Emerging dynamic distributed systems and challenges for Internet-scale services engineering
This document discusses emerging computing models, data and infrastructure provisioning models, and challenges for engineering Internet-scale service systems. Some key points discussed include:
- Today's computing converges technologies like cloud, social, peer-to-peer, and distributed computing. This introduces issues around scalability, elasticity, and unpredictable workloads.
- Emerging data provisioning includes real-time sensor data, open data, and marketable/commercial data. Issues involve data quality control, integration across sources, and cost versus quality tradeoffs.
- Computational infrastructures as a service are diversifying but bring complexity in APIs, integration, and data locality
Emerging Dynamic TUW-ASE Summer 2015 - Distributed Systems and Challenges for...Hong-Linh Truong
This is a lecture from the advanced service engineering course from the Vienna University of Technology. See http://dsg.tuwien.ac.at/teaching/courses/ase/
Most downloaded article for an year in academia - Advanced Computing: An Inte...acijjournal
Advanced Computing: An International Journal (ACIJ) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced computing. The journal focuses on all technical and practical aspects of high performance computing, green computing, pervasive computing, cloud computing etc. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in computing and establishing new collaborations in these areas.
The concept of Genetic algorithm is specifically useful in load balancing for best virtual
machines distribution across servers. In this paper, we focus on load balancing and also on
efficient use of resources to reduce the energy consumption without degrading cloud
performance. Cloud computing is an on demand service in which shared resources, information,
software and other devices are provided according to the clients requirement at specific time. It‟s
a term which is generally used in case of Internet. The whole Internet can be viewed as a cloud.
Capital and operational costs can be cut using cloud computing. Cloud computing is defined as a
large scale distributed computing paradigm that is driven by economics of scale in which a pool
of abstracted virtualized dynamically scalable , managed computing power ,storage , platforms
and services are delivered on demand to external customer over the internet. cloud computing is
a recent field in the computational intelligence techniques which aims at surmounting the
computational complexity and provides dynamically services using very large scalable and
virtualized resources over the Internet. It is defined as a distributed system containing a
collection of computing and communication resources located in distributed data enters which
are shared by several end users. It has widely been adopted by the industry, though there are
many existing issues like Load Balancing, Virtual Machine Migration, Server Consolidation,
Energy Management, etc.
Emerging Dynamic TUW-ASE Summer 2015 - Distributed Systems and Challenges for...Hong-Linh Truong
This is a lecture from the advanced service engineering course from the Vienna University of Technology. See http://dsg.tuwien.ac.at/teaching/courses/ase/
Most downloaded article for an year in academia - Advanced Computing: An Inte...acijjournal
Advanced Computing: An International Journal (ACIJ) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced computing. The journal focuses on all technical and practical aspects of high performance computing, green computing, pervasive computing, cloud computing etc. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in computing and establishing new collaborations in these areas.
The concept of Genetic algorithm is specifically useful in load balancing for best virtual
machines distribution across servers. In this paper, we focus on load balancing and also on
efficient use of resources to reduce the energy consumption without degrading cloud
performance. Cloud computing is an on demand service in which shared resources, information,
software and other devices are provided according to the clients requirement at specific time. It‟s
a term which is generally used in case of Internet. The whole Internet can be viewed as a cloud.
Capital and operational costs can be cut using cloud computing. Cloud computing is defined as a
large scale distributed computing paradigm that is driven by economics of scale in which a pool
of abstracted virtualized dynamically scalable , managed computing power ,storage , platforms
and services are delivered on demand to external customer over the internet. cloud computing is
a recent field in the computational intelligence techniques which aims at surmounting the
computational complexity and provides dynamically services using very large scalable and
virtualized resources over the Internet. It is defined as a distributed system containing a
collection of computing and communication resources located in distributed data enters which
are shared by several end users. It has widely been adopted by the industry, though there are
many existing issues like Load Balancing, Virtual Machine Migration, Server Consolidation,
Energy Management, etc.
Top 5 most viewed articles from academia in 2020IJCSEA Journal
Data enter total cost of ownerships (TCO) tools and spreadsheets can be used to estimate the capital and operational costs required for running datacenters. These tools are helpful for business owners to improve and evaluate the costs and the underlying efficiency of such facilities or evaluate the costs of alternatives, such as off-site computing. Well understanding of the cost drivers of TCO models can provide more opportunities to business owners to control costs .In addition, they also introduce an analytical structure in which anecdotal information can be cross-checked for consistency with other well-known parameters driving data center costs. This work focuses on comparing between number of proposed tools and spreadsheets which are publicly available to calculate datacenter total cost of ownership (TCO) ,The comparison is based on many aspects such as what are the parameters included and not included in such tools and whether the tools are documented or not. Such an approach presents a solid ground for designing more and better tools and spreadsheets in the future.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian explains data science, steps in a data science workflow and show some experiments in AzureML. He also mentions about big data issues in a data science project and solutions to them.
Today, servitization has reached its saturation point as enterprises in almost every business and continent pursued it as a differentiation strategy. Data analytics may offer the next frontier of innovation and hold the potential for enterprises to create value for their customers. Nevertheless, organizations face a series of barriers when utilizing the technologies. We apply a rigorous qualitative analysis process based on grounded theory and interview data of 15 business-to-business companies that already successfully utilize data analytics to create value for their customers. We analyzed our results in the lights of the barriers organization face in servitization and reveal that data analytics adds an additional layer of complexity. Our work contributes to the fundamental understanding of organizational transformation and should provide concrete guidance to business leaders on how to address transformation regarding the utilization of data and analytics.
Today, servitization has reached its saturation point as enterprises in almost every business and continent pursued it as a differentiation strategy. Data analytics may offer the next frontier of innovation and hold the potential for enterprises to create value for their customers. Nevertheless, organizations face a series of barriers when utilizing the technologies. We apply a rigorous qualitative analysis process based on grounded theory and interview data of 15 business-to-business companies that already successfully utilize data analytics to create value for their customers. We analyzed our results in the lights of the barriers organization face in servitization and reveal that data analytics adds an additional layer of complexity. Our work contributes to the fundamental understanding of organizational transformation and should provide concrete guidance to business leaders on how to address transformation regarding the utilization of data and analytics.
CLOUD COMPUTING IN EDUCATION: POTENTIALS AND CHALLENGES FOR BANGLADESHIJCSEA Journal
Cloud Computing is an emerging technology. It is a growing technology which can change traditional IT systems. It plays a major role in today’s technology sector. People are using it every day through one way or another. Education sector is not out of this phenomenon. At the present time the teaching method is changing and students are becoming much technology based and therefore it is necessary that we think about the most recent technologies to incorporate in the teaching and learning methods. By sharing Information technology related services in the cloud, educational institutions can better concentrate on offering students, teachers, faculty and staff the essential instruments. Bangladesh is a developing country. So applying this technology on education sector is a huge challenge for Bangladesh. In this paper it is discussed that how Bangladesh can be benefited by applying cloud in education and its challenges followed by some case studies and success stories.
