon Patgiri to provide efficient and scalable metadata management for Hadoop. It addresses key issues with Hadoop's current single point of failure for metadata on the NameNode. The new framework is called Dr. Hadoop and uses a technique called Dynamic Circular Metadata Splitting (DCMS) that distributes metadata uniformly across multiple NameNodes for load balancing while also preserving metadata locality through consistent hashing and locality-preserving hashing. Dr. Hadoop aims to provide infinite scalability for metadata as data scales to exabytes without affecting throughput.
iaetsd Controlling data deuplication in cloud storageIaetsd Iaetsd
This document discusses controlling data deduplication in cloud storage. It proposes an architecture that provides duplicate check procedures with minimal overhead compared to normal cloud storage operations. The key aspects of the proposed system are:
1) It uses convergent encryption to encrypt data for privacy while still allowing for deduplication of duplicate files.
2) It introduces a private cloud that manages user privileges and generates tokens for authorized duplicate checking in a hybrid cloud architecture.
3) It evaluates the overhead of the proposed authorized duplicate checking scheme and finds it incurs negligible overhead compared to normal cloud storage operations.
Data Analytics: HDFS with Big Data : Issues and ApplicationDr. Chitra Dhawale
This document provides information about a course on data analytics. It outlines the course outcomes, which include developing scalable systems using Apache and Hadoop, writing MapReduce applications, differentiating SQL and NoSQL, and analyzing and developing big data solutions using Hive and Pig. The document also describes some of the topics that will be covered in the course, including distributed file systems and their issues, an introduction to big data, characteristics of big data, types of big data, and comparisons between traditional and big data approaches.
Methodology for Optimizing Storage on Cloud Using Authorized De-Duplication –...IRJET Journal
This document summarizes a research paper that proposes a methodology for optimizing storage on the cloud using authorized de-duplication. It discusses how de-duplication works to eliminate duplicate data and optimize storage. The key steps are chunking files into blocks, applying secure hash algorithms like SHA-512 to generate unique hashes for each block, and comparing hashes to reference duplicate blocks instead of storing multiple copies. It also discusses using cryptographic techniques like ciphertext-policy attribute-based encryption for authentication and security on public clouds. The proposed approach aims to optimize storage while providing authorized de-duplication functionality.
This document provides an overview of the key differences between storage area networks (SANs) and network attached storage (NAS). It explains that a NAS is a single network storage device that operates on data files, while a SAN connects multiple storage devices that can share data. The document also discusses how NAS and SAN connect and communicate with other devices on the network and how they are sometimes used together in a unified SAN configuration.
This document discusses Relational Database Management Systems (RDBMS). It provides an overview of early database systems like hierarchical and network models. It then describes the key concepts of RDBMS including relations, attributes, and using tables, rows, and columns. RDBMS uses Structured Query Language (SQL) and has advantages over early systems by allowing data to be spread across multiple tables and accessed simultaneously by users.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
Several solutions exist for file storage, sharing, and synchronization. Many of them involve a
central server, or a collection of servers, that either store the files, or act as a gateway for them
to be shared. Some systems take a decentralized approach, wherein interconnected users form a
peer-to-peer (P2P) network, and partake in the sharing process: they share the files they
possess with others, and can obtain the files owned by other peers.
In this paper, we survey various technologies, both cloud-based and P2P-based, that users use
to synchronize their files across the network, and discuss their strengths and weaknesses.
Non-shared disk clusters provide a fault tolerant and cost-effective approach to data-intensive computing. This document describes a prototype non-shared disk cluster and plans for a full implementation. The prototype uses local disks on nodes that are not shared over the network, requiring processing to occur on nodes containing the needed data. A file catalog tracks data placement. The full implementation will include modifications to analysis software to dispatch jobs to nodes based on file location. Fault tolerance is provided by restarting jobs if nodes fail and restoring failed nodes.
This document discusses using distributed processing to increase computational power. It introduces the concepts of using a distributed file system like HDFS to store large files across multiple machines. MapReduce is presented as a way to process large amounts of data in parallel by splitting it into chunks, mapping functions to each chunk, and then recombining the results. Hadoop is introduced as an open-source MapReduce framework that uses HDFS for storage and allows processing of data across a cluster of machines. Examples are given of writing Map and Reduce functions and running jobs on Hadoop to demonstrate distributed processing of large datasets.
This document discusses the problem of storing and processing many small files in HDFS and Hadoop. It introduces the concept of "harballing" where Hadoop uses an archiving technique called Hadoop Archive (HAR) to collect many small files into a single large file to reduce overhead on the namenode and improve performance. HAR packs small files into an archive file with a .har extension so the original files can still be accessed efficiently and in parallel. This reduction of small files through harballing increases scalability by reducing namespace usage and load on the namenode.
This document proposes a self-assured deduplication system for authorized deduplication in a hybrid cloud. The system uses convergent encryption to encrypt data before uploading it to the public cloud while allowing for deduplication. A private cloud server manages the private keys and generates file tokens to allow authorized duplicate checking on the public cloud. The proposed system was implemented as a prototype and experiments show it incurs minimal overhead compared to normal operations like encryption and uploading.
The document discusses process migration in Linux. It begins with an abstract and introduction on process migration and its benefits. It then provides details on the characteristics and motivations for process migration, including load balancing, resource sharing, fault tolerance, and mobility. The document discusses homogeneous migration in Linux in detail, including user-level and kernel-level approaches. It also describes the key components involved in process migration in Linux like the central server, load balancer, checkpointer, and file transferrer. Finally, it discusses ELF files and their structure including the ELF header and various fields.
White Paper: Using Perforce 'Attributes' for Managing Game Asset MetadataPerforce
Perforce attributes are used to organize and manipulate game assets. Attributes store metadata like categorization and dependency information. A local SQLite database caches attribute information for faster searching. Integrating assets between branches is challenging since attributes cannot be merged like text files.
The document describes the Google File System (GFS), a scalable distributed file system designed and implemented by Google to meet its rapidly growing data storage needs. Key aspects of GFS include using inexpensive commodity hardware, supporting large files and high throughput appending, and providing fault tolerance through replication across multiple servers. GFS differs from previous distributed file systems in its focus on high volume appending over rewriting, use of large files, and relaxed consistency to improve performance for Google's specific workload characteristics.
The document describes the Google File System (GFS), a scalable distributed file system designed and implemented by Google to meet its rapidly growing data storage needs. Key aspects of the GFS design include supporting large files and high throughput appending workloads on inexpensive commodity hardware in the face of frequent component failures. The GFS architecture uses a single master to manage metadata and multiple chunkservers to store and retrieve file chunks, providing fault tolerance through replication.
