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Resource Management in Virtualization-based
Data Centers
Resource Management in Virtualization-based
Data Centers
Bhuvan UrgaonkarComputer Systems
LaboratoryPennsylvania State University
Bhuvan UrgaonkarComputer Systems
LaboratoryPennsylvania State University
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Data CenterData Center• Cluster of compute and storage servers connected by
high-speed network• Rent out resources in return for revenue
• Internet applications, Scientific applications, …• Revenue scheme expressed using SLAs
• Cluster of compute and storage servers connected by high-speed network
• Rent out resources in return for revenue• Internet applications, Scientific applications, …• Revenue scheme expressed using SLAs
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Resource Management in Data Centers
Resource Management in Data Centers
• Goal: Meet application SLAs• Easy solution: Over-provision resources
• Over-provisioning can be very wasteful• Energy, management, failures, …
• Data center would like to maximize revenue!• Dynamic capacity provisioning: match resource
allocations to varying workloads• Challenges:
• Determining changing resource needs of applications• Effective sharing of resources among applications
• E.g., server consolidation can reduce cost• Automating resource management
• Goal: Meet application SLAs• Easy solution: Over-provision resources
• Over-provisioning can be very wasteful• Energy, management, failures, …
• Data center would like to maximize revenue!• Dynamic capacity provisioning: match resource
allocations to varying workloads• Challenges:
• Determining changing resource needs of applications• Effective sharing of resources among applications
• E.g., server consolidation can reduce cost• Automating resource management
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Resource Management in Data Centers
Resource Management in Data Centers
• Goal: Meet application SLAs• Easy solution: Over-provision resources
• Over-provisioning can be very wasteful• Energy, management, failures, …
• Data center would like to maximize revenue!• Dynamic capacity provisioning: match resource
allocations to varying workloads• Challenges:
• Determining changing resource needs of applications• Effective sharing of resources among applications
• E.g., server consolidation can reduce cost• Automating resource management
• Goal: Meet application SLAs• Easy solution: Over-provision resources
• Over-provisioning can be very wasteful• Energy, management, failures, …
• Data center would like to maximize revenue!• Dynamic capacity provisioning: match resource
allocations to varying workloads• Challenges:
• Determining changing resource needs of applications• Effective sharing of resources among applications
• E.g., server consolidation can reduce cost• Automating resource management
![Page 5: Resource Management in Virtualization-based Data Centers Bhuvan Urgaonkar Computer Systems Laboratory Pennsylvania State University Bhuvan Urgaonkar Computer](https://reader037.vdocuments.us/reader037/viewer/2022110400/56649db45503460f94aa4399/html5/thumbnails/5.jpg)
Motivation for Virtualized Hosting in
Data Centers
Motivation for Virtualized Hosting in
Data Centers• Key idea: Design data center using virtualization
• Virtual machine monitor (VMM) and virtual machine (VM)• A software layer that runs on a server and allows multiple
OS/applications to co-exist• Each OS/application is given the illusion of its own “virtual”
machine that it has to itself• Why is this good?
• Consolidation of diverse OS/apps possible• Migration made easier• Small code of VMM => improved security
• Not a new idea, but existing solutions are inadequate• Goal: Devise efficient resource management solutions for a
virtualization-based data center
• Key idea: Design data center using virtualization• Virtual machine monitor (VMM) and virtual machine (VM)
• A software layer that runs on a server and allows multiple OS/applications to co-exist
• Each OS/application is given the illusion of its own “virtual” machine that it has to itself
• Why is this good?• Consolidation of diverse OS/apps possible• Migration made easier• Small code of VMM => improved security
• Not a new idea, but existing solutions are inadequate• Goal: Devise efficient resource management solutions for a
virtualization-based data center
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The Xen Virtual Machine Monitor
The Xen Virtual Machine Monitor
• VMM = hypervisor• VM = domain• Para-virtualization• Special domain called Dom0
• VMM = hypervisor• VM = domain• Para-virtualization• Special domain called Dom0
Xen hypervisorHardware
Linux’Windows’
Mysql database
Apache Web server
Dom2Dom1Dom0
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OutlineOutline
• Introduction and Motivation• Resource Management in a Xen-based Data
Center• Resource Accounting• Resource Allocation and Scheduling
• Performance Optimizations for Xen• Other Research• Concluding Remarks
• Introduction and Motivation• Resource Management in a Xen-based Data
Center• Resource Accounting• Resource Allocation and Scheduling
• Performance Optimizations for Xen• Other Research• Concluding Remarks
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Xen-based Data CenterXen-based Data Center• Each application component runs within a Xen
domain• Each application component runs within a Xen
domain
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql
Physical machine # 1 Physical machine # 2
Quake 1 Quake 2
Online book-store Online game server
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Resource Usage Accounting
Resource Usage Accounting
• Need for accurate resource accounting• Estimate future needs• Relate performance and resource consumption• Charge applications for resource usage
• Accounting in Xen-based hosting• Statistics for each DomU can be gathered by hypervisor
• E.g., number of bytes sent by a DomU• Hidden activity: CPU activity performed by Dom0
• Similar to activity done by a kernel for a process
• Techniques to de-multiplex Dom0’s activity across DomUs• How much work does Dom0 have to do for each DomU?