AN OVERVIEW OF THE SECURITY CONCERNS IN ENTERPRISE CLOUD COMPUTINGIJNSA Journal
Deploying cloud computing in an enterprise infrastructure bring significant security concerns. Successful implementation of cloud computing in an enterprise requires proper planning and understanding of emerging risks, threats, vulnerabilities, and possible countermeasures. We believe enterprise should analyze the company/organization security risks, threats, and available countermeasures before adopting this technology. In this paper, we have discussed security risks and concerns in cloud computing and enlightened steps that an enterprise can take to reduce security risks and protect their resources. We have also explained cloud computing strengths/benefits, weaknesses, and
applicable areas in information risk management
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Since announcing its “Cloud First” policy in 2010, the Federal government has correctly identified cloud computing as a way to reduce costs and improve the use of existing assets, and has accordingly prioritized its adoption. It has also taken judicious steps to protect Federal networks from nefarious cyber-attacks and promote the dissemination of best practices for cybersecurity. The Federal government has also embraced mobility as a means to conduct work from any location. But until now, the implementation of these initiatives has been fragmented and lacked coordination across Federal agencies. This paper offers a framework for integrating these programs in a way that enables the Federal government to realize the economic, technological, and mission-effectiveness benefits of cloud services while simultaneously meeting current Federal cybersecurity
requirements. It advocates shifting from a compliance-based cybersecurity paradigm to
one that is risk-based and focusing on how to most effectively secure their implementation of cloud services.
Telecommunications networks are vast, complex graphs upon a map. Why is it then, that Telcos typically do not use graph technology as means to understand and traverse their networks of devices, systems and customers?
This webinar explores ways for Telecommunications and media vendors to experience their networks as graphs from Neo4j.
Providing a multi-objective scheduling tasks by Using PSO algorithm for cost ...Editor IJCATR
This article is intended to use the multi-PSO algorithm for scheduling tasks for cost management in cloud computing. This means that
any migration costs due to supply failure consider as a one objective and each task is a little particle and recognize by use of the
appropriate fitness schedule function (how the particles arrangement) that cost at least amount of total expense. In addition to, the weight
is granted to the each expenditure that reflects the importance of cost. The data which is used to simulate proposed method are series of
academic and research data that are prepared from the Internet and MATLAB software is used for simulation. We simulate two issues,
in the first issue, consider four task by four vehicles and divide tasks. In the second issue, make the issue more complicated and consider
six tasks by four vehicles. We write PSO's output for each two issues of various iterations. Finally, the particles dispersion and as well
as the output of the cost function were computed for each pa
Cloud computing has become the mainstream of the emerging technologies for information interchange and accessibility. With such systems, the information accessed from any geographic location on this planet with some decent kind of internet connection. Applying machine learning together with artificial intelligence in dealing with the problem of energy reduction in cloud data center is an innovative idea. A large combination of Artificial intelligence is playing a significant role in cloud environment. For that matter, the Big organization providers like Amazon have taken steps to ensure that they can continue to expand their fast-growing cloud services to commensurate with the fast growth of population. These companies have built large data centers in remote parts of the world to overcome a shortage of information. These centers consume significant amounts of electrical energy. There is often a lot of energy wastage. According to IDC white paper, data centers have tremendously wasted billions of energy regarding billing and cash. Additionally, researchers have argued that by the year 2020 the energy consumption rate would have doubled. Research in this area is still a hot topic. This paper seeks to address the energy efficiency issue at a Cloud Data Center using machine learning methodologies, principles, and practices. This article also aims to bring out possible future implementation methods for artificially intelligent agents that would help reduce energy wastage at a Cloud data center and thus help ameliorate the great big energy problem at hand.
Detecting eCommerce Fraud with Neo4j and LinkuriousNeo4j
Last year, the global eCommerce market represented $1.9 trillions. As the market expands worldwide, the opportunity for fraud keeps growing with fraudsters constantly refining their tactics to outsmart anti-fraud frameworks. From chargeback fraud to re-shipping scam or identity fraud, numerous types of fraud can impact your organization. While collecting data is essential to enable real-time risk assessment, many organizations don’t have the necessary tools to find the insights needed to block fraud attempts.
Neo4j and Linkurious offer a solution to tackle the eCommerce fraud challenge. Their combined technologies provide a 360° overview of organization’s data and allow real-time analysis and detection of eCommerce fraud patterns and activities.
In this webinar, you will learn about:
- The current trends of eCommerce frauds and the risks for organizations;
- The challenges of detecting fraud tentatives in real-time and the advantage of the graph approach;
- How to use Linkurious’ graph visualization and analysis software to prevent and investigate eCommerce fraud.
Presentation held 17 September 2015 at IBM T. J. Watson Research Center, NY
Hybrid Collective Adaptive Systems (hCAS) is a new generation of socio-technical systems where both humans and machine peers complement each other and operate jointly on complex collaborative processes (e.g., collaborative question answering, ride-sharing, collaborative software development).
This presupposes deploying ad-hoc assembled teams of human and machine services that actively collaborate and communicate among each other, exchanging different artifacts and jointly processing them. hCAS are characterized by the fundamental properties of hybridity and collectiveness, hiding from users the complexities associated with managing the collaboration and coordination of hybrid human/machine teams.
In this talk, I discuss major challenges in designing such systems (e.g., team formation, adaptability, execution orchestration) and how these can be alleviated by delegating the responsibility and the know-how needed for these duties to the participating human peers, while influencing them through appropriate programming abstractions (directly) and incentive mechanisms (indirectly). I will present the design of the hCAS named SmartSociety platform, and the programming abstractions and incentive modeling language we developed for it.
Context-aware Programming for Hybrid and Diversity-aware Collective Adaptive ...Hong-Linh Truong
Collective adaptive systems (CASs) have been researched intensively since many years. However, the recent emerging developments and advanced models in service-oriented computing, cloud computing and human computation have fostered several new forms of CASs. Among them, Hybrid and Diversity-aware CASs (HDA-CASs) characterize new types of CASs in which a collective is composed of hybrid machines and humans that collaborate together with different complementary roles. This emerging HDA-CAS poses several research chal
lenges in terms of programming, management and provisioning. In this paper, we investigate the main issues in programming HDA-CASs. First, we analyze context characterizing HDA-CASs. Second, we propose to use the concept of hybrid compute units to implement HDA-CASs that can be elastic. We call this type of HDA-CASs h2 CAS (Hybrid Compute Unit-based HDA-CAS). We then discuss a meta-view of h2CAS that describes a h 2 CAS program. We analyze and present program features for h2CAS in four main different contexts.
Enabling and controlling elasticity of cloud comput-
ing applications is a challenging issue. Elasticity programming directives have been introduced to
delegate elasticity control to infrastructures and to
separate elasticity control from application logic. Since
coordination models provide a general approach to manage interaction and elasticity control entails interactions among cloud infrastructure components, we present a coordination-based approach to elasticity control, supporting delegation and separation of concerns at design and run-time, paving the way towards coordination-aware elasticity.
Top 5 most viewed articles from academia in 2020IJCSEA Journal
Data enter total cost of ownerships (TCO) tools and spreadsheets can be used to estimate the capital and operational costs required for running datacenters. These tools are helpful for business owners to improve and evaluate the costs and the underlying efficiency of such facilities or evaluate the costs of alternatives, such as off-site computing. Well understanding of the cost drivers of TCO models can provide more opportunities to business owners to control costs .In addition, they also introduce an analytical structure in which anecdotal information can be cross-checked for consistency with other well-known parameters driving data center costs. This work focuses on comparing between number of proposed tools and spreadsheets which are publicly available to calculate datacenter total cost of ownership (TCO) ,The comparison is based on many aspects such as what are the parameters included and not included in such tools and whether the tools are documented or not. Such an approach presents a solid ground for designing more and better tools and spreadsheets in the future.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian explains data science, steps in a data science workflow and show some experiments in AzureML. He also mentions about big data issues in a data science project and solutions to them.