This document summarizes a research paper that proposes using a Google File System (GFS) configuration with MapReduce to improve CPU and storage utilization in a cloud computing system. It discusses how GFS with MapReduce can split large files into chunks and distribute the processing of those chunks across idle cloud nodes to make better use of resources. The document also addresses using encryption to improve security of data in the cloud.
The document discusses methods for accelerating Perforce workspace syncs and reducing network storage usage as digital assets grow rapidly. It introduces IC Manage Views, which uses dynamic virtual workspaces and local caching to achieve near-instant workspace syncs and reduce network storage by 4x. Benchmark results show IC Manage Views delivers files 2x faster than traditional methods through intelligent file redirection that separates reads from writes. IC Manage Views is compatible with existing storage technologies and scales as users and data grow.
The document describes iDedup, a system for performing inline data deduplication on primary storage systems while minimizing performance impacts. It leverages two insights about duplicated data in real-world workloads: 1) spatial locality exists where duplicated data occurs in sequences of disk blocks, and 2) temporal locality exists where duplicated data is accessed repeatedly close in time. The system performs selective deduplication of block sequences to reduce fragmentation and seeks during reads. It also replaces expensive on-disk deduplication metadata with a smaller in-memory fingerprint cache to reduce writes. Evaluation shows the system achieves 60-70% deduplication with less than 5% CPU overhead and 2-4% increased latency.
Active directory job_interview_preparation_guideabdulkalamattari
This document provides answers to common interview questions about Active Directory. It explains key Active Directory concepts such as domains, organizational units, group policy, forests, and mixed vs native modes. It also covers requirements for installing Active Directory and how to verify a proper installation.
Dr.Hadoop- an infinite scalable metadata management for Hadoop-How the baby e...Dipayan Dev
This document discusses Dr. Hadoop, a new framework proposed by authors Dipayan Dev and Ripon Patgiri to provide efficient and scalable metadata management for Hadoop. It addresses key issues with Hadoop's current single point of failure for metadata on the NameNode. The new framework is called Dr. Hadoop and uses a technique called Dynamic Circular Metadata Splitting (DCMS) that distributes metadata uniformly across multiple NameNodes for load balancing while also preserving metadata locality through consistent hashing and locality-preserving hashing. Dr. Hadoop aims to provide infinite scalability for metadata as data scales to exabytes without affecting throughput.
iaetsd Controlling data deuplication in cloud storageIaetsd Iaetsd
This document discusses controlling data deduplication in cloud storage. It proposes an architecture that provides duplicate check procedures with minimal overhead compared to normal cloud storage operations. The key aspects of the proposed system are:
1) It uses convergent encryption to encrypt data for privacy while still allowing for deduplication of duplicate files.
2) It introduces a private cloud that manages user privileges and generates tokens for authorized duplicate checking in a hybrid cloud architecture.
3) It evaluates the overhead of the proposed authorized duplicate checking scheme and finds it incurs negligible overhead compared to normal cloud storage operations.
Data Analytics: HDFS with Big Data : Issues and ApplicationDr. Chitra Dhawale
This document provides information about a course on data analytics. It outlines the course outcomes, which include developing scalable systems using Apache and Hadoop, writing MapReduce applications, differentiating SQL and NoSQL, and analyzing and developing big data solutions using Hive and Pig. The document also describes some of the topics that will be covered in the course, including distributed file systems and their issues, an introduction to big data, characteristics of big data, types of big data, and comparisons between traditional and big data approaches.
Methodology for Optimizing Storage on Cloud Using Authorized De-Duplication –...IRJET Journal
This document summarizes a research paper that proposes a methodology for optimizing storage on the cloud using authorized de-duplication. It discusses how de-duplication works to eliminate duplicate data and optimize storage. The key steps are chunking files into blocks, applying secure hash algorithms like SHA-512 to generate unique hashes for each block, and comparing hashes to reference duplicate blocks instead of storing multiple copies. It also discusses using cryptographic techniques like ciphertext-policy attribute-based encryption for authentication and security on public clouds. The proposed approach aims to optimize storage while providing authorized de-duplication functionality.
This document provides an overview of the key differences between storage area networks (SANs) and network attached storage (NAS). It explains that a NAS is a single network storage device that operates on data files, while a SAN connects multiple storage devices that can share data. The document also discusses how NAS and SAN connect and communicate with other devices on the network and how they are sometimes used together in a unified SAN configuration.
This document discusses Relational Database Management Systems (RDBMS). It provides an overview of early database systems like hierarchical and network models. It then describes the key concepts of RDBMS including relations, attributes, and using tables, rows, and columns. RDBMS uses Structured Query Language (SQL) and has advantages over early systems by allowing data to be spread across multiple tables and accessed simultaneously by users.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
Several solutions exist for file storage, sharing, and synchronization. Many of them involve a
central server, or a collection of servers, that either store the files, or act as a gateway for them
to be shared. Some systems take a decentralized approach, wherein interconnected users form a
peer-to-peer (P2P) network, and partake in the sharing process: they share the files they
possess with others, and can obtain the files owned by other peers.
In this paper, we survey various technologies, both cloud-based and P2P-based, that users use
to synchronize their files across the network, and discuss their strengths and weaknesses.
Non-shared disk clusters provide a fault tolerant and cost-effective approach to data-intensive computing. This document describes a prototype non-shared disk cluster and plans for a full implementation. The prototype uses local disks on nodes that are not shared over the network, requiring processing to occur on nodes containing the needed data. A file catalog tracks data placement. The full implementation will include modifications to analysis software to dispatch jobs to nodes based on file location. Fault tolerance is provided by restarting jobs if nodes fail and restoring failed nodes.
This document discusses using distributed processing to increase computational power. It introduces the concepts of using a distributed file system like HDFS to store large files across multiple machines. MapReduce is presented as a way to process large amounts of data in parallel by splitting it into chunks, mapping functions to each chunk, and then recombining the results. Hadoop is introduced as an open-source MapReduce framework that uses HDFS for storage and allows processing of data across a cluster of machines. Examples are given of writing Map and Reduce functions and running jobs on Hadoop to demonstrate distributed processing of large datasets.
This document discusses the problem of storing and processing many small files in HDFS and Hadoop. It introduces the concept of "harballing" where Hadoop uses an archiving technique called Hadoop Archive (HAR) to collect many small files into a single large file to reduce overhead on the namenode and improve performance. HAR packs small files into an archive file with a .har extension so the original files can still be accessed efficiently and in parallel. This reduction of small files through harballing increases scalability by reducing namespace usage and load on the namenode.
This document proposes a self-assured deduplication system for authorized deduplication in a hybrid cloud. The system uses convergent encryption to encrypt data before uploading it to the public cloud while allowing for deduplication. A private cloud server manages the private keys and generates file tokens to allow authorized duplicate checking on the public cloud. The proposed system was implemented as a prototype and experiments show it incurs minimal overhead compared to normal operations like encryption and uploading.