• Need for accurate resource accounting• Estimate future needs• Relate performance and resource consumption• Charge applications for resource usage
• Accounting in Xen-based hosting• Statistics for each DomU can be gathered by hypervisor
• E.g., number of bytes sent by a DomU• Hidden activity: CPU activity performed by Dom0
• Similar to activity done by a kernel for a process
• Techniques to de-multiplex Dom0’s activity across DomUs• How much work does Dom0 have to do for each DomU?
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Resource AllocationResource Allocation
• Multi-time scale resource allocation• Server assignment: course time-scale• Scheduling: fine time-scale
• Placement• Like a knapsack problem• What time-scale?
• Migration versus replication
• Multi-time scale resource allocation• Server assignment: course time-scale• Scheduling: fine time-scale
• Placement• Like a knapsack problem• What time-scale?
• Migration versus replication
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Intelligent Scheduling of Distributed ApplicationsIntelligent Scheduling of Distributed Applications
• Motivation: Co-scheduling of parallel applications• Schedule distributed communicating components together
• Motivation: Co-scheduling of parallel applications• Schedule distributed communicating components together
Physical machine # 1 Physical machine # 2
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Intelligent Scheduling of Distributed ApplicationsIntelligent Scheduling of Distributed Applications
Physical machine # 1 Physical machine # 2
• Motivation: Co-scheduling of parallel applications• Schedule distributed communicating components together
• Motivation: Co-scheduling of parallel applications• Schedule distributed communicating components together
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Intelligent Scheduling of Distributed ApplicationsIntelligent Scheduling of Distributed Applications
Physical machine # 1 Physical machine # 2
• Motivation: Co-scheduling of parallel applications• Schedule distributed communicating components together
• Motivation: Co-scheduling of parallel applications• Schedule distributed communicating components together
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Intelligent Scheduling of Distributed ApplicationsIntelligent Scheduling of Distributed Applications
Physical machine # 1 Physical machine # 2
Message waits tillyellow app gets the CPU
• Motivation: Co-scheduling of parallel applications• Schedule distributed communicating components together
• Motivation: Co-scheduling of parallel applications• Schedule distributed communicating components together
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Intelligent Scheduling of Distributed ApplicationsIntelligent Scheduling of Distributed Applications
Physical machine # 1 Physical machine # 2
• Motivation: Co-scheduling of parallel applications• Schedule distributed communicating components together
• Motivation: Co-scheduling of parallel applications• Schedule distributed communicating components together
Message can be receivedImmediately if theyellow app gets the CPU
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Intelligent Scheduling of Distributed ApplicationsIntelligent Scheduling of Distributed Applications
Physical machine # 1 Physical machine # 2
• Motivation: Co-scheduling of parallel applications• Schedule distributed communicating components together
• Motivation: Co-scheduling of parallel applications• Schedule distributed communicating components together
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Co-ordinated Schedulingof Communicating
Domains
Co-ordinated Schedulingof Communicating
Domains• Idea #1: Preferentially schedule a DomU
when it receives data• Modify Xen CPU scheduler to give higher
preference to receiving DomU
• Important: Also need to ensure that Dom0 gets to run to take care of I/O• Scheduler should partition the CPU allocation for
a DomU into those for Dom0 and DomU appropriately
• Idea #1: Preferentially schedule a DomU when it receives data• Modify Xen CPU scheduler to give higher
preference to receiving DomU
• Important: Also need to ensure that Dom0 gets to run to take care of I/O• Scheduler should partition the CPU allocation for
a DomU into those for Dom0 and DomU appropriately
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Co-ordinated Schedulingof Communicating
Domains
Co-ordinated Schedulingof Communicating
Domains• Idea #2: Try to schedule a sender DomU
when it is expected to receive the response • An application knows best, but mods undesirable• Let the hypervisor learn from past behavior
• E.g., query responses might be returning in 1-2 seconds
• Idea #3: Anticipatory CPU scheduling• If a domain has sent/received data, it may be likely
to do that again• E.g., queries may be issued in bursts
• Trade-off between domain context switch and how much extra time you let a sender DomU continue
• Idea #2: Try to schedule a sender DomU when it is expected to receive the response • An application knows best, but mods undesirable• Let the hypervisor learn from past behavior
• E.g., query responses might be returning in 1-2 seconds
• Idea #3: Anticipatory CPU scheduling• If a domain has sent/received data, it may be likely
to do that again• E.g., queries may be issued in bursts
• Trade-off between domain context switch and how much extra time you let a sender DomU continue
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Multi-processor Scheduling
Multi-processor Scheduling
• Idea: Dom0 should be scheduled together with a DomU doing I/O• Utilize the multiple CPUs to “co-schedule” a
communicating DomU with Dom0
• Ensure domains that communicate a lot do not starve others• Relaxed fairness: 50% CPU over intervals > 1
second• Approach: Decay the CPU priority of communicating
DomUs to ensure relaxed fairness is not violated
• Idea: Dom0 should be scheduled together with a DomU doing I/O• Utilize the multiple CPUs to “co-schedule” a
communicating DomU with Dom0
• Ensure domains that communicate a lot do not starve others• Relaxed fairness: 50% CPU over intervals > 1