Today, servitization has reached its saturation point as enterprises in almost every business and continent pursued it as a differentiation strategy. Data analytics may offer the next frontier of innovation and hold the potential for enterprises to create value for their customers. Nevertheless, organizations face a series of barriers when utilizing the technologies. We apply a rigorous qualitative analysis process based on grounded theory and interview data of 15 business-to-business companies that already successfully utilize data analytics to create value for their customers. We analyzed our results in the lights of the barriers organization face in servitization and reveal that data analytics adds an additional layer of complexity. Our work contributes to the fundamental understanding of organizational transformation and should provide concrete guidance to business leaders on how to address transformation regarding the utilization of data and analytics.
Today, servitization has reached its saturation point as enterprises in almost every business and continent pursued it as a differentiation strategy. Data analytics may offer the next frontier of innovation and hold the potential for enterprises to create value for their customers. Nevertheless, organizations face a series of barriers when utilizing the technologies. We apply a rigorous qualitative analysis process based on grounded theory and interview data of 15 business-to-business companies that already successfully utilize data analytics to create value for their customers. We analyzed our results in the lights of the barriers organization face in servitization and reveal that data analytics adds an additional layer of complexity. Our work contributes to the fundamental understanding of organizational transformation and should provide concrete guidance to business leaders on how to address transformation regarding the utilization of data and analytics.
CLOUD COMPUTING IN EDUCATION: POTENTIALS AND CHALLENGES FOR BANGLADESHIJCSEA Journal
Cloud Computing is an emerging technology. It is a growing technology which can change traditional IT systems. It plays a major role in today’s technology sector. People are using it every day through one way or another. Education sector is not out of this phenomenon. At the present time the teaching method is changing and students are becoming much technology based and therefore it is necessary that we think about the most recent technologies to incorporate in the teaching and learning methods. By sharing Information technology related services in the cloud, educational institutions can better concentrate on offering students, teachers, faculty and staff the essential instruments. Bangladesh is a developing country. So applying this technology on education sector is a huge challenge for Bangladesh. In this paper it is discussed that how Bangladesh can be benefited by applying cloud in education and its challenges followed by some case studies and success stories.
AN OVERVIEW OF THE SECURITY CONCERNS IN ENTERPRISE CLOUD COMPUTINGIJNSA Journal
Deploying cloud computing in an enterprise infrastructure bring significant security concerns. Successful implementation of cloud computing in an enterprise requires proper planning and understanding of emerging risks, threats, vulnerabilities, and possible countermeasures. We believe enterprise should analyze the company/organization security risks, threats, and available countermeasures before adopting this technology. In this paper, we have discussed security risks and concerns in cloud computing and enlightened steps that an enterprise can take to reduce security risks and protect their resources. We have also explained cloud computing strengths/benefits, weaknesses, and
applicable areas in information risk management
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Since announcing its “Cloud First” policy in 2010, the Federal government has correctly identified cloud computing as a way to reduce costs and improve the use of existing assets, and has accordingly prioritized its adoption. It has also taken judicious steps to protect Federal networks from nefarious cyber-attacks and promote the dissemination of best practices for cybersecurity. The Federal government has also embraced mobility as a means to conduct work from any location. But until now, the implementation of these initiatives has been fragmented and lacked coordination across Federal agencies. This paper offers a framework for integrating these programs in a way that enables the Federal government to realize the economic, technological, and mission-effectiveness benefits of cloud services while simultaneously meeting current Federal cybersecurity
requirements. It advocates shifting from a compliance-based cybersecurity paradigm to
one that is risk-based and focusing on how to most effectively secure their implementation of cloud services.
Telecommunications networks are vast, complex graphs upon a map. Why is it then, that Telcos typically do not use graph technology as means to understand and traverse their networks of devices, systems and customers?
This webinar explores ways for Telecommunications and media vendors to experience their networks as graphs from Neo4j.
Providing a multi-objective scheduling tasks by Using PSO algorithm for cost ...Editor IJCATR
This article is intended to use the multi-PSO algorithm for scheduling tasks for cost management in cloud computing. This means that
any migration costs due to supply failure consider as a one objective and each task is a little particle and recognize by use of the
appropriate fitness schedule function (how the particles arrangement) that cost at least amount of total expense. In addition to, the weight
is granted to the each expenditure that reflects the importance of cost. The data which is used to simulate proposed method are series of
academic and research data that are prepared from the Internet and MATLAB software is used for simulation. We simulate two issues,
in the first issue, consider four task by four vehicles and divide tasks. In the second issue, make the issue more complicated and consider
six tasks by four vehicles. We write PSO's output for each two issues of various iterations. Finally, the particles dispersion and as well
as the output of the cost function were computed for each pa
Cloud computing has become the mainstream of the emerging technologies for information interchange and accessibility. With such systems, the information accessed from any geographic location on this planet with some decent kind of internet connection. Applying machine learning together with artificial intelligence in dealing with the problem of energy reduction in cloud data center is an innovative idea. A large combination of Artificial intelligence is playing a significant role in cloud environment. For that matter, the Big organization providers like Amazon have taken steps to ensure that they can continue to expand their fast-growing cloud services to commensurate with the fast growth of population. These companies have built large data centers in remote parts of the world to overcome a shortage of information. These centers consume significant amounts of electrical energy. There is often a lot of energy wastage. According to IDC white paper, data centers have tremendously wasted billions of energy regarding billing and cash. Additionally, researchers have argued that by the year 2020 the energy consumption rate would have doubled. Research in this area is still a hot topic. This paper seeks to address the energy efficiency issue at a Cloud Data Center using machine learning methodologies, principles, and practices. This article also aims to bring out possible future implementation methods for artificially intelligent agents that would help reduce energy wastage at a Cloud data center and thus help ameliorate the great big energy problem at hand.
Detecting eCommerce Fraud with Neo4j and LinkuriousNeo4j
Last year, the global eCommerce market represented $1.9 trillions. As the market expands worldwide, the opportunity for fraud keeps growing with fraudsters constantly refining their tactics to outsmart anti-fraud frameworks. From chargeback fraud to re-shipping scam or identity fraud, numerous types of fraud can impact your organization. While collecting data is essential to enable real-time risk assessment, many organizations don’t have the necessary tools to find the insights needed to block fraud attempts.
Neo4j and Linkurious offer a solution to tackle the eCommerce fraud challenge. Their combined technologies provide a 360° overview of organization’s data and allow real-time analysis and detection of eCommerce fraud patterns and activities.
In this webinar, you will learn about:
- The current trends of eCommerce frauds and the risks for organizations;
- The challenges of detecting fraud tentatives in real-time and the advantage of the graph approach;
- How to use Linkurious’ graph visualization and analysis software to prevent and investigate eCommerce fraud.
Presentation held 17 September 2015 at IBM T. J. Watson Research Center, NY
Hybrid Collective Adaptive Systems (hCAS) is a new generation of socio-technical systems where both humans and machine peers complement each other and operate jointly on complex collaborative processes (e.g., collaborative question answering, ride-sharing, collaborative software development).