The document discusses process migration in Linux. It begins with an abstract and introduction on process migration and its benefits. It then provides details on the characteristics and motivations for process migration, including load balancing, resource sharing, fault tolerance, and mobility. The document discusses homogeneous migration in Linux in detail, including user-level and kernel-level approaches. It also describes the key components involved in process migration in Linux like the central server, load balancer, checkpointer, and file transferrer. Finally, it discusses ELF files and their structure including the ELF header and various fields.
White Paper: Using Perforce 'Attributes' for Managing Game Asset MetadataPerforce
Perforce attributes are used to organize and manipulate game assets. Attributes store metadata like categorization and dependency information. A local SQLite database caches attribute information for faster searching. Integrating assets between branches is challenging since attributes cannot be merged like text files.
The document describes the Google File System (GFS), a scalable distributed file system designed and implemented by Google to meet its rapidly growing data storage needs. Key aspects of GFS include using inexpensive commodity hardware, supporting large files and high throughput appending, and providing fault tolerance through replication across multiple servers. GFS differs from previous distributed file systems in its focus on high volume appending over rewriting, use of large files, and relaxed consistency to improve performance for Google's specific workload characteristics.
The document describes the Google File System (GFS), a scalable distributed file system designed and implemented by Google to meet its rapidly growing data storage needs. Key aspects of the GFS design include supporting large files and high throughput appending workloads on inexpensive commodity hardware in the face of frequent component failures. The GFS architecture uses a single master to manage metadata and multiple chunkservers to store and retrieve file chunks, providing fault tolerance through replication.
This document summarizes a research paper that proposes using a Google File System (GFS) configuration with MapReduce to improve CPU and storage utilization in a cloud computing system. It discusses how GFS with MapReduce can split large files into chunks and distribute the processing of those chunks across idle cloud nodes to make better use of resources. The document also addresses using encryption to improve security of data in the cloud.
Comparative Analysis, Security Aspects & Optimization of Workload in Gfs Base...IOSR Journals
This document summarizes a research paper that proposes using Google File System (GFS) and MapReduce in a cloud computing environment to improve resource utilization and processing of large datasets. The paper discusses GFS architecture with a master node and chunk servers, and how MapReduce can split large files into chunks and process them in parallel across idle cloud nodes. It also proposes encrypting data for security and using a third party to audit client files. The goal is to provide fault tolerance, optimize workload processing time, and maximize utilization of cloud resources for data-intensive applications.
The Google File System is a scalable distributed file system designed to meet the rapidly growing data storage needs of Google. It provides fault tolerance on inexpensive commodity hardware and high aggregate performance to large numbers of clients. The key design drivers were the assumptions that components often fail, files are huge, writes are append-only, and concurrent appending is important. The system has a single master that manages metadata and assigns chunks to chunkservers, which store replicated file chunks. Clients communicate directly with chunkservers to read and write large, sequentially accessed files in chunks of 64MB.
The Google File System is a scalable distributed file system designed to meet the rapidly growing data storage needs of Google. It provides fault tolerance on inexpensive commodity hardware and high aggregate performance to large numbers of clients. The key design drivers were the assumptions that components often fail, files are huge, writes are append-only, and concurrent appending is important. The system has a single master that manages metadata and assigns chunks to chunkservers, which store replicated file chunks. Clients communicate directly with chunkservers to read and write large, sequentially accessed files in chunks of 64MB.
The document proposes a cloud environment for backup and data storage using remote servers that can be accessed through the Internet. It involves using the disks of cluster nodes as a global storage system with PVFS2 parallel file system for improved performance. The proposed system aims to increase data availability and reduce information loss by storing data on a private cloud using PVFS2 and developing a multiplatform client application for fast data transfer. It allows reuse of existing infrastructure to reduce costs and gives users experience of managing a private cloud.
Google has designed and implemented a scalable distributed file system for their large distributed data intensive applications. They named it Google File System, GFS.
The Google File System is a scalable distributed file system designed to meet the rapidly growing data storage needs of Google. It provides fault tolerance on inexpensive commodity hardware and high aggregate performance to large numbers of clients. Key aspects of its design include handling frequent component failures as the norm, managing huge files up to multiple gigabytes in size containing many objects, optimizing for file appending and sequential reads of appended data, and co-designing the file system interface to increase flexibility for applications. The largest deployment to date includes over 1,000 storage nodes providing hundreds of terabytes of storage.
The document provides an overview of cloud computing, including Google's approach. Google builds large cloud systems from many commodity computers, disks and networks to provide services. Their Google File System (GFS) stores very large files across machines for fault tolerance. Their MapReduce framework processes vast amounts of data across the cloud in a parallel and fault-tolerant way by breaking jobs into independent map and reduce tasks. An example shows how MapReduce can be used to build an inverted index of words in documents. Google achieves high performance and scalability through their optimized cloud infrastructure and programming model.
This document discusses changes to Hyper-V virtualization from Windows Server 2008 to 2012. Key changes include the ability to share virtual hard disks between VMs, improved quality of service controls, and more robust resource sharing between host and guest systems. The new features make Hyper-V more reliable and scalable for server virtualization needs over the next 2-3 years.
Approved TPA along with Integrity Verification in CloudEditor IJCATR
Cloud computing is new model that helps cloud user to
access resources in pay-as-you-go fashion. This helped the firm to
reduce high capital investments in their own IT organization. Data
security is one of the major issues in cloud computing environment.
The cloud user stores their data on cloud storage will have no longer
direct control over their data. The existing systems already supported
the data integrity check without possessions of actual data file. The
Data Auditing is the method of verification of the user data which is
stored on cloud and is done by the TTP called as TPA. There are
many drawbacks of existing techniques. First, in spite some of the
recent works which supports updates on fixed-sized data blocks
which are called coarse-grained updates, do not support for variablesized
block operations. Second, an essential authorization is missing
between CSP, the cloud user and the TPA. The newly proposed
scheme will support for Fine-grained data updates on dynamic data
using RMHT algorithm and also supports for authorization of TPA.
BFC: High-Performance Distributed Big-File Cloud Storage Based On Key-Value S...dbpublications
This document summarizes a research paper about Big File Cloud (BFC), a high-performance distributed big-file cloud storage system based on a key-value store. BFC addresses challenges in designing an efficient storage engine for cloud systems requiring support for big files, lightweight metadata, low latency, parallel I/O, deduplication, distribution, and scalability. It proposes a lightweight metadata design with fixed-size metadata regardless of file size. It also details BFC's architecture, logical data layout using file chunks, metadata and data storage, distribution and replication, and uploading/deduplication algorithms. The results can be used to build scalable distributed data cloud storage supporting files up to terabytes in size.
