SlideShare ist ein Scribd-Unternehmen logo
1 von 26
Challenges in Cloud Computing – VM
Migration
Sarmad Makhdoom
2012-03-0019
Kamran Khalil
2012-03-0012
Hafeez ur Rehman
2012-03-0031
Introduction
Cloud Computing
•

Cloud computing is an umbrella term used to refer to Internet based
development and services

•

A number of characteristics define cloud data, applications services and
infrastructure:
▫
▫
▫

Remotely hosted: Services or data are hosted on remote infrastructure.
Ubiquitous: Services or data are available anywhere.
Commodified: The result is a utility computing model similar to traditional
that of traditional utilities, like gas and electricity - you pay for what you
would want!
Virtual Machines
• A virtual machine provides interface identical to underlying bare
hardware
▫ i.e. all devices, interrupts, memory, page tables etc.

• Applications of Virtual Machines
• Virtualization Software
▫
▫
▫
▫

VMWare
ZAP
Xen
QEMU
Virtual Machines in Cloud
▫ Benefits of Virtual Machines







Virtualization help making efficient use of hardware resources
Facilitates a greater degree of abstraction
Easily move from one piece of hardware to another
Replicate them at will
Create more scalable and flexible infrastructure
Snapshots

▫ Cloud computing has taken that degree of efficiency and agility realized
from virtualization
 Pooled resources
 Geographic diversity
 Universal connectivity
Research Problems
Research Problems
• Automated services provisioning

• Virtual machine migration
•
•
•
•
•
•
•

Server consolidation
Energy management
Traffic management and analysis
Data security
Software frameworks
Storage technology and data management
Novel cloud architecture
Motivation
Motivation
• Consider a data center consisting of “n” physical machines (PM) hosting
“m” VMs implementing one customer application each
• Resources(CPU, Network, Memory, I/O) are allocated to each VM to
handle the workload and operate at certain performance level (SLA)
• Each VM sees workload fluctuation from time to time => resource
requirement changes
# of user visit increases
PM Capacity
VM1

VM2

cricinfo

Network Bandwidth
Memory
CPU

mail server

Virtualization Layer
Hardware

Resource
Allocation

VM1
N = 5Gbps
M = 8GB
C = 4 cores

= 10 Gbps
= 16 GB
= 8 cores
VM2
N = 5Gbps
M = 8GB
C = 4 cores
Motivation
• An increase in workload can be handled by allocating more resources to
it, if idle resources are available
• Main Issues:
▫ What if PM does not have (enough or no) idle resources to satisfy VM's
requirement?
 Performance of the application degrades
 SLA violation occurs

• Key Ideas
▫ Replication VMs
▫ Migrating VMs
Virtual Machine Migration
It is impossible or impractical to bring the data (or devices) close to the computation
engines.
Virtual Machine Migration
• Why we need migration?
• When we need to migrate?
• How migration is done?
• Issues in long distance migration (across data centers)
When we need to migrate? [NSDI’ 07]
• Hotspots can cause SLA violations
▫ Burden on some Virtual or Physical Machines are called hotspots

• Hotspot Detection (Sandpiper)
▫ Black-box Monitoring
 CPU (/proc)
 Network (/proc/net/dev)
 Memory (swap)

▫ Gray-box Monitoring
 Gather OS level statistics and application logs

• A hotspot is flagged only if thresholds or SLAs are exceeded for a
sustained time
Wood T et al (2007) Black-box and gray-box strategies for virtual machine migration. In: Proc of NSDI
Gathering
resource usage
statistics
Gathers on that
server
processor, network

Determine:
What virtual servers should migrate
Monitors usage profiles to detect
Where to move them
hotspots.
Construct resource much any resource exceeds a the
How
Hotspot: of a resource to allocate
usage profiles forvirtual servers afterviolation) for a sustain
threshold(or SLA migration
each virtual server period
(Predict PM workload)

and memory swap
statistics
for each VM Implements a
daemon to gather
OS-level statistics
and application logs