This presupposes deploying ad-hoc assembled teams of human and machine services that actively collaborate and communicate among each other, exchanging different artifacts and jointly processing them. hCAS are characterized by the fundamental properties of hybridity and collectiveness, hiding from users the complexities associated with managing the collaboration and coordination of hybrid human/machine teams.
In this talk, I discuss major challenges in designing such systems (e.g., team formation, adaptability, execution orchestration) and how these can be alleviated by delegating the responsibility and the know-how needed for these duties to the participating human peers, while influencing them through appropriate programming abstractions (directly) and incentive mechanisms (indirectly). I will present the design of the hCAS named SmartSociety platform, and the programming abstractions and incentive modeling language we developed for it.
Context-aware Programming for Hybrid and Diversity-aware Collective Adaptive ...Hong-Linh Truong
Collective adaptive systems (CASs) have been researched intensively since many years. However, the recent emerging developments and advanced models in service-oriented computing, cloud computing and human computation have fostered several new forms of CASs. Among them, Hybrid and Diversity-aware CASs (HDA-CASs) characterize new types of CASs in which a collective is composed of hybrid machines and humans that collaborate together with different complementary roles. This emerging HDA-CAS poses several research chal
lenges in terms of programming, management and provisioning. In this paper, we investigate the main issues in programming HDA-CASs. First, we analyze context characterizing HDA-CASs. Second, we propose to use the concept of hybrid compute units to implement HDA-CASs that can be elastic. We call this type of HDA-CASs h2 CAS (Hybrid Compute Unit-based HDA-CAS). We then discuss a meta-view of h2CAS that describes a h 2 CAS program. We analyze and present program features for h2CAS in four main different contexts.
Enabling and controlling elasticity of cloud comput-
ing applications is a challenging issue. Elasticity programming directives have been introduced to
delegate elasticity control to infrastructures and to
separate elasticity control from application logic. Since
coordination models provide a general approach to manage interaction and elasticity control entails interactions among cloud infrastructure components, we present a coordination-based approach to elasticity control, supporting delegation and separation of concerns at design and run-time, paving the way towards coordination-aware elasticity.
TUW- 184.742 Emerging Dynamic Distributed Systems and Challenges for Advanced...Hong-Linh Truong
This presentation is part of the course "184.742 Advanced Services Engineering" at The Vienna University of Technology, in Winter Semester 2012. Check the course at: http://www.infosys.tuwien.ac.at/teaching/courses/ase/
TUW-ASE Summer 2015: Advanced service-based data analytics: Models, Elasticit...Hong-Linh Truong
This is a lecture from the advanced service engineering course from the Vienna University of Technology. See http://dsg.tuwien.ac.at/teaching/courses/ase
Review of Business Information Systems – Fourth Quarter 2013 V.docxmichael591
Review of Business Information Systems – Fourth Quarter 2013 Volume 17, Number 4
2013 The Clute Institute Copyright by author(s) Creative Commons License CC-BY 159
Dimensions Of Security Threats In Cloud
Computing: A Case Study
Mathew Nicho, University of Dubai, UAE
Mahmoud Hendy, University of Dubai, UAE
ABSTRACT
Even though cloud computing, as a model, is not new, organizations are increasingly
implementing it because of its large-scale computation and data storage, flexible scalability,
relative reliability, and cost economy of services. However, despite its rapid adoption in some
sectors and domains, it is evident from research and statistics, that security-related threats are the
most noticeable barrier to its widespread adoption. To investigate the reasons behind these
threats, the authors used available literature to identify and aggregate information about IS
security threats in cloud computing. Based on this information, the authors explored the
dimensions of the nature of threat by interviewing a cloud computing practitioner in an
organization that uses both the private and public cloud deployment models. From these findings,
the authors found that IS security threats in cloud computing must be defined at different levels;
namely, at the business and technical level, as well as from a generic and cloud-specific threat
perspective. Based on their findings, the authors developed the Cloud Computing Threat Matrix
(CCTM) which provides a two-dimensional definition of threat that enables cloud users to fully
comprehend the concerns so that they can make relevant decisions while availing cloud computing
services.
Keywords: Cloud Computing; Security; Cloud Security Issues Taxonomy; Threat Matrix
INTRODUCTION
ecause a cloud is a collection of inter-connected and virtualized computers (Buyya et al., 2008), the
main enabling technology for cloud computing is virtualization. The basic concept of cloud is based
on the premise that instead of having selected information systems (IS) resources, such as software
and data stored locally on a user’s or organization’s computer systems, these resources can be stored on Internet
servers, called “clouds,” and accessed anytime, anywhere as a paid service on the Internet. Cloud computing has the
potential to bring significant benefits to small- and medium-sized businesses by reducing the costs of investment in
information communication technology (ICT) infrastructure because it enables the use of services, such as
computation, software, data access, and storage by end-users, without the need to know the physical location and
configuration of the system that delivers the services (Mujinga & Chipangura, 2011). However, it has been stated
that organizations adopt cloud computing projects and systems cautiously while maximizing benefits and
minimizing risks (Lawler, Joseph, & Howell-Barber, 2012). Cloud computing is expected to play .
Within this tutorial we present the results of recent research about the cloud enablement of data streaming systems. We illustrate, based on both industrial as well as academic prototypes, new emerging uses cases and research trends. Specifically, we focus on novel approaches for (1) fault tolerance and (2) scalability in large scale distributed streaming systems. In general, new fault tolerance mechanisms strive to be more robust and at the same time introduce less overhead. Novel load balancing approaches focus on elastic scaling over hundreds of instances based on the data and query workload. Finally, we present open challenges for the next generation of cloud-based data stream processing engines.
Review and Classification of Cloud Computing Researchiosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.
Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels
TUW-ASE Summer 2015: Data marketplaces: core models and conceptsHong-Linh Truong
This is a lecture from the advanced service engineering course from the Vienna University of Technology. See http://dsg.tuwien.ac.at/teaching/courses/ase/
Exploring Cloud Computing Technologies For GIS (Location Based) ApplicationsChristopher Blough
Online GIS applications can be delivered using cloud computing platforms which provide Infrastructure as a Service(IaaS) resources. An introduction to essential cloud computing concepts and considerations will be reviewed in addition to a presentation of public sector industry trends involving other cloud hosted technologies related to GIS applications. The presentation will feature examples of cloud hosted GIS applications at federal, state, and local government levels including the City of Novi\'s ArcGIS Server 10 deployment using a public cloud hosting provider.
Integrated Analytics for IIoT Predictive Maintenance using IoT Big Data Cloud...Hong-Linh Truong
For predictive maintenance of equipment with In-
dustrial Internet of Things (IIoT) technologies, existing IoT Cloud
systems provide strong monitoring and data analysis capabilities
for detecting and predicting status of equipment. However, we
need to support complex interactions among different software
components and human activities to provide an integrated analyt-
ics, as software algorithms alone cannot deal with the complexity
and scale of data collection and analysis and the diversity of
equipment, due to the difficulties of capturing and modeling
uncertainties and domain knowledge in predictive maintenance.
In this paper, we describe how we design and augment complex
IoT big data cloud systems for integrated analytics of IIoT
predictive maintenance. Our approach is to identify various
complex interactions for solving system incidents together with
relevant critical analytics results about equipment. We incorpo-
rate humans into various parts of complex IoT Cloud systems
to enable situational data collection, services management, and
data analytics. We leverage serverless functions, cloud services,
and domain knowledge to support dynamic interactions between
human and software for maintaining equipment. We use a real-
world maintenance of Base Transceiver Stations to illustrate our
engineering approach which we have prototyped with state-of-
the art cloud and IoT technologies, such as Apache Nifi, Hadoop,
Spark and Google Cloud Functions.