This document proposes a seed block algorithm and remote data backup server to help users recover files if the cloud is destroyed or files are deleted. The proposed system stores a backup of user's cloud data on a remote server. It uses a seed block algorithm that breaks files into blocks, takes their XOR, and stores the output to allow data to be recovered. The system was tested on different file types and sizes, showing it could recover same-sized files and required less time than existing solutions. Its applications include secure storage and access to information even without network connectivity.
This document summarizes a research paper on developing a cloud-based storage system similar to Dropbox called "Unbox". It describes the proposed system's architecture which includes users uploading files via a web browser that are stored on servers using the OwnCloud storage system. The storage is implemented using a Raspberry Pi server with Samba file sharing enabled to allow access from multiple devices via CIFS/SMB protocol. The system aims to provide a cost-efficient cloud storage option for users with features like file encryption, backup plans and access from multiple devices.
(Speaker Notes Version) Architecting An Enterprise Storage Platform Using Obj...Niraj Tolia
This document provides an overview of MagFS, a file system designed for cloud storage. Some key points:
- MagFS uses object storage for data storage and metadata servers to manage file metadata and encryption keys. It pushes intelligence to client edges through smart client agents.
- The architecture separates data and metadata planes, with clients directly accessing object storage for data while coordinating with metadata servers. This allows it to scale without bottlenecking on the data path.
- The design goals were to deliver low-cost, high-scale storage that spans devices and networks while supporting rapid iteration through a software-defined approach using VMs and clean separation of data and control planes.
ANALYSIS OF ATTACK TECHNIQUES ON CLOUD BASED DATA DEDUPLICATION TECHNIQUESijccsa
Data in the cloud is increasing rapidly. This huge amount of data is stored in various data centers around
the world. Data deduplication allows lossless compression by removing the duplicate data. So, these data
centers are able to utilize the storage efficiently by removing the redundant data. Attacks in the cloud
computing infrastructure are not new, but attacks based on the deduplication feature in the cloud
computing is relatively new and has made its urge nowadays. Attacks on deduplication features in the cloud
environment can happen in several ways and can give away sensitive information. Though, deduplication
feature facilitates efficient storage usage and bandwidth utilization, there are some drawbacks of this
feature. In this paper, data deduplication features are closely examined. The behavior of data deduplication
depending on its various parameters are explained and analyzed in this paper
ANALYSIS OF ATTACK TECHNIQUES ON CLOUD BASED DATA DEDUPLICATION TECHNIQUESijccsa
Data in the cloud is increasing rapidly. This huge amount of data is stored in various data centers around the world. Data deduplication allows lossless compression by removing the duplicate data. So, these data centers are able to utilize the storage efficiently by removing the redundant data. Attacks in the cloud computing infrastructure are not new, but attacks based on the deduplication feature in the cloud computing is relatively new and has made its urge nowadays. Attacks on deduplication features in the cloud environment can happen in several ways and can give away sensitive information. Though, deduplication feature facilitates efficient storage usage and bandwidth utilization, there are some drawbacks of this feature. In this paper, data deduplication features are closely examined. The behavior of data deduplication depending on its various parameters are explained and analyzed in this paper.
ANALYSIS OF ATTACK TECHNIQUES ON CLOUD BASED DATA DEDUPLICATION TECHNIQUESijccsa
Data in the cloud is increasing rapidly. This huge amount of data is stored in various data centers around
the world. Data deduplication allows lossless compression by removing the duplicate data. So, these data
centers are able to utilize the storage efficiently by removing the redundant data. Attacks in the cloud
computing infrastructure are not new, but attacks based on the deduplication feature in the cloud
computing is relatively new and has made its urge nowadays. Attacks on deduplication features in the cloud
environment can happen in several ways and can give away sensitive information. Though, deduplication
feature facilitates efficient storage usage and bandwidth utilization, there are some drawbacks of this
feature. In this paper, data deduplication features are closely examined. The behavior of data deduplication
depending on its various parameters are explained and analyzed in this paper.
Understanding the Impact and Challenges of Corona Crisis on Education Sector...vivatechijri
n the second week of March 2020, governments of all states in a country suddenly declared
shutting down of all colleges and schools for a temporary period of time as an immediate measure to stop the
spread of pandemic that is of novel corona virus. As the days pass by almost close to a month with no certainty
when they will again reopen. Due to pandemic like this an alarm bells have started sounding in the field of
education where a huge impact can be seen on teaching and learning process as well as on the entire education
sector in turn. The pandemic disruption like this is actually gave time to educators of today to really think about
the sector. Through the present research article, the author is highlighting on the possible impact of
coronavirus on education sector with the future challenges for education sector with possible suggestions.
LEADERSHIP ONLY CAN LEAD THE ORGANIZATION TOWARDS IMPROVEMENT AND DEVELOPMENT vivatechijri
This document discusses the importance of leadership in leading an organization towards improvement and development. It states that leadership is responsible for providing a clear vision and strategy to successfully achieve that vision. Effective leadership can impact the success of an organization by controlling its direction and motivating employees. Leadership is different from traditional management in that it guides employees towards organizational goals through open communication and motivation, rather than simply directing work. The paper concludes that only leadership can lead an organization to change according to its evolving environment, while management may simply follow old rules. Leadership is key to adapting to new market needs and trends.
The topic of assignment is a critical problem in mathematics and is further explored in the real
physical world. We try to implement a replacement method during this paper to solve assignment problems with
algorithm and solution steps. By using new method and computing by existing two methods, we analyse a
numerical example, also we compare the optimal solutions between this new method and two current methods. A
standardized technique, simple to use to solve assignment problems, may be the proposed method
Structural and Morphological Studies of Nano Composite Polymer Gel Electroly...vivatechijri
The document summarizes research on a nano composite polymer gel electrolyte containing SiO2 nanoparticles. Key points:
1. Polyvinylidene fluoride-co-hexafluoropropylene polymer was used as the base polymer mixed with propylene carbonate, magnesium perchlorate, and SiO2 nanoparticles to synthesize the nano composite polymer gel electrolyte.
2. The electrolyte was characterized using XRD, SEM, and FTIR which confirmed the homogeneous dispersion of SiO2 nanoparticles and increased amorphous nature of the electrolyte, enhancing its ion conductivity.
3. XRD showed decreased crystallinity and disappearance of polymer peaks upon addition of SiO2. SEM revealed
Theoretical study of two dimensional Nano sheet for gas sensing applicationvivatechijri
This study is focus on various two dimensional material for sensing various gases with theoretical
view for new research in gas sensing application. In this paper we review various two dimensional sheet such as
Graphene, Boron Nitride nanosheet, Mxene and their application in sensing various gases present in the
atmosphere.