Wood T et al (2007) Black-box and gray-box strategies for virtual machine migration. In: Proc of NSDI

14
When we need to migrate? [FGCS’ 12]
• SLA violation detection
▫ Mapping low-level resource metrics to high-level SLAs
▫ Crude data maps to user requirements such as
 CPU speed maps to Response Time
 Occupied memory size maps to number of concurrent clients

▫ Predictive Strategy for detection of possible SLA violations
▫ Detection interval
 Short measurement intervals may degrade performance
 Long measurement intervals may cause ignorance of heavy SLA violations

Towards autonomic detection of SLA violations in Cloud infrastructures, Future Generation Computer Systems, 2012
How migration is done?
• Memory Migration
▫ Pre-copy
 Push phase
 Stop-and-copy phase
 Pull Phase

▫ Pure demand-migration

• File System Migration
▫ In case of distributed file system, there is no need to copy
▫ Alternatively, copy only changed local files to the destination using Virtual
Machine Manager’s API.

S. Venkatesha, S. Sadhu, S. Kintali, and S. Barbara, "Survey of virtual machine migration techniques" - Memory, 2009
How migration is done?
• Network Migration
▫ If both source and destination are on same LAN switch
 an unsolicited ARP reply from the migrating host is provided

▫ Alternatively, on a switched network
 the migrating OS can keep its original Ethernet MAC address, relying on the
network switch to detect its move to a new port

• Device Migration
▫ Three type of device support
 Emulation
 Virtualization
 Non-migratable

S. Venkatesha, S. Sadhu, S. Kintali, and S. Barbara, "Survey of virtual machine migration techniques" - Memory, 2009
Migration: A Performance Evaluation
• Testbed specification
▫
▫
▫
▫
▫

6 Servers (1 head node, 5 VM hosts)
Intel Xeon (2.33 GHz Quad-core with 2x6MB L2 Cache)
4GB memory and 7200rpm hard drive
64-bit Ubuntu Linux 8.04 Server Edition
Apache 2.2.8 and MySQL 5.2.4-2

• Workload
▫ Olio as a Web 2.0 application (http://incubator.apache.org/olio/)
▫ Faban Load generator (http://faban.sunsource.net)

• Experiments
▫ 10 minute and 20 minute benchmark runs with 600 concurrent users
Cost of virtual machine live migration in clouds: A performance evaluation, International Conference on Cloud Computing, 2009
Migration: A Performance Evaluation

Cost of virtual machine live migration in clouds: A performance evaluation, International Conference on Cloud Computing, 2009
Migration across data centers
• Need for VM mobility across data centers
▫
▫
▫
▫

Data center maintenance without downtime
Disaster avoidance
Data center migration/expansion
Workload balancing across multiple sites

• Issues
▫
▫
▫
▫

RTT is 1,000 times greater than sub-networks, ‘word wide wait’
Trust to a remote execution environment
Interoperability at the level of Web Services, Java etc
Migration across multiple domains are vulnerable to security exploits
Related Work: Migration over MAN/WAN
[FGCS’06]
• Introduced an intermediate traffic controller to facilitate migration
which consists of:
▫ VM Traffic Controller
 Provisioning of network resources and the re-provisioning of the IP tunnel to
ensure seamless layer

▫ AAA (Authentication, Authorization and Accounting)
 Pre-allocation of extra VM-resources required for migration

▫ DRAC (Dynamic Resource Allocation Controller)
 Exposes a service-oriented API for coupling with applications

▫ Preservation of TCP and higher-level sessions
 Dynamically configured IP tunnels allow client connectivity