Modeling and Provisioning IoT Cloud Systems for Testing UncertaintiesHong-Linh Truong
Modern Cyber-Physical Systems (CPS) and Internet of Things (IoT)
systems consist of both loosely and tightly interactions among
various resources in IoT networks, edge servers and cloud data
centers. These elements are being built atop virtualization layers
and deployed in both edge and cloud infrastructures. They also deal
with a lot of data through the interconnection of different types of
networks and services. Therefore, several new types of uncertainties
are emerging, such as data, actuation, and elasticity uncertainties.
This triggers several challenges for testing uncertainty in such
systems. However, there is a lack of novel ways to model and
prepare the right infrastructural elements covering requirements
for testing emerging uncertainties. In this paper, first we present
techniques for modeling CPS/IoT Systems and their uncertainties
to be tested. Second, we introduce techniques for determining and
generating deployment configuration for testing in different IoT
and cloud infrastructures. We illustrate our work with a real-world
use case for monitoring and analysis of Base Transceiver Stations.
Testing Uncertainty of Cyber-Physical Systems in IoT Cloud Infrastructures: C...Hong-Linh Truong
Today’s cyber-physical systems (CPS) span IoT and cloud-based
datacenter infrastructures, which are highly heterogeneous with
various types of uncertainty. Thus, testing uncertainties in these
CPS is a challenging and multidisciplinary activity. We need several
tools for modeling, deployment, control, and analytics to test and
evaluate uncertainties for different configurations of the same CPS.
In this paper, we explain why using state-of-the art model-driven
engineering (MDE) and model-based testing (MBT) tools is not
adequate for testing uncertainties of CPS in IoT Cloud infrastruc-
tures. We discus how to combine them with techniques for elastic
execution to dynamically provision both CPS under test and testing
utilities to perform tests in various IoT Cloud infrastructures.
Towards a Resource Slice Interoperability Hub for IoTHong-Linh Truong
Interoperability for IoT is a challenging problem
because it requires us to tackle (i) cross-system interoperability
issues at the IoT platform sides as well as relevant network
functions and clouds in the edge systems and data centers
and (ii) cross-layer interoperability, e.g., w.r.t. data formats,
communication protocols, data delivery mechanisms, and perfor-
mance. However, existing solutions are quite static w.r.t software
deployment and provisioning for interoperability. Many middle-
ware, services and platforms have been built and deployed as
interoperability bridges but they are not dynamically provisioned
and reconfigured for interoperability at runtime. Furthermore,
they are often not considered together with other services as a
whole in application-specific contexts. In this paper, we focus
on dynamic aspects by introducing the concept of Resource
Slice Interoperability Hub (rsiHub). Our approach leverages
existing software artifacts and services for interoperability to
create and provision dynamic resource slices, including IoT,
network functions and clouds, for addressing application-specific
interoperability requirements. We will present our key concepts,
architectures and examples toward the realization of rsiHub.
On Supporting Contract-aware IoT Dataspace ServicesHong-Linh Truong
Advances in the Internet of Things (IoT) enable a
huge number of connected devices that produce large amounts
of data. Such data is increasingly shared among various
stakeholders to support advanced (predictive) analytics and
precision decision making in different application domains like
smart cities and industrial internet. Currently there are several
platforms that facilitate sharing, buying and selling IoT data.
However, these platforms do not support the establishment and
monitoring of usage contracts for IoT data. In this paper we
address this research issue by introducing a new extensible
platform for enabling contract-aware IoT dataspace services,
which supports data contract specification and IoT data flow
monitoring based on established data contracts. We present
a general architecture of contract monitoring services for
IoT dataspaces and evaluate our platform through illustrative
examples with real-world datasets and through performance
analysis.
Towards the Realization of Multi-dimensional Elasticity for Distributed Cloud...Hong-Linh Truong
As multiple types of distributed, heterogeneous cloud computing environments have proliferated, cloud software can leverage
diverse types of infrastructural, platform and data resources with di
erent cost and quality models. This introduces a multi-
dimensional elasticity perspective for cloud software that would greatly meet changing demands from the user. However, we argue
that current techniques are not enough for dealing with multi-dimensional elasticity in distributed cloud environments. We present
our approach to the realization of multi-dimensional elasticity by introducing novel concepts and a roadmap to achieve them.
On Engineering Analytics of Elastic IoT Cloud SystemsHong-Linh Truong
Developing IoT cloud platforms is very challenging, as IoT
cloud platforms consist of a mix of cloud services and IoT elements, e.g.,
for sensor management, near-realtime events handling, and data analyt-
ics. Developers need several tools for deployment, control, governance
and analytics actions to test and evaluate designs of software compo-
nents and optimize the operation of di erent design con gurations. In
this paper, we describe requirements and our techniques on support-
ing the development and testing of IoT cloud platforms. We present our
choices of tools and engineering actions that help the developer to design,
test and evaluate IoT cloud platforms in multi-cloud environments.
HINC – Harmonizing Diverse Resource Information Across IoT, Network Functions...Hong-Linh Truong
Effective resource management in IoT systems must
represent IoT resources, edge-to-cloud network capabilities, and
cloud resources at a high-level, while being able to link to diverse
low-level types of IoT devices, network functions, and cloud
computing infrastructures. Hence resource management in such
a context demands a highly distributed and extensible approach,
which allows us to integrate and provision IoT, network functions,
and cloud resources from various providers. In this paper, we
address this crucial research issue. We first present a high-
level information model for virtualized IoT, network functions
and cloud resource modeling, which also incorporates software-
defined gateways, network slicing and data centers. This model
is used to glue various low-level resource models from different
types of infrastructures in a distributed manner to capture
sets of resources spanning across different sub-networks. We
then develop a set of utilities and a middleware to support
the integration of information about distributed resources from
various sources. We present a proof of concept prototype with
various experiments to illustrate how various tasks in IoT cloud
systems can be simplified as well as to evaluate the performance
of our framework.
SINC – An Information-Centric Approach for End-to-End IoT Cloud Resource Prov...Hong-Linh Truong
We present SINC –
Slicing IoT, Network Functions, and Clouds – which enables designers to dynamically create/update end-to-end slices of the overall IoT network in order to simultaneously meet multiple user needs.
Governing Elastic IoT Cloud Systems under UncertaintiesHong-Linh Truong
we introduce U-GovOps – a novel framework for
dynamic, on-demand governance of elastic IoT cloud systems under
uncertainty. We introduce a declarative policy language to simplify
the development of uncertainty- and elasticity-aware governance
strategies. Based on that we develop runtime mechanisms, which
enable mitigating the uncertainties by monitoring and governing
the IoT cloud systems through specified strategies.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
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Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
TUW-ASE-Summer 2014: Emerging Dynamic Distributed Systems and Challenges for Internet-scale Services Engineering
1. Emerging Dynamic Distributed Systems
and Challenges for Internet-scale Services
Engineering
Hong-Linh Truong
Distributed Systems Group,
Vienna University of Technology
truong@dsg.tuwien.ac.at
http://dsg.tuwien.ac.at/staff/truong
1ASE Summer 2014
Advanced Services Engineering,
Summer 2014
Advanced Services Engineering,
Summer 2014
2. Outline
Today‘s Internet-scale Computing
Some emerging models – properties and issues
Data provisioning models
Computational infrastructures/frameworks
provisioning
Human computation provisioning
Software-defined *
Internet-scale service engineering
Single service/platform and Internet-scale multi-
platform services engineering
ASE Summer 2014 2
3. Today‘s Computing Models
Internet infrastructure and software connect
contents, things, and people, each has different
roles (computation, sensing, analytics, etc.)