METHODS FOR DETECTION OF COMMON ADULTERANTS IN FOODvivatechijri
Food is essential forliving. Food adulteration deceives consumers and can endanger their health. The
purpose of this document is to list common food adulterant methods commonly found in India. An adulterant is
a substance found in other substances such as food, cosmetics, pharmaceuticals, fuels, or other chemicals that
compromise the safety or effectiveness of that substance. The addition of adulterants is called adulteration. The
most common reason for adulteration is the use of undeclared materials by manufacturers that are cheaper than
the correct and declared ones. The adulterants can be harmful or reduce the effectiveness of the product, or
they can be harmless.
The novel ideas of being a entrepreneur is a key for everyone to get in the hustle, but developing a
idea from core requires a systematic plan, time management, time investment and most importantly client
attention. The Time required for developing may vary from idea to idea and strength of the team. Leadership to
build a team and manage the same throughout the peak of development is the main quality. Innovations and
Techniques to qualify the huddles is another aspect of Business Development and client Retention.
Innovation for supporting prosperity has for quite some time been a focus on numerous orders, including PC science, brain research, and human-PC connection. In any case, the meaning of prosperity isn't continuously clear and this has suggestions for how we plan for and evaluate advances that intend to cultivate it. Here, we talk about current meanings of prosperity and how it relates with and now and then is a result of self-amazing quality. We at that point center around how innovations can uphold prosperity through encounters of self-amazing quality, finishing with conceivable future bearings.
An Alternative to Hard Drives in the Coming Future:DNA-BASED DATA STORAGEvivatechijri
Demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up, there emerges a requirement for a storage medium with high capacity, high storage density, and possibility to face up to extreme environmental conditions. According to a research in 2018, every minute Google conducted 3.88 million searches, other people posted 49,000 photos on Instagram, sent 159,362,760 e-mails, tweeted 473,000 times and watched 4.33 million videos on YouTube. In 2020 it estimated a creation of 1.7 megabytes of knowledge per second per person globally, which translates to about 418 zettabytes during a single year. The magnetic or optical data-storage systems that currently hold this volume of 0s and 1s typically cannot last for quite a century. Running data centres takes vast amounts of energy. In short, we are close to have a substantial data-storage problem which will only become more severe over time. Deoxyribonucleic acid (DNA) are often potentially used for these purposes because it isn't much different from the traditional method utilized in a computer. DNA’s information density is notable, 215 petabytes or 215 million gigabytes of data can be stored in just one gram of DNA. First we can encode all data at a molecular level and then store it in a medium that will last for a while and not become out-dated just like floppy disks. Due to the improved techniques for reading and writing DNA, a rapid increase is observed in the amount of possible data storage in DNA.
The usage of chatbots has increased tremendously since past few years. A conversational interface is an interface that the user can interact with by means of a conversation. The conversation can occur by speech but also by text input. When a chatty interface uses text, it is also described as a chatbot or a conversational medium. During this study, the user experience factors of these so called chatbots were investigated. The prime objective is “to spot the state of the art in chatbot usability and applied human-computer interaction methodologies, to research the way to assess chatbots usability". Two sorts of chatbots are formulated, one with and one without personalisation factors. the planning of this research may be a two-by-two factorial design. The independent variables are the two chatbots (unpersonalised versus personalised) and thus the speci?c task or goal the user are ready to do with the chatbot within the ?nancial ?eld (a simple versus a posh task). The results are that there was no noteworthy interaction effect between personalisation and task on the user experience of chatbots. A signi?cant di?erence was found between the two tasks with regard to the user experience of chatbots, however this variation wasn't because of personalisation.
The Smart glasses Technology of wearable computing aims to identify the computing devices into today’s world.(SGT) are wearable Computer glasses that is used to add the information alongside or what the wearer sees. They are also able to change their optical properties at runtime.(SGT) is used to be one of the modern computing devices that amalgamate the humans and machines with the help of information and communication technology. Smart glasses is mainly made up of an optical head-mounted display or embedded wireless glasses with transparent heads- up display or augmented reality (AR) overlay in it. In recent years, it is been used in the medical and gaming applications, and also in the education sector. This report basically focuses on smart glasses, one of the categories of wearable computing which is very popular presently in the media and expected to be a big market in the next coming years. It Evaluate the differences from smart glasses to other smart devices. It introduces many possible different applications from the different companies for the different types of audience and gives an overview of the different smart glasses which are available presently and will be available after the next few years.
Future Applications of Smart Iot Devicesvivatechijri
With the Internet of Things (IoT) bit by bit creating as the resulting time of the headway of the Internet, it gets critical to see the diverse expected zones for the utilization of IoT and the research challenges that are connected with these applications going from splendid savvy urban areas, to medical care administrations, shrewd farming, collaborations and retail. IoT is needed to attack into for all expectations and purposes for all pieces of our day-to-day life. Despite the fact that the current IoT enabling advancements have immensely improved in the continuous years, there are so far different issues that require attention. Since the IoT ideas results from heterogeneous advancements, many examination difficulties will arise. In like manner, IoT is planning for new components of exploration to be finished. This paper presents the progressing headway of IoT advancements and inspects future applications.
Cross Platform Development Using Fluttervivatechijri
Today the development of cross-platform mobile application has under the state of compromise. The developers are not willing to choose an alternative of either building the similar app many times for many operating systems or to accept a lowest common denominator and optimal solution that will going to trade the native speed, accuracy for portability. The Flutter is an open-source SDK for creating high-performance, high fidelity mobile apps for the development of iOS and Android. Few significant features of flutter are - Just-in-time compilation (JIT), Ahead- of-time compilation (AOT compilation) into a native (system-dependent) machine code so that the resulting binary file can execute natively. The Flutter’s hot reload functionality helps us to understand quickly and easily experiment, build UIs, add features, and fix bugs. Hot reload works by injecting updated source code files into the running Dart Virtual Machine (VM). With the help of Flutter, we believe that we would be having a solution that gives us the best of both worlds: hardware accelerated graphics and UI, powered by native ARM code, targeting both popular mobile operating systems.