“Seamless Live Migration of Virtual Machines over the MAN/WAN”, Elsevier Future Generation Computer Systems 2006
Related Work: HP Cluster Extension and
Microsoft Hyper-V™
• HP Cluster Extension (CLX) provides flawless mirroring capabilities for
disaster recovery
• VM data is already replicated in single data center cluster
▫ Modifications to spread it across multi-site:
 Multi-site Disaster Recovery solution is implemented
 CLX enables Hyper-V Live Migration across sites

(VMware, Hyper-V, HP-VM and AMD-V)
Our limitation is non-availability of peer referenced or published
material of these architectures to discuss their techniques in detail
Proposed Solution
• Security
▫ Layer 2 Link Encryption (IEEE 802.1AE) may be used to help ensure privacy
and confidentially
▫ Token based security - Authorization message sequence to thwart resource
theft

• 1,000 times rise in RTT doesn’t matter much because
▫ All states will be transferred before the switch
▫ Downtime will be based on the time required in redirection
 Which is only 5-10 times than the intra-LAN setup
Conclusion
Conclusion
• Virtual machine migration provide significant benefits in cloud
computing
• State of the art work is already done on resource monitoring, live VM
migration over the LAN and MAN/WAN
• Downtime is 60ms to 3s which they assume is negligible
• Small transient spikes does not trigger needless migrations, if threshold
or SLAs are exceeded for sustained time
• A slowdown is expected due to cache warm-up at the destination after
migration
• Hybrid approach has been suggested to harness the benefits of all
technqiues
Challenges in Cloud Computing – VM Migration

Weitere ähnliche Inhalte

Was ist angesagt?

Load balancing
Load balancingLoad balancing
Load balancingSoujanya V
 
Mobile Computing UNIT-6
Mobile Computing UNIT-6Mobile Computing UNIT-6
Mobile Computing UNIT-6Ramesh Babu
 
Scheduling in Cloud Computing
Scheduling in Cloud ComputingScheduling in Cloud Computing
Scheduling in Cloud ComputingHitesh Mohapatra
 
Mobile transport layer - traditional TCP
Mobile transport layer - traditional TCPMobile transport layer - traditional TCP
Mobile transport layer - traditional TCPVishal Tandel
 
Federated Cloud Computing - The OpenNebula Experience v1.0s
Federated Cloud Computing  - The OpenNebula Experience v1.0sFederated Cloud Computing  - The OpenNebula Experience v1.0s
Federated Cloud Computing - The OpenNebula Experience v1.0sIgnacio M. Llorente
 
Eucalyptus, Nimbus & OpenNebula
Eucalyptus, Nimbus & OpenNebulaEucalyptus, Nimbus & OpenNebula
Eucalyptus, Nimbus & OpenNebulaAmar Myana
 
cloud computing:Types of virtualization
cloud computing:Types of virtualizationcloud computing:Types of virtualization
cloud computing:Types of virtualizationDr.Neeraj Kumar Pandey
 
VIRTUALIZATION STRUCTURES TOOLS.docx
VIRTUALIZATION STRUCTURES TOOLS.docxVIRTUALIZATION STRUCTURES TOOLS.docx
VIRTUALIZATION STRUCTURES TOOLS.docxkumari36
 
Levels of Virtualization.docx
Levels of Virtualization.docxLevels of Virtualization.docx
Levels of Virtualization.docxkumari36
 
Io t system management with
Io t system management withIo t system management with
Io t system management withxyxz
 
ENCAPSULATION AND TUNNELING
ENCAPSULATION AND TUNNELINGENCAPSULATION AND TUNNELING
ENCAPSULATION AND TUNNELINGMohammad Adil
 
IEEE 802.11 Architecture and Services
IEEE 802.11 Architecture and ServicesIEEE 802.11 Architecture and Services
IEEE 802.11 Architecture and ServicesSayed Chhattan Shah
 
Open Cloud Consortium Overview (01-10-10 V6)
Open Cloud Consortium Overview (01-10-10 V6)Open Cloud Consortium Overview (01-10-10 V6)
Open Cloud Consortium Overview (01-10-10 V6)Robert Grossman
 