ASE Summer 2014 3
PeopleSoftware
Things
Size does
matter
Large-scale
interactions
Big data
generated
Big quantities to be
managed
Hard to control quality
of data and services
Any * access
behaviour does
matter
Unpredictable
workload
Scalability
Elasticity
Software-defined
Economic
factors do
matter
On-demand,
pay-as-you-go
Complex
contracts
Internet infrastructure and
software
4. Today‘s Computing Models
ASE Summer 2014 4
Social
computing
Service
Computing
Distributed
Computing
Peer-to-
Peer
Computing
Cloud
Computing
converge
PeopleSoftware
Things Emerging forms of
computing
models, systems
and applications
introduces
Technologies and
computing models
Big and high performance
centralized data analytics
oT data streaming analytics
Large-scale applications
spanning data centers and edge
servers/gateways
Adaptive collective systems of
humans and machines
Big and high performance
centralized data analytics
oT data streaming analytics
Large-scale applications
spanning data centers and edge
servers/gateways
Adaptive collective systems of
humans and machines
5. WHICH ARE EMERGING
FORMS OF COMPUTING
MODELS, SYSTEMS AND
APPLICATIONS THAT YOU
SEE?
Discussion time:
ASE Summer 2014 5
6. Some emerging data provisioning
models (1)
ASE Summer 2014 6
• Satellites and environmental/city sensor networks
(e.g., from specific orgs/countries)
• Machine-to-machine (e.g., from companies)
• Social media (e.g., from people + platform providers)
Large (near-
) realtime
data
• Open science and engineering data sets
• Open government dataOpen data
• Statistics and business data
• Commercial data in general
Marketable
data
Data are assetsData are assets
7. Some emerging data provisioning
models (2)
ASE Summer 2014 7
Social
Platforms
Social
Platforms
Things
EnvirontmentsEnvirontments
InfrastructuresInfrastructures
........
Data/Service Platforms
APPs
Data
Storage
Data
Storage
Data Profiling
and Enrichment
Data Profiling
and Enrichment
Data
Analytics
Data
Analytics
Data
Query
Data
Query
......
A lot A few A lot
8. Examples of large-scale (near-)
realtime data
ASE Summer 2014 8
Xively Cloud Services™
https://xively.com/
9. Large-scale (near-)realtime data:
properties and issues
Some properties
Having massive data
Requiring large-scale, big
(near-) real time
processing and storing
capabilities
Enabling predictive and
realtime data analytics
Some issues
Timely analytics
Performance and
scalability
Quality of data control
Handle of unknown data
patterns
Benefit/cost versus
quality tradeoffs
ASE Summer 2014 9
11. Open data: properties and issues
Some properties
Having large, multiple
data sources but mainly
static data
Having good quality
control in many cases
Usually providing the
data as a whole set
Some issues
Fine-grained content
search
Balance between
processing cost and
performance
Correlation/combination
with real-time data
ASE Summer 2014 11
13. Marketable data: properties and
issues
Some properties
Can be large, multiple
data sources but mainly
static data
Having good quality
control
Have strong data contract
terms
Some do not offer the
whole dataset
Some issues
Multiple levels of
service/data contracts
Compatible with other
data sources w.r.t.
contract
Cost w.r.t. up-to-date
data
Near-realtime data
marketplaces
ASE Summer 2014 13
14. Emerging computational
infrastructure/platform provisioning
models
Infrastructure-as-a-Service
Machine as a service
Storage as a Service
Database as a Service
Network as a Service
Platform-as-a-Service
Application middleware
Computational frameworks
Management middleware (e.g., monitoring, control,
deployment)
ASE Summer 2014 14
15. Examples of Infrastructure-as-a-
Service
ASE Summer 2014 15
Source: Hong Linh Truong, Schahram Dustdar: Cloud computing for small research groups in computational science and engineering: current
status and outlook. Computing 91(1): 75-91 (2011)
Source: Hong Linh Truong, Schahram Dustdar: Cloud computing for small research groups in computational science and engineering: current
status and outlook. Computing 91(1): 75-91 (2011)
And more
MongoLabMongoLab
Amazon S3Amazon S3
OKEANOSOKEANOS
Microsoft AruzeMicrosoft Aruze
OUTDATED but still useful for
illustrating the IaaS ecosystem
16. Examples of Platform-as-a-Service
ASE Summer 2014 16
Source: Hong Linh Truong, Schahram Dustdar: Cloud computing for small research groups in computational science and engineering:
current status and outlook. Computing 91(1): 75-91 (2011)
Source: Hong Linh Truong, Schahram Dustdar: Cloud computing for small research groups in computational science and engineering:
current status and outlook. Computing 91(1): 75-91 (2011)
And more
Amazon Elastic MapReduceAmazon Elastic MapReduce
StormMQStormMQ Globus Online (GO)Globus Online (GO)
OUTDATED but still useful for
illustrating the PaaS ecosystem
17. ASE Summer 2014 17
Examples of multiple clouds
aaa
Source: Katarzyna Keahey, Mauricio Tsugawa, Andrea Matsunaga, and Jose Fortes. 2009. Sky Computing. IEEE Internet
Computing 13, 5 (September 2009), 43-51. DOI=10.1109/MIC.2009.94 http://dx.doi.org/10.1109/MIC.2009.94
Source: Katarzyna Keahey, Mauricio Tsugawa, Andrea Matsunaga, and Jose Fortes. 2009. Sky Computing. IEEE Internet
Computing 13, 5 (September 2009), 43-51. DOI=10.1109/MIC.2009.94 http://dx.doi.org/10.1109/MIC.2009.94
18. Emerging computational
infrastructure/platform provisioning
models– properties and issues
Some properties
Rich types of services
from multiple providers
Better choices in terms of
functions and costs
Concepts are similar but
diverse APIs
Strong
dependencies/tight
ecosystems
Some issues
On-demand information
management from
multiple sources
APIs complexity and API
management
Cross-vendor integration
Data locality
ASE Summer 2014 18
19. Emerging human computation
models
Crowdsourcing platforms
(Anonymous) people computing capabilities exploited
via task bids
Individual Compute Unit
An individual is treated like „a processor“ or “functional
unit“. A service can wrap human capabilities to support
the communication and coordination of tasks
Social Compute Unit
A set of people and software that are initiated and
provisioned as a service for solving tasks
ASE Summer 2014 19
The main point: humans are a computing elementThe main point: humans are a computing element
20. Examples of human computation
(1)
ASE Summer 2014 20
Source: Salman Ahmad, Alexis Battle, Zahan Malkani, Sepandar D. Kamvar: The jabberwocky programming environment for structured
social computing. UIST 2011: 53-64
Source: Salman Ahmad, Alexis Battle, Zahan Malkani, Sepandar D. Kamvar: The jabberwocky programming environment for structured
social computing. UIST 2011: 53-64
21. Examples of human computation
(2)
ASE Summer 2014 21
Source: Daniel W. Barowy, Charlie Curtsinger, Emery D. Berger, Andrew McGregor: AutoMan: a platform for integrating human-based
and digital computation. OOPSLA 2012: 639-654
Source: Daniel W. Barowy, Charlie Curtsinger, Emery D. Berger, Andrew McGregor: AutoMan: a platform for integrating human-based
and digital computation. OOPSLA 2012: 639-654
22. Examples of human computation
(3)
ASE Summer 2014 22
Source: Muhammad Z.C. Candra, Rostyslav Zabolotnyi, Hong-Linh Truong, and Schahram Dustdar, Virtualizing Software and Human for
Elastic Hybrid Services, Web Services Handbook, (c)Springer-Verlag, 2012.