The Internet, today, has become an important part of our lives. The World Wide Web that was once a small and inaccessible data storage service is now large and valuable. Current activities partially or completely integrated into the physical world can be made to a higher standard. All activities related to our daily life are mapped and linked to another business in the digital world. The world has seen great strides in the Internet and in 3D stereoscopic displays. The time has come to unite the two to bring a new level of experience to the users. 3D Internet is a concept that is yet to be used and requires browsers to be equipped with in-depth visualization and artificial intelligence. When this material is included, the Internet concept of material may become a reality discussed in this paper. In this paper we have discussed the features, possible setting methods, applications, and advantages and disadvantages of using the Internet. With this paper we aim to provide a clear view of 3D Internet and the potential benefits associated with this obviously cost the amount of investment needed to be used.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
The study LiFi (Light Fidelity) demonstrates about how can we use this technology as a medium of communication similar to Wifi . This is the latest technology proposed by Harold Haas in 2011. It explains about the process of transmitting data with the help of illumination of an Led bulb and about its speed intensity to transmit data. Basically in this paper, author will discuss about the technology and also explain that how we can replace from WiFi to LiFi . WiFi generally used for wireless coverage within the buildings while LiFi is capable for high intensity wireless data coverage in limited areas with no obstacles .This research paper represents introduction of the Lifi technology,performance,modulation and challenges. This research paper can be used as a reference and knowledge to develop some of LiFitechnology.
Social media platform and Our right to privacyvivatechijri
The advancement of Information Technology has hastened the ability to disseminate information across the globe. In particular, the recent trends in ‘Social Networking’ have led to a spark in personally sensitive information being published on the World Wide Web. While such socially active websites are creative tools for expressing one’s personality it also entails serious privacy concerns. Thus, Social Networking websites could be termed a double edged sword. It is important for the law to keep abreast of these developments in technology. The purpose of this paper is to demonstrate the limits of extending existing laws to battle privacy intrusions in the Internet especially in the context of social networking. It is suggested that privacy specific legislation is the most appropriate means of protecting online privacy. In doing so it is important to maintain a balance between the competing right of expression, the failure of which may hinder the reaping of benefits offered by Internet technology
THE USABILITY METRICS FOR USER EXPERIENCEvivatechijri
THE USABILITY METRICS FOR USER EXPERIENCE was innovatively created by Google engineers and it is ready for production in record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying commodity hardware. As Google run number of application then Google’s goal became to build a vast storage network out of inexpensive commodity hardware. So Google create its own file system, named as THE USABILITY METRICS FOR USER EXPERIENCE that is GFS. THE USABILITY METRICS FOR USER EXPERIENCE is one of the largest file system in operation. Generally THE USABILITY METRICS FOR USER EXPERIENCE is a scalable distributed file system of large distributed data intensive apps. In the design phase of THE USABILITY METRICS FOR USER EXPERIENCE, in which the given stress includes component failures , files are huge and files are mutated by appending data. The entire file system is organized hierarchically in directories and identified by pathnames. The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into chunks, and that is the key design parameter. THE USABILITY METRICS FOR USER EXPERIENCE also uses leases and mutation order in their design to achieve atomicity and consistency. As of there fault tolerance, THE USABILITY METRICS FOR USER EXPERIENCE is highly available, replicas of chunk servers and master exists.
A Study of Tokenization of Real Estate Using Blockchain Technologyvivatechijri
Real estate is by far one of the most trusted investments that people have preferred, being a lucrative investment it provides a steady source of income in the form of lease and rents. Although there are numerous advantages, one of the key downsides of real estate investments is lack of liquidity. Thus, even though global real estate investments amount to about twice the size of investments in stock markets, the number of investors in the real estate market is significantly lower. Block chain technology has real potential in addressing the issues of liquidity and transparency, opening the market to even retail investors. Owing to the functionality and flexibility of creating Security Tokens, which are backed by real-world assets, real estate can be made liquid with the help of Special Purpose Vehicles. Tokens of ERC 777 standard, which represent fractional ownership of the real estate can be purchased by an investor and these tokens can also be listed on secondary exchanges. The robustness of Smart Contracts can enable the efficient transfer of tokens and seamless distribution of earnings amongst the investors. This work describes Ethereum blockchainbased solutions to make the existing Real Estate investment system much more efficient.
A Study of Data Storage Security Issues in Cloud Computingvivatechijri
Cloudcomputingprovidesondemandservicestoitsclients.Datastorageisamongoneoftheprimaryservices providedbycloudcomputing.Cloudserviceproviderhoststhedataofdataownerontheirserverandusercan accesstheirdatafromtheseservers.Asdata,ownersandserversaredifferentidentities,theparadigmofdata storagebringsupmanysecuritychallenges.Anindependentmechanismisrequiredtomakesurethatdatais correctlyhostedintothecloudstorageserver.Inthispaper,wewilldiscussthedifferenttechniquesthatare usedforsecuredatastorageoncloud. Cloud computing is a functional paradigm that is evolving and making IT utilization easier by the day for consumers. Cloud computing offers standardized applications to users online and in a manner that can be accessed regularly. Such applications can be accessed by as many persons as permitted within an organization without bothering about the maintenance of such application. The Cloud also provides a channel to design and deploy user applications including its storage space and database without bothering about the underlying operating system. The application can run without consideration for on premise infrastructure. Also, the Cloud makes massive storage available both for data and databases. Storage of data on the Cloud is one of the core activities in Cloud computing. Storage utilizes infrastructure spread across several geographical locations.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
Google File System
1. VIVA-Tech International Journal for Research and Innovation Volume 1, Issue 4 (2021)
ISSN(Online): 2581-7280
VIVA Institute of Technology
9th
National Conference onRole of Engineers in Nation Building – 2021 (NCRENB-2021)
1
Google File System
Pramod A. Yadav1
, Prof. Krutika Vartak2
1
(Department of MCA, Viva School Of MCA/ University of Mumbai, India)
2
(Department of MCA, Viva School Of MCA/ University of Mumbai, India)
Abstract : Google File System was innovatively created by Google engineers and it is ready for production in
record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying
commodity hardware. As Google run number of application then Google’s goal became to build a vast storage
network out of inexpensive commodity hardware. So Google create its own file system, named as Google File
System that is GFS. Google File system is one of the largest file system in operation. Generally Google File
System is a scalable distributed file system of large distributed data intensive apps. In the design phase of
Google file system, in which the given stress includes component failures , files are huge and files are mutated
by appending data. The entire file system is organized hierarchically in directories and identified by pathnames.
The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into
chunks, and that is the key design parameter. Google File System also uses leases and mutation order in their
design to achieve atomicity and consistency. As of there fault tolerance, Google file system is highly available,
replicas of chunk servers and master exists.
Keywords - Design, Scalability, Fault tolerance, Performance, Measurements.
I. INTRODUCTION
Google File Sys-tem (GFS) to meet the ever-increasing needs of Google data processing needs. GFS
shares many similar goals with previously distributed file systems such as functionality, feasibility, reliability,
and availability. However, its design is driven by the critical recognition of our application workloads and
technology, current and hosted, reflecting the significant departure from other design of file system design. We
also reviewed the major sector options and explored very different points in the design space.