Was ist angesagt? (20)

Introduction to CloudStack
Introduction to CloudStack Introduction to CloudStack
Introduction to CloudStack
 
Cloud computing architectures
Cloud computing architecturesCloud computing architectures
Cloud computing architectures
 
Load balancing
Load balancingLoad balancing
Load balancing
 
Mobile Computing UNIT-6
Mobile Computing UNIT-6Mobile Computing UNIT-6
Mobile Computing UNIT-6
 
Comet Cloud
Comet CloudComet Cloud
Comet Cloud
 
Scheduling in Cloud Computing
Scheduling in Cloud ComputingScheduling in Cloud Computing
Scheduling in Cloud Computing
 
Mobile transport layer - traditional TCP
Mobile transport layer - traditional TCPMobile transport layer - traditional TCP
Mobile transport layer - traditional TCP
 
Federated Cloud Computing - The OpenNebula Experience v1.0s
Federated Cloud Computing  - The OpenNebula Experience v1.0sFederated Cloud Computing  - The OpenNebula Experience v1.0s
Federated Cloud Computing - The OpenNebula Experience v1.0s
 
Mobile Ad hoc network
Mobile Ad hoc networkMobile Ad hoc network
Mobile Ad hoc network
 
Eucalyptus, Nimbus & OpenNebula
Eucalyptus, Nimbus & OpenNebulaEucalyptus, Nimbus & OpenNebula
Eucalyptus, Nimbus & OpenNebula
 
Distributed information system
Distributed information systemDistributed information system
Distributed information system
 
cloud computing:Types of virtualization
cloud computing:Types of virtualizationcloud computing:Types of virtualization
cloud computing:Types of virtualization
 
Routing Protocols in WSN
Routing Protocols in WSNRouting Protocols in WSN
Routing Protocols in WSN
 
VIRTUALIZATION STRUCTURES TOOLS.docx
VIRTUALIZATION STRUCTURES TOOLS.docxVIRTUALIZATION STRUCTURES TOOLS.docx
VIRTUALIZATION STRUCTURES TOOLS.docx
 
Levels of Virtualization.docx
Levels of Virtualization.docxLevels of Virtualization.docx
Levels of Virtualization.docx
 
Io t system management with
Io t system management withIo t system management with
Io t system management with
 
ENCAPSULATION AND TUNNELING
ENCAPSULATION AND TUNNELINGENCAPSULATION AND TUNNELING
ENCAPSULATION AND TUNNELING
 
IEEE 802.11 Architecture and Services
IEEE 802.11 Architecture and ServicesIEEE 802.11 Architecture and Services
IEEE 802.11 Architecture and Services
 
Open Cloud Consortium Overview (01-10-10 V6)
Open Cloud Consortium Overview (01-10-10 V6)Open Cloud Consortium Overview (01-10-10 V6)
Open Cloud Consortium Overview (01-10-10 V6)
 
Trends in distributed systems
Trends in distributed systemsTrends in distributed systems
Trends in distributed systems
 

Ähnlich wie Challenges in Cloud Computing – VM Migration

Zero Downtime JEE Architectures
Zero Downtime JEE ArchitecturesZero Downtime JEE Architectures
Zero Downtime JEE ArchitecturesAlexander Penev
 
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Enviro...
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Enviro...Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Enviro...
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Enviro...SaikiranReddy Sama
 
Cloud Computing
Cloud ComputingCloud Computing
Cloud Computingchrismik
 
A Complete Guide Cloud Computing
A Complete Guide Cloud ComputingA Complete Guide Cloud Computing
A Complete Guide Cloud ComputingSripati Mahapatra
 