second• Approach: Decay the CPU priority of communicating
DomUs to ensure relaxed fairness is not violated
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OutlineOutline
• Introduction and Motivation• Resource Management in a Xen-based Data
Center• Resource Accounting• Resource Allocation and Scheduling
• Performance Optimizations for Xen• Other Research• Concluding Remarks
• Introduction and Motivation• Resource Management in a Xen-based Data
Center• Resource Accounting• Resource Allocation and Scheduling
• Performance Optimizations for Xen• Other Research• Concluding Remarks
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Performance Optimizations for Xen
Performance Optimizations for Xen
• Switching between native & virtual hosting• Dynamic merging and splitting of domains• Overbooking of memory• Improved migration techniques• Coalesce network packets directed to the
same physical server
• Switching between native & virtual hosting• Dynamic merging and splitting of domains• Overbooking of memory• Improved migration techniques• Coalesce network packets directed to the
same physical server
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Performance Optimizations for Xen
Performance Optimizations for Xen
• Switching between native & virtual hosting• Dynamic merging and splitting of domains• Overbooking of memory• Improved migration techniques• Coalesce network packets directed to the
same physical server
• Switching between native & virtual hosting• Dynamic merging and splitting of domains• Overbooking of memory• Improved migration techniques• Coalesce network packets directed to the
same physical server
![Page 23: Resource Management in Virtualization-based Data Centers Bhuvan Urgaonkar Computer Systems Laboratory Pennsylvania State University Bhuvan Urgaonkar Computer](https://reader037.vdocuments.us/reader037/viewer/2022110400/56649db45503460f94aa4399/html5/thumbnails/23.jpg)
Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
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Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
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Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
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Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
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Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
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Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
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Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
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Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
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Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
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Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
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Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
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Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
• (-) Increased CPU processing for coalescing and splitting packets
• (+) Reduced interrupt processing at receiver
• (-) Increased CPU processing for coalescing and splitting packets
• (+) Reduced interrupt processing at receiver
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Optimizing Network Communication
Optimizing Network Communication
• What kinds of packets can be coalesced?• TCP ACKs? Other packets?
• Would it make sense to do anticipatory packet scheduling at the sender?
• What kinds of packets can be coalesced?• TCP ACKs? Other packets?
• Would it make sense to do anticipatory packet scheduling at the sender?
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
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OutlineOutline
• Introduction and Motivation• Resource Management in a Xen-based Data
Center• Resource Accounting• Resource Allocation and Scheduling
• Performance Optimizations for Xen• Other Research• Concluding Remarks
• Introduction and Motivation• Resource Management in a Xen-based Data
Center• Resource Accounting• Resource Allocation and Scheduling
• Performance Optimizations for Xen• Other Research• Concluding Remarks
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Provisioning a Directional Antenna-
based Network
Provisioning a Directional Antenna-
based Network • Directional antennas• Longer reach• Less interference => Increased capacity
• Directional antennas• Longer reach• Less interference => Increased capacity
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Provisioning a Directional Antenna-
based Network
Provisioning a Directional Antenna-
based Network • Theoretical results• User-centric version
• Fair bandwidth allocation• Optimal algorithm based on dynamic
programming• Provider-centric version
• Maximize revenue• NP-hard, 2-approximation algorithm
• Ongoing work• Heuristics to incorporate mobility• Evaluation through simulation• Implementation … may be
• Theoretical results• User-centric version
• Fair bandwidth allocation• Optimal algorithm based on dynamic
programming• Provider-centric version
• Maximize revenue• NP-hard, 2-approximation algorithm
• Ongoing work• Heuristics to incorporate mobility• Evaluation through simulation• Implementation … may be
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Concluding RemarksConcluding Remarks• Resource mgmt. in virtualized environments• Provisioning wireless networks• Energy optimization in sensor networks
• Distributed systems, Operating systems• Combination of analysis, algorithm design and
experimentation with prototypes
• Acknowledgements:• Faculty: Anand, Piotr, Wang-Chien• Students: Amitayu, Arjun, Ross, Shiva, Sriram
• Resource mgmt. in virtualized environments• Provisioning wireless networks• Energy optimization in sensor networks
• Distributed systems, Operating systems• Combination of analysis, algorithm design and
experimentation with prototypes
• Acknowledgements:• Faculty: Anand, Piotr, Wang-Chien• Students: Amitayu, Arjun, Ross, Shiva, Sriram