First, partial failure is more common than different. The file system contains hundreds or thousands of
storage devices built from the most inexpensive components of the com-modity type and is available with an
equal number of customer's equipment. The quantity and quality of computers is likely to ensure that some do
not work at any time and some will not recover from their rent failure. We've identified problems caused by app
bugs, operating system bugs, human errors, and disks, memory, connectors, connections, and power of sup-
plies. Therefore, regular monitoring, error detection, error tolerance, and automatic recovery should be in line
with the system.
Second, the files are large by traditional standards. Multi-GB files are standard. Each file typically
contains multiple application elements such as web documents. When we are constantly working on fast-
growing data sets many TBs contain billions of items, it is impossible to manage billions of files equivalent to
KB almost even if the file system can support them. For this reason, design considerations and parameters such
as I / O performance and block size should be reviewed.
2. VIVA-Tech International Journal for Research and Innovation Volume 1, Issue 4 (2021)
ISSN(Online): 2581-7280
VIVA Institute of Technology
9th
National Conference onRole of Engineers in Nation Building – 2021 (NCRENB-2021)
2
Third, many files are converted by inserting new data instead of overwriting existing data. Random
write inside a file is almost non-existent. Once written, files are read only, and usually only in sequence. Various
details share these features. Some may create large repositories where data analysis systems test. Some may be
data streams that continue continuously through ap-plications. Some may be historical data. Some may be
temporary effects produced on one machine and processed in another, either simultaneously or later. Given this
pattern of access to large files, the application becomes the cause of performance and atomic verification, while
data storage for customers loses appeal.
Many GFS collections are currently used for a variety of purposes. The largest have more than 1000
storage areas, more than 300 TB of disk storage, and are widely available by hundreds of clients on
continuously different machines.
Figure1 : Google File System
II. SYSTEM INTERACTIONS
2.1.Leases and Mutation Order
Conversion is a modification that changes chunk content or metadata such as writing or append-opion.
Each modification is performed on all chunk pieces. We use the rental to maintain a consistent variable density
in all replicas. The king offered a chunk rent to one of the repli-cas, which we call the principal. The primary is
selected in serial sequence with all feature changes in the chunk. All drawings follow this order when inserting
genetic modification. Therefore, the land reform order is defined first by the king’s rental grant order, and
within the lease serial numbers provided by the primary.
2.2.Data Flow
We cut the flow of data in the flow of control so we can use the network efficiently. While control
flows from client to primary and then to all managers, data is pushed to the line in a carefully collected line of
chunkservers in the form of pipelines. Our goals are to take full advantage of the network bandwidth of each
machine, to avoid network barriers and high-latency links, and to reduce push-up delays in all data.
To take full advantage of the network bandwidth of each machine, data is pushed sequentially to the
chunkservers line rather than distributed to another type of topology (e.g., Tree). Therefore, the full output
bandwidth of each machine is used to transmit data much faster than is divided between multiple receivers.
To avoid network barriers and high-latency connections (e.g., inter-switch connectors are common to
both) as much as possible, each machine transmits data to a machine that is "too close" to a network that has not
yet received it. Our network topology is simple enough that “distances” can be accurately measured from IP
addresses.
2.3.Atomic Record Appends
GFS provides atomic insertion known as record append In traditional writing, the client specifies the
set in which the data will be recorded. Simultaneous writing in the same region is immeasurable: a region may
end up containing pieces of data from multiple clients. In the appendend of the record, however, the client only
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specifies the data.
Record recording is widely used by our distributed applications where multiple clients of different
machines stick to the same file at the same time. Clients will need complex and expensive synchronization of
synchronization, of getting more power using a locked manager, if they do so with traditional writing. In our
workloads, such files are oftenit works as a multi-producer line / individual customer line or integrated results
from various customers.
Record append is a form of transformation and follows the flow of con-trol with just a little more sense
in the beginning. The client pushes the data into all the final file fragments. After that, it sends its request to pri-
mary. The main test is to see if inserting a record in the current fragment could cause the chunk to exceed the
maximum size (64 MB). If so, add a chunk to the max-imum size, tell the seconds to do the same, and then
respond to the client indicating that the task should be repeated in the next chunk exactly where he found it, and
finally respond to the client's success.
2.4.Snapshot
The snapshot function makes a copy of the file or direc-tory tree ("source") almost immediately, while
mimicking any disruption of ongoing changes. Our users use it to quickly create branch copies of large data sets.
We use standard copying techniques in writing using abbreviations. When the master receives a
summary request, it first removes any prominent leases in chunks from the files that are about to be
downloaded. This ensures that any subsequent writing of these cases will require contacting the landlord to find
a lease owner. This will give the king a chance to make a new copy of the chunk first.
After the lease is completed or expired, the master installs the disk functionality. It then uses this log to
log in to its memory state by repeating the metadata of the source file or directory tree. Recent shooting files
identify the same nodes as source files.
III. MASTER OPERATION
3.1.Namespace Management and Locking
We now show you how this lock method can prevent file / home / user / foo from being created while /
home / user is being downloaded to / save / user. The snapshot oper-ation finds read lock in / home and / save,
and locks in / home / user and / save / user. The structure of the ac-quires file is learned to lock in / home and /
home / user, and to lock in / home / user / foo. The two activities will be designed to work well together as they
try to find conflicting locks on the / home / user. File formatting does not require a text lock in the parent
directory because there is no "directory", or inode-like, data structure that should be protected from conversion.
The key read in the name is able to protect the parent's guide from removal.
3.2.Replica placement
The GFS collection is still widely distributed at more than one level. It usually has hundreds of
chunkservers scattered throughout the machine racks. These chunkservers can also be accessed by hundreds of
clients from the same or different racks. Connections between two machines in different markets may require
one or more network switching. In addition to the ally, the bandwidth entering or exiting the rack may be less
than the combined bandwidth of all equipment within the rack. Multi-level distribution presents a unique
challenge in distributing data on robustness, reliability, and availability.
3.3.Creation, Re-replication, Rebalancing
Chunk duplication is created for three reasons: chunk duplication, duplication, and redesign.
When the king creates a lump, he chooses where you can put the empty answers first. It looks at several
fac-tors. (1) We want to place new duplicates on chunkservers with less than disk usage. Over time this will
measure disk usage across all chunkservers. (2) We want to limit the number of "recent" items in each
chunkserver. Although creation itself is cheap, it accurately predicts future heavy writing because episodes are
made when they are sent in writing, and in append-once-read-most of our work is always read once they have
read it in full. (3) As mentioned above, we want a partial distribution across all racks.
3.4.Garbage Collection
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After a file is deleted, GFS does not immediately reclaim the available physical storage. It does so only
lazily during regular garbage collection at both the file and chunk levels. We find that this approach makes the
system much simpler and more reliable.