AUTOMATED VM MIGRATION USING INTELLIGENT LEARNING TECHNIQUE
AUTOMATED VM MIGRATION USING INTELLIGENT LEARNING TECHNIQUEAUTOMATED VM MIGRATION USING INTELLIGENT LEARNING TECHNIQUE
AUTOMATED VM MIGRATION USING INTELLIGENT LEARNING TECHNIQUEIRJET Journal
 
Virtual Machine Migration Techniques in Cloud Environment: A Survey
Virtual Machine Migration Techniques in Cloud Environment: A SurveyVirtual Machine Migration Techniques in Cloud Environment: A Survey
Virtual Machine Migration Techniques in Cloud Environment: A Surveyijsrd.com
 
Caching for Microservices Architectures: Session I
Caching for Microservices Architectures: Session ICaching for Microservices Architectures: Session I
Caching for Microservices Architectures: Session IVMware Tanzu
 
Lisa Guess - Embracing the Cloud
Lisa Guess - Embracing the CloudLisa Guess - Embracing the Cloud
Lisa Guess - Embracing the Cloudcentralohioissa
 
Virtualization for competitive advantage - Eric Vanderburg
Virtualization for competitive advantage - Eric VanderburgVirtualization for competitive advantage - Eric Vanderburg
Virtualization for competitive advantage - Eric VanderburgEric Vanderburg
 
Cloud computing and its job opportunities
Cloud computing and its job opportunities Cloud computing and its job opportunities
Cloud computing and its job opportunities Ramya SK
 
Optimising Service Deployment and Infrastructure Resource Configuration
Optimising Service Deployment and Infrastructure Resource ConfigurationOptimising Service Deployment and Infrastructure Resource Configuration
Optimising Service Deployment and Infrastructure Resource ConfigurationRECAP Project
 
Fog Computing Platform
Fog Computing PlatformFog Computing Platform
Fog Computing Platform霈萱 蔡
 
Cloud Bursting 101: What to do When Cloud Computing Demand Exceeds Capacity
Cloud Bursting 101: What to do When Cloud Computing Demand Exceeds CapacityCloud Bursting 101: What to do When Cloud Computing Demand Exceeds Capacity
Cloud Bursting 101: What to do When Cloud Computing Demand Exceeds CapacityAvere Systems
 
Big Data on Cloud Native Platform
Big Data on Cloud Native PlatformBig Data on Cloud Native Platform
Big Data on Cloud Native PlatformSunil Govindan
 
Big Data on Cloud Native Platform
Big Data on Cloud Native PlatformBig Data on Cloud Native Platform
Big Data on Cloud Native PlatformSunil Govindan
 

Ähnlich wie Challenges in Cloud Computing – VM Migration (20)

Brad stack - Digital Health and Well-Being Festival
Brad stack - Digital Health and Well-Being Festival Brad stack - Digital Health and Well-Being Festival
Brad stack - Digital Health and Well-Being Festival
 
Zero Downtime JEE Architectures
Zero Downtime JEE ArchitecturesZero Downtime JEE Architectures
Zero Downtime JEE Architectures
 
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Enviro...
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Enviro...Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Enviro...
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Enviro...
 
Cloud Computing
Cloud ComputingCloud Computing
Cloud Computing
 
A Complete Guide Cloud Computing
A Complete Guide Cloud ComputingA Complete Guide Cloud Computing
A Complete Guide Cloud Computing
 
AUTOMATED VM MIGRATION USING INTELLIGENT LEARNING TECHNIQUE
AUTOMATED VM MIGRATION USING INTELLIGENT LEARNING TECHNIQUEAUTOMATED VM MIGRATION USING INTELLIGENT LEARNING TECHNIQUE
AUTOMATED VM MIGRATION USING INTELLIGENT LEARNING TECHNIQUE
 
Virtual Machine Migration Techniques in Cloud Environment: A Survey
Virtual Machine Migration Techniques in Cloud Environment: A SurveyVirtual Machine Migration Techniques in Cloud Environment: A Survey
Virtual Machine Migration Techniques in Cloud Environment: A Survey
 