Source: Muhammad Z.C. Candra, Rostyslav Zabolotnyi, Hong-Linh Truong, and Schahram Dustdar, Virtualizing Software and Human for
Elastic Hybrid Services, Web Services Handbook, (c)Springer-Verlag, 2012.
23. Human computation models –
properties and issues
Some properties
Huge number of people
Capabilities might not
know in advance
Simple coordination
models
Some issues
Quality control
Reliability assurance
Proactive, on-demand
acquisition
Incentive strategies
Collectives
ASE Summer 2014 23
Source: Muhammad Z.C. Candra, Rostyslav Zabolotnyi, Hong-Linh Truong, and Schahram Dustdar, Virtualizing Software and Human for
Elastic Hybrid Services, Advanced Web Services Handbook, (c)Springer-Verlag, 2014.
Source: Muhammad Z.C. Candra, Rostyslav Zabolotnyi, Hong-Linh Truong, and Schahram Dustdar, Virtualizing Software and Human for
Elastic Hybrid Services, Advanced Web Services Handbook, (c)Springer-Verlag, 2014.
24. Emerging Software-defined *
Goal
To have better way to manage dynamic changes in
computation, network and data
Software-defined concepts
Capabilities to manage and control computation, data,
and network features at runtime using software
Management and control are performed via open APIs
Software-defined techniques
Software-defined networking, Software-defined
environments, Software-defined services
ASE Summer 2014 24
K IRKPATRICK , K. Software-defined networking. Commun. ACM 56, 9 (Sept. 2013), 16–19.
L ANGO , J. Toward software-defined slas. Commun. ACM 57, 1 (Jan. 2014), 54–60.
S UGIKI , A., AND K ATO , K. Elements and composition of software-defined data centers. In Proceedings of the Posters and Demo
Track (New York, NY, USA, 2012), Middleware ’12, ACM, pp. 3:1–3:2.
K IRKPATRICK , K. Software-defined networking. Commun. ACM 56, 9 (Sept. 2013), 16–19.
L ANGO , J. Toward software-defined slas. Commun. ACM 57, 1 (Jan. 2014), 54–60.
S UGIKI , A., AND K ATO , K. Elements and composition of software-defined data centers. In Proceedings of the Posters and Demo
Track (New York, NY, USA, 2012), Middleware ’12, ACM, pp. 3:1–3:2.
25. Summary of emerging models wrt
advanced service-based systems
ASE Summer 2014 25
PeopleSoftware
Things
Engineering advanced service-
based systems
Engineering advanced service-
based systems
utilize/consist of
Emerging data
provisioning models
Emerging computational
infrastructure/platform
provisioning models
Emerging human
computation
models
Emerging data
provisioning
models
Emerging data
provisioning models
How can we deal with dynamic changes?
26. WHERE WE CAN FIND SOME
OPPORTUNITIES?
DO I NEED TO TAKE THEM?
WHY?
Discussion time:
ASE Summer 2014 26
27. Recall our motivating example (1)
ASE Summer 2014 27
Equipment Operation
and Maintenance
Equipment Operation
and Maintenance
Civil protectionCivil protection
Building Operation
Optimization
Building Operation
Optimization
Cities, e.g. including:
10000+ buildings
1000000+ sensors
Near
realtime
analytics
Near
realtime
analytics
Predictive
data
analytics
Visual
Analytics
Enterprise
Resource
Planning
Enterprise
Resource
Planning
Emergency
Management
Emergency
Management
Internet/public cloud
boundary
Organization-specific
boundary
Tracking/Log
istics
Tracking/Log
istics
Infrastructure
Monitoring
Infrastructure
Monitoring
Infrastructure/Internet of Things
......
Can we combine open government data
with building monitoring data?
28. Recall our motivating
example (2)
ASE Summer 2014 28
A lot of input data (L0):
~2.7 TB per day
A lot of results (L1, L2):
e.g., L1 has ~140 MB per
day for a grid of
1kmx1km
Soil
moisture
analysis for
Sentinel-1
Michael Hornacek,Wolfgang Wagner, Daniel Sabel, Hong-Linh Truong, Paul Snoeij, Thomas Hahmann, Erhard Diedrich, Marcela Doubkova,
Potential for High Resolution Systematic Global Surface Soil Moisture Retrieval Via Change Detection Using Sentinel-1, IEEE
Journal of Selected Topics in Applied Earth Observations and Remote Sensing, April, 2012
Michael Hornacek,Wolfgang Wagner, Daniel Sabel, Hong-Linh Truong, Paul Snoeij, Thomas Hahmann, Erhard Diedrich, Marcela Doubkova,
Potential for High Resolution Systematic Global Surface Soil Moisture Retrieval Via Change Detection Using Sentinel-1, IEEE
Journal of Selected Topics in Applied Earth Observations and Remote Sensing, April, 2012
Can we combine them
with open government
data?
Can we combine them
with open government
data?
29. Recall our motivating example (3)
ASE Summer 2014 29
Source: http://www.undata-api.org/
Source:
http://www.strikeiron.com/Catalog/StrikeIronServices.aspx
Source: http://docs.gnip.com/w/page/23722723/Introduction-
to-Gnip
32. Internet-scale service engineering –
the elasticity
More data more
computational resources
(e.g. more VMs)
More types of data
more computational models
more analytics
processes
Change quality of results
Change quality of data
Change response time
Change cost
Change types of result
(form of the data
output, e.g. tree, visual,
story, etc.)
More data more
computational resources
(e.g. more VMs)
More types of data
more computational models
more analytics
processes
Change quality of results
Change quality of data
Change response time
Change cost
Change types of result
(form of the data
output, e.g. tree, visual,
story, etc.)
Data
Computational
Model
Analytics
Process
Analytics Result
Data
Data
DataxDatax
DatayDatay
DatazDataz
Computational
Model
Computational
ModelComputational
Model
Computational
ModelComputational
Model
Computational
Model
Analytics
Process
Analytics
ProcessAnalytics
Process
Analytics
ProcessAnalytics
Process
Analytics
Process
Quality of
Result
ASE Summer 2014 32
Hong-Linh Truong, Schahram Dustdar, "Principles of Software-defined
Elastic Systems for Big Data Analytics", (c) IEEE Computer
Society, IEEE International Workshop on Software Defined
Systems, 2014 IEEE International Conference on Cloud
Engineering (IC2E 2014), Boston, Massachusetts, USA, 10-14
March 2014
Hong-Linh Truong, Schahram Dustdar, "Principles of Software-defined
Elastic Systems for Big Data Analytics", (c) IEEE Computer
Society, IEEE International Workshop on Software Defined
Systems, 2014 IEEE International Conference on Cloud
Engineering (IC2E 2014), Boston, Massachusetts, USA, 10-14
March 2014
33. Internet-scale service engineering -
- big/near-real time data impact
Which are data concerns that impact the data
processing?