3.5.Mechanism
When a file is deleted by the app, the master logs are removed as quickly as other changes. However,
instead of retrieving resources quickly, the file is renamed a hidden name that includes a delete stamp. During
the master scan of the space file name, it removes any hidden files if they have been lost for more than three
days (time is corrected). Until then, the file can still be read under a new, special name and can be deleted by
renaming it back to normal. When a hidden file is deleted from the namespace, its memory metadata is erased.
This removes its links from all its links.
IV. FAULT TOLERANCE AND DIAGNOSIS
4.1High Availability
Among hundreds of servers in a GFS cluster, some are bound to be unavailable at any given time. We
keep the overall system highly available with two simple yet effective strategies: fast recovery and replication
4.2.Fast Recovery
Both king and chunkserver are designed to retrieve their status and start in seconds no matter how they
cut. In fact, we do not distinguish between normal and abnormal cuts; servers are constantly shut down by the
process kill. Clients and other servers experience a slight hiccup as they expire on their outstanding applications,
reconnect with the restart server, and try again. Sec-tion 6.2.2 reports detected startup times.
4.3.Chunk Replication
Users can specify different levels of duplication of different parts of file name space. Default three
times. Master clones replicas are available as required to keep each cluster fully converted as chunkservers
move. While repetition has worked well for us, we are exploring other forms of cross-server redun-dancy such
as parity codes or erasure of our growing read-only retention needs. We expect it to be a challenge but it is
manageable to use these sophisticated programs to redefine the performance of our freely integrated program
because our campaign is dominated by appenders and learns rather than writing a little random.
4.4.Master Replication
The main situation is honestly repeated. Work log and test locations are duplicated on multiple
machines. A regime change is considered a commitment only after their entry record is placed on a local disk
and in all major responses. For convenience, one major process always handles all modifications and
background functions such as garbage collection that transforms the system internally. If it fails, it can restart
almost immediately. If its machine or disk fails, monitoring infrastructure outside the GFS initiates a new
process in another location with a duplicate operation. Clients use only the canonical name of the master (e.g.
gfs-test), which is the alias of the DNS that can be changed when the master is transferred to another machine.
4.5.Diagnostic Tools
The detailed and detailed login helped the im-equate with problem classification, error correction, and
perfor-mance analysis, while only low cost is available. Outgoing logs, it is difficult to comprehend the passing,
non-recurring connections between machines. GFS servers produce di-agnostic logs that record many important
events (such as up and down chunkservers) and all RPC requests and responses. These diagnostic logs can be
removed freely without affecting the accuracy of the system. However, we try to keep these logs close to space
permits.
V. MEASUREMENTS
5.1.Micro-benchmarks
We rated performance on a GFS collection consisting of one master, two master replicas, 16
chunkservers, and 16 clients. Note that this setting is set to facilitate testing. Standardcollections have hundreds
of chunkservers and hundreds of clients.
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All devices are equipped with two 1.4 GHz PIII processors, 2 GB of memory, two discs of 80 GB 5400
rpm, and a 100 Mbps full-duplex Ethernet connection to the HP 2524 switch. All 19 GFS server equipment is
connected. in one switch, and all 16 customer equipment to another. The two switches are connected via a 1
Gbps link.
5.2.Reads
Clients read simultaneously from the file system. Each client reads a randomly selected 4 MB region
from a 320 GB file set. This is 256 times more so for each client to end up reading 1 GB of data. Chunkservers
bundled together have only 32 GB of memory, so we expect at least 10% of Linux cache memory. Our results
should be close to the cold storage results.
The combined reading rate of N clients and its theoretical limit. The limit is up to 125 MB / s when 1
Gbps link between these switches is full, or 12.5 MB / s per client when its 100 Mbps network interface is full,
whichever works. The rated reading rate is 10 MB / s, or 80% of each client limit, where one client reads. The
combined reading rate is up to 94 MB / s, approximately 75% of the link limit of 125 MB / s, for 16 students, or
6 MB / s per client. Technology decreases from 80% to 75% because as the number of students increases, so do
the chances of more students learning simultaneously from the same chunkserver.
5.3.Writes
Clients write simultaneously in separate files of N. Each client writes 1 GB of data to a new file in a 1
MB write series. The level of writing combined with the theoretical limit is shown in Figure 3 (b). Fields are
limited to 67 MB / s because we need to write each byte in 3 out of 16 chunkservers, each with an input
connection of 12.5 MB / s.
One client's write rate is 6.3 MB / s, about half the limit. A major feature of this is our network stack. It
does not work well with the piping system we use to compress data in chunk duplication. Delays in transmitting
data from one to another reduce the overall writing rate.
5.4.Appends versus Writes
Appendices are widely used in our pro-duction programs. In the Cluster X, the average recording rate
included is 108: 1 per relay by 8: 1 per performance count. In Group Y, which is used by production systems,
the ratings are 3.7: 1 and 2.5: 1 respectively. Moreover, these ra-tios suggest that in both collections the
appendents tend to be larger than the writing. In Cluster X, however, the total usage of the recording time is
relatively low so the results may be offset by one or two application settings by selecting the bus size.
VI. CONCLUSIONS
We started by re-examining file system assump-tions in terms of our current and anticipated
operational and technological environment. Our vision has led to very different points in the construction space.
We treat component failures as normal rather than different, prepare large files that are heavily loaded (perhaps
simultaneously) and read (usually sequentially), and both expand and release the file system interface to
improve the entire system.
Our system provides error tolerance by constantly scanning, duplicating important data, and fast and
automatic updates. Chunk duplication allows us to tolerate chunkserverfailure. The frequency of these failures
encourages the online repair process of the novel which revaluates the damage and transparency and
compensates for lost answers very quickly. Additionally, we use data corruption testing tests at the disk or IDE
subsystem level, which is most commonly assigned to the number of disks in the system.
Our design brings the full inclusion of many similar readers and writers who perform a variety of tasks.
We do this by separating file system control, which passes through the master, from the data transfer, which
passes directly between the chunkservers and customers. Master's involvement in normal operation is reduced
by large chunk size and chunk rental, which authorizes pri-mary duplication in data conversion. This makes it
possible to be a sim-ple, a masterized master not a bottle. We believe that the development of our network stack
will remove the current limit on the transfer of writing seen by each client.
GFS has successfully met our storage needs and is widely used within Google as the ultimate platform
for research and development and processing of production data. It is an important tool that enables us to
continue to create and attack problems on the entire web scale.
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ACKNOWLEDGEMENTS
I thank my college for giving us the opportunity to make this project a success. I offer my special thanks and
sincerity.
I thank Professor Krutika vartak for encouraging me to complete this research paper, for guidance and assistance
for all the problems I encountered while doing research.Without his guidance, I would not have completed my research
paper.
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