Caching for Microservices Architectures: Session I
Caching for Microservices Architectures: Session ICaching for Microservices Architectures: Session I
Caching for Microservices Architectures: Session I
 
Cloud computing_Final
Cloud computing_FinalCloud computing_Final
Cloud computing_Final
 
Lisa Guess - Embracing the Cloud
Lisa Guess - Embracing the CloudLisa Guess - Embracing the Cloud
Lisa Guess - Embracing the Cloud
 
Virtualization for competitive advantage - Eric Vanderburg
Virtualization for competitive advantage - Eric VanderburgVirtualization for competitive advantage - Eric Vanderburg
Virtualization for competitive advantage - Eric Vanderburg
 
Could the “C” in HPC stand for Cloud?
Could the “C” in HPC stand for Cloud?Could the “C” in HPC stand for Cloud?
Could the “C” in HPC stand for Cloud?
 
Cloud computing
Cloud computingCloud computing
Cloud computing
 
Cloud ppt
Cloud pptCloud ppt
Cloud ppt
 
Cloud computing and its job opportunities
Cloud computing and its job opportunities Cloud computing and its job opportunities
Cloud computing and its job opportunities
 
Optimising Service Deployment and Infrastructure Resource Configuration
Optimising Service Deployment and Infrastructure Resource ConfigurationOptimising Service Deployment and Infrastructure Resource Configuration
Optimising Service Deployment and Infrastructure Resource Configuration
 
Fog Computing Platform
Fog Computing PlatformFog Computing Platform
Fog Computing Platform
 
Cloud Bursting 101: What to do When Cloud Computing Demand Exceeds Capacity
Cloud Bursting 101: What to do When Cloud Computing Demand Exceeds CapacityCloud Bursting 101: What to do When Cloud Computing Demand Exceeds Capacity
Cloud Bursting 101: What to do When Cloud Computing Demand Exceeds Capacity
 
Big Data on Cloud Native Platform
Big Data on Cloud Native PlatformBig Data on Cloud Native Platform
Big Data on Cloud Native Platform
 
Big Data on Cloud Native Platform
Big Data on Cloud Native PlatformBig Data on Cloud Native Platform
Big Data on Cloud Native Platform
 

Kürzlich hochgeladen

ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesrafiqahmad00786416
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsNanddeep Nachan
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontologyjohnbeverley2021
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfOrbitshub
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxRemote DBA Services
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdfSandro Moreira
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWERMadyBayot
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...Zilliz
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDropbox
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Angeliki Cooney
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamUiPathCommunity
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...apidays
 
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot ModelMcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot ModelDeepika Singh
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Jeffrey Haguewood
 

Kürzlich hochgeladen (20)

ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot ModelMcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
 