How to use data concerns to optimize data
analytics and service provisioning?
How to use available data assets for advanced
services in an elastic manner?
What are the role of human-based servies in
dealing with complex data analytics?
ASE Summer 2014 33
34. Internet-scale service engineering -
- Steps
ASE Summer 2014 34
Large-scale, multi-platform services engineering
Identify
platform/application
problems
Identify the scale,
complexity and *city
design units, selection
of existing service
units;
development and
Integration,
Optimization
Understanding Properties/Concerns
Data /Service/Application
concerns; their dependencies
Monitoring, evaluation and
provisioning of concerns
Utilization of data/service
concerns
Single service/platform engineering
Service units for representing
fundamental things, people
and software
Provisioning of fundamental
service units
Engineering with single
service units
36. Single service/platform engineering
– service unit (1)
The service model and the unit concept can be applied
to things, people and software
ASE Summer 2014 36
Service
model
Unit
Concept
Service
unit
„basic
component“/“basic
function“ modeling
and description
Consumption,
ownership,
provisioning, price, etc.
37. Single service/platform engineering
– service units (2)
ASE Summer 2014 37
Source: Stefan Tai, Philipp Leitner, Schahram Dustdar: Design by Units: Abstractions for Human and Compute Resources for Elastic Systems.
IEEE Internet Computing 16(4): 84-88 (2012)
Source: Stefan Tai, Philipp Leitner, Schahram Dustdar: Design by Units: Abstractions for Human and Compute Resources for Elastic Systems.
IEEE Internet Computing 16(4): 84-88 (2012)
38. Single service/platform engineering
– service unit provisioning
Provisioning software under services
Provisioning things under services
Provisioning human under services
Crowd platforms of massive numbers of individuals
Individual Compute Unit (ICU)
Social Compute Unit (SCU)
ASE Summer 2014 38
1. Mark Turner, David Budgen, and Pearl Brereton. 2003. Turning Software into a Service. Computer 36, 10 (October 2003), 38-44.
DOI=10.1109/MC.2003.1236470 http://dx.doi.org/10.1109/MC.2003.1236470
2. Luigi Atzori, Antonio Iera, and Giacomo Morabito. 2010. The Internet of Things: A survey. Comput. Netw. 54, 15 (October 2010), 2787-2805.
DOI=10.1016/j.comnet.2010.05.010 http://dx.doi.org/10.1016/j.comnet.2010.05.010
3. Dominique Guinard, Vlad Trifa, Stamatis Karnouskos, Patrik Spiess, Domnic Savio: Interacting with the SOA-Based Internet of Things:
Discovery, Query, Selection, and On-Demand Provisioning of Web Services. IEEE T. Services Computing 3(3): 223-235 (2010)
4. Schahram Dustdar, Kamal Bhattacharya: The Social Compute Unit. IEEE Internet Computing 15(3): 64-69 (2011)
5. Hong-Linh Truong, Schahram Dustdar, Kamal Bhattacharya "Programming Hybrid Services in the Cloud", Springer-Verlag, 10th
International Conference on Service-oriented Computing (ICSOC 2012), November 12-16, 2012, Shanghai, China
1. Mark Turner, David Budgen, and Pearl Brereton. 2003. Turning Software into a Service. Computer 36, 10 (October 2003), 38-44.
DOI=10.1109/MC.2003.1236470 http://dx.doi.org/10.1109/MC.2003.1236470
2. Luigi Atzori, Antonio Iera, and Giacomo Morabito. 2010. The Internet of Things: A survey. Comput. Netw. 54, 15 (October 2010), 2787-2805.
DOI=10.1016/j.comnet.2010.05.010 http://dx.doi.org/10.1016/j.comnet.2010.05.010
3. Dominique Guinard, Vlad Trifa, Stamatis Karnouskos, Patrik Spiess, Domnic Savio: Interacting with the SOA-Based Internet of Things:
Discovery, Query, Selection, and On-Demand Provisioning of Web Services. IEEE T. Services Computing 3(3): 223-235 (2010)
4. Schahram Dustdar, Kamal Bhattacharya: The Social Compute Unit. IEEE Internet Computing 15(3): 64-69 (2011)
5. Hong-Linh Truong, Schahram Dustdar, Kamal Bhattacharya "Programming Hybrid Services in the Cloud", Springer-Verlag, 10th
International Conference on Service-oriented Computing (ICSOC 2012), November 12-16, 2012, Shanghai, China
39. Single service/platform engineering
– examples (1)
Service engineering with a single
system/platform
Using Excel to access Azure datamarket places
Using Boto to access data in Amazon S3
Using Hadoop within a cluster to process local data
Using workflows to process data (e.g.,
Trident/Taverna/ASKALON)
Using StormMQ to store messages
ASE Summer 2014 39
41. Internet-scale multi-platform
services engineering – required
technologies
ASE Summer 2014 41
Internet-scale,
Multi-platform
Services
Engineering for
Software, Things
and People
Internet-scale,
Multi-platform
Services
Engineering for
Software, Things
and People
Data
analysis/Computation
services in cluster
(e.g., Hadoop)
Data services (e.g.,
Azure, S3)
Middleware (e.g.,
StormMQ)
Workflows (e.g.,
Trident)
Crowd platforms,
human-based service
platforms(e.g.,
Mturks, VieCOM)
Billing/Monitoring
(e.g.,
thecurrencycloud)
42. From service unit to elastic service
unit
Elastic
Service
Unit
Service
model
Unit
Dependency
Elastic
Capability
Function
Software-
defined APIs
Hong-Linh Truong, Schahram Dustdar, Georgiana Copil, Alessio Gambi, Waldemar Hummer, Duc-Hung Le, Daniel Moldovan, "CoMoT - a
Platform-as-a-Service for Elasticity in the Cloud", (c) IEEE Computer Society, IEEE International Workshop on the Future of PaaS
(PaaS2014), 2014 IEEE International Conference on Cloud Engineering (IC2E 2014), Boston, Massachusetts, USA, 10-14 March 201
Hong-Linh Truong, Schahram Dustdar, Georgiana Copil, Alessio Gambi, Waldemar Hummer, Duc-Hung Le, Daniel Moldovan, "CoMoT - a
Platform-as-a-Service for Elasticity in the Cloud", (c) IEEE Computer Society, IEEE International Workshop on the Future of PaaS
(PaaS2014), 2014 IEEE International Conference on Cloud Engineering (IC2E 2014), Boston, Massachusetts, USA, 10-14 March 201
ASE Summer 2014 42
43. Exercises
Read papers mentioned in slides
Get their main ideas
Check services mentioned in examples
Examine capabilities of the mentioned services
Including price models and underlying technologies
Examine their size and scale
Examine their ecosystems and dependencies
Work on possible categories of single service
units that are useful for your work
Some common service units with capabilities and
providers
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44. 44
Thanks for
your attention
Hong-Linh Truong
Distributed Systems Group
Vienna University of Technology
truong@dsg.tuwien.ac.at
http://dsg.tuwien.ac.at/staff/truong
ASE Summer 2014