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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
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
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
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
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
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
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
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
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?
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
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
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
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
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
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
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
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
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
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
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
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
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
Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
Optimizing Network Communication
Optimizing Network Communication
Xen hypervisorHardware
Linux’Windows’
Mysql database Apache
Dom2Dom1Dom0
Xen hypervisorHardware
Linux’Windows’
Dom2Dom1Dom0
Mysql Quake 1 Quake 2
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
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
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
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
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
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
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