Challenges in Cloud Computing – VM Migration

  • 1. Challenges in Cloud Computing – VM Migration Sarmad Makhdoom 2012-03-0019 Kamran Khalil 2012-03-0012 Hafeez ur Rehman 2012-03-0031
  • 3. Cloud Computing • Cloud computing is an umbrella term used to refer to Internet based development and services • A number of characteristics define cloud data, applications services and infrastructure: ▫ ▫ ▫ Remotely hosted: Services or data are hosted on remote infrastructure. Ubiquitous: Services or data are available anywhere. Commodified: The result is a utility computing model similar to traditional that of traditional utilities, like gas and electricity - you pay for what you would want!
  • 4. Virtual Machines • A virtual machine provides interface identical to underlying bare hardware ▫ i.e. all devices, interrupts, memory, page tables etc. • Applications of Virtual Machines • Virtualization Software ▫ ▫ ▫ ▫ VMWare ZAP Xen QEMU
  • 5. Virtual Machines in Cloud ▫ Benefits of Virtual Machines       Virtualization help making efficient use of hardware resources Facilitates a greater degree of abstraction Easily move from one piece of hardware to another Replicate them at will Create more scalable and flexible infrastructure Snapshots ▫ Cloud computing has taken that degree of efficiency and agility realized from virtualization  Pooled resources  Geographic diversity  Universal connectivity
  • 7. Research Problems • Automated services provisioning • Virtual machine migration • • • • • • • Server consolidation Energy management Traffic management and analysis Data security Software frameworks Storage technology and data management Novel cloud architecture
  • 9. Motivation • Consider a data center consisting of “n” physical machines (PM) hosting “m” VMs implementing one customer application each • Resources(CPU, Network, Memory, I/O) are allocated to each VM to handle the workload and operate at certain performance level (SLA) • Each VM sees workload fluctuation from time to time => resource requirement changes # of user visit increases PM Capacity VM1 VM2 cricinfo Network Bandwidth Memory CPU mail server Virtualization Layer Hardware Resource Allocation VM1 N = 5Gbps M = 8GB C = 4 cores = 10 Gbps = 16 GB = 8 cores VM2 N = 5Gbps M = 8GB C = 4 cores
  • 10. Motivation • An increase in workload can be handled by allocating more resources to it, if idle resources are available • Main Issues: ▫ What if PM does not have (enough or no) idle resources to satisfy VM's requirement?  Performance of the application degrades  SLA violation occurs • Key Ideas ▫ Replication VMs ▫ Migrating VMs
  • 11. Virtual Machine Migration It is impossible or impractical to bring the data (or devices) close to the computation engines.
  • 12. Virtual Machine Migration • Why we need migration? • When we need to migrate? • How migration is done? • Issues in long distance migration (across data centers)
  • 13. When we need to migrate? [NSDI’ 07] • Hotspots can cause SLA violations ▫ Burden on some Virtual or Physical Machines are called hotspots • Hotspot Detection (Sandpiper) ▫ Black-box Monitoring  CPU (/proc)  Network (/proc/net/dev)  Memory (swap) ▫ Gray-box Monitoring  Gather OS level statistics and application logs • A hotspot is flagged only if thresholds or SLAs are exceeded for a sustained time Wood T et al (2007) Black-box and gray-box strategies for virtual machine migration. In: Proc of NSDI
  • 14. Gathering resource usage statistics Gathers on that server processor, network Determine: What virtual servers should migrate Monitors usage profiles to detect Where to move them hotspots. Construct resource much any resource exceeds a the How Hotspot: of a resource to allocate usage profiles forvirtual servers afterviolation) for a sustain threshold(or SLA migration each virtual server period (Predict PM workload) and memory swap statistics for each VM Implements a daemon to gather OS-level statistics and application logs Wood T et al (2007) Black-box and gray-box strategies for virtual machine migration. In: Proc of NSDI 14
  • 15. When we need to migrate? [FGCS’ 12] • SLA violation detection ▫ Mapping low-level resource metrics to high-level SLAs ▫ Crude data maps to user requirements such as  CPU speed maps to Response Time  Occupied memory size maps to number of concurrent clients ▫ Predictive Strategy for detection of possible SLA violations ▫ Detection interval  Short measurement intervals may degrade performance  Long measurement intervals may cause ignorance of heavy SLA violations Towards autonomic detection of SLA violations in Cloud infrastructures, Future Generation Computer Systems, 2012
  • 16. How migration is done? • Memory Migration ▫ Pre-copy  Push phase  Stop-and-copy phase  Pull Phase ▫ Pure demand-migration • File System Migration ▫ In case of distributed file system, there is no need to copy ▫ Alternatively, copy only changed local files to the destination using Virtual Machine Manager’s API. S. Venkatesha, S. Sadhu, S. Kintali, and S. Barbara, "Survey of virtual machine migration techniques" - Memory, 2009
  • 17. How migration is done? • Network Migration ▫ If both source and destination are on same LAN switch  an unsolicited ARP reply from the migrating host is provided ▫ Alternatively, on a switched network  the migrating OS can keep its original Ethernet MAC address, relying on the network switch to detect its move to a new port • Device Migration ▫ Three type of device support  Emulation  Virtualization  Non-migratable S. Venkatesha, S. Sadhu, S. Kintali, and S. Barbara, "Survey of virtual machine migration techniques" - Memory, 2009
  • 18. Migration: A Performance Evaluation • Testbed specification ▫ ▫ ▫ ▫ ▫ 6 Servers (1 head node, 5 VM hosts) Intel Xeon (2.33 GHz Quad-core with 2x6MB L2 Cache) 4GB memory and 7200rpm hard drive 64-bit Ubuntu Linux 8.04 Server Edition Apache 2.2.8 and MySQL 5.2.4-2 • Workload ▫ Olio as a Web 2.0 application (http://incubator.apache.org/olio/) ▫ Faban Load generator (http://faban.sunsource.net) • Experiments ▫ 10 minute and 20 minute benchmark runs with 600 concurrent users Cost of virtual machine live migration in clouds: A performance evaluation, International Conference on Cloud Computing, 2009
  • 19. Migration: A Performance Evaluation Cost of virtual machine live migration in clouds: A performance evaluation, International Conference on Cloud Computing, 2009
  • 20. Migration across data centers • Need for VM mobility across data centers ▫ ▫ ▫ ▫ Data center maintenance without downtime Disaster avoidance Data center migration/expansion Workload balancing across multiple sites • Issues ▫ ▫ ▫ ▫ RTT is 1,000 times greater than sub-networks, ‘word wide wait’ Trust to a remote execution environment Interoperability at the level of Web Services, Java etc Migration across multiple domains are vulnerable to security exploits
  • 21. Related Work: Migration over MAN/WAN [FGCS’06] • Introduced an intermediate traffic controller to facilitate migration which consists of: ▫ VM Traffic Controller  Provisioning of network resources and the re-provisioning of the IP tunnel to ensure seamless layer ▫ AAA (Authentication, Authorization and Accounting)  Pre-allocation of extra VM-resources required for migration ▫ DRAC (Dynamic Resource Allocation Controller)  Exposes a service-oriented API for coupling with applications ▫ Preservation of TCP and higher-level sessions  Dynamically configured IP tunnels allow client connectivity “Seamless Live Migration of Virtual Machines over the MAN/WAN”, Elsevier Future Generation Computer Systems 2006
  • 22. Related Work: HP Cluster Extension and Microsoft Hyper-V™ • HP Cluster Extension (CLX) provides flawless mirroring capabilities for disaster recovery • VM data is already replicated in single data center cluster ▫ Modifications to spread it across multi-site:  Multi-site Disaster Recovery solution is implemented  CLX enables Hyper-V Live Migration across sites (VMware, Hyper-V, HP-VM and AMD-V) Our limitation is non-availability of peer referenced or published material of these architectures to discuss their techniques in detail
  • 23. Proposed Solution • Security ▫ Layer 2 Link Encryption (IEEE 802.1AE) may be used to help ensure privacy and confidentially ▫ Token based security - Authorization message sequence to thwart resource theft • 1,000 times rise in RTT doesn’t matter much because ▫ All states will be transferred before the switch ▫ Downtime will be based on the time required in redirection  Which is only 5-10 times than the intra-LAN setup
  • 25. Conclusion • Virtual machine migration provide significant benefits in cloud computing • State of the art work is already done on resource monitoring, live VM migration over the LAN and MAN/WAN • Downtime is 60ms to 3s which they assume is negligible • Small transient spikes does not trigger needless migrations, if threshold or SLAs are exceeded for sustained time • A slowdown is expected due to cache warm-up at the destination after migration • Hybrid approach has been suggested to harness the benefits of all technqiues

Hinweis der Redaktion

  1. Applications of VMUse different OS’Software testing