Resource Management in Virtualization-based Data Centers

1
Resource Management in Virtualization-based
Data Centers

• Bhuvan Urgaonkar
• Computer Systems Laboratory
• Pennsylvania State University

2
Data 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

3
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

4
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

5
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 gt improved security
• Not a new idea, but existing solutions are
  inadequate
• Goal Devise efficient resource management
  solutions for a virtualization-based data center

6
The Xen Virtual Machine Monitor

• VMM hypervisor
• VM domain
• Para-virtualization
• Special domain called Dom0

Dom0
Dom2
Dom1
Mysql database
Apache Web server
Linux
Windows
Xen hypervisor
Hardware
7
Outline

• 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

8
Xen-based Data Center

• Each application component runs within a Xen
  domain

Online book-store
Online game server
Dom0
Dom0
Dom2
Dom1
Dom2
Dom1
Mysql database
Apache
Mysql
Quake 1
Quake 2
Linux
Windows
Linux
Windows
Xen hypervisor
Xen hypervisor
Hardware
Hardware
Physical machine 1
Physical machine 2
9
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 Dom0s activity across
  DomUs
• How much work does Dom0 have to do for each DomU?

10
Resource 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

11
Intelligent Scheduling of Distributed Applications

• Motivation Co-scheduling of parallel
  applications
• Schedule distributed communicating components
  together

Physical machine 1
Physical machine 2
12
Intelligent Scheduling of Distributed Applications

• Motivation Co-scheduling of parallel
  applications
• Schedule distributed communicating components
  together

Physical machine 1
Physical machine 2
13
Intelligent Scheduling of Distributed Applications

• Motivation Co-scheduling of parallel
  applications
• Schedule distributed communicating components
  together

Physical machine 1
Physical machine 2
14
Intelligent Scheduling of Distributed Applications

• Motivation Co-scheduling of parallel
  applications
• Schedule distributed communicating components
  together

Message waits till yellow app gets the CPU
Physical machine 1
Physical machine 2
15
Intelligent Scheduling of Distributed Applications

• Motivation Co-scheduling of parallel
  applications
• Schedule distributed communicating components
  together

Message can be received Immediately if the yellow
app gets the CPU
Physical machine 1
Physical machine 2
16
Intelligent Scheduling of Distributed Applications

• Motivation Co-scheduling of parallel
  applications
• Schedule distributed communicating components
  together

Physical machine 1
Physical machine 2
17
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

18
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

19
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 gt 1
  second
• Approach Decay the CPU priority of communicating
  DomUs to ensure relaxed fairness is not violated

20
Outline

• 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

21
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

22
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

23
Optimizing Network Communication
Dom0
Dom0
Dom2
Dom1
Dom2
Dom1
Mysql database
Apache
Mysql
Quake 1
Quake 2
Linux
Windows
Linux
Windows
Xen hypervisor
Xen hypervisor
Hardware
Hardware
24
Optimizing Network Communication
Dom0
Dom0
Dom2
Dom1
Dom2
Dom1
Mysql database
Apache
Mysql
Quake 1
Quake 2
Linux
Windows
Linux
Windows
Xen hypervisor
Xen hypervisor
Hardware
Hardware
25
Optimizing Network Communication
Dom0
Dom0
Dom2
Dom1
Dom2
Dom1
Mysql database
Apache
Mysql
Quake 1
Quake 2
Linux
Windows
Linux
Windows
Xen hypervisor
Xen hypervisor
Hardware
Hardware
26
Optimizing Network Communication
Dom0
Dom0
Dom2
Dom1
Dom2
Dom1
Mysql database
Apache
Mysql
Quake 1
Quake 2
Linux
Windows
Linux
Windows
Xen hypervisor
Xen hypervisor
Hardware
Hardware
27
Optimizing Network Communication
Dom0
Dom0
Dom2
Dom1
Dom2
Dom1
Mysql database
Apache
Mysql
Quake 1
Quake 2
Linux
Windows
Linux
Windows
Xen hypervisor
Xen hypervisor
Hardware
Hardware
28
Optimizing Network Communication
Dom0
Dom0
Dom2
Dom1
Dom2
Dom1
Mysql database
Apache
Mysql
Quake 1
Quake 2
Linux
Windows
Linux
Windows
Xen hypervisor
Xen hypervisor
Hardware
Hardware
29
Optimizing Network Communication
Dom0
Dom0
Dom2
Dom1
Dom2
Dom1
Mysql database
Apache
Mysql
Quake 1
Quake 2
Linux
Windows
Linux
Windows
Xen hypervisor
Xen hypervisor
Hardware
Hardware
30
Optimizing Network Communication
Dom0
Dom0
Dom2
Dom1
Dom2
Dom1
Mysql database
Apache
Mysql
Quake 1
Quake 2
Linux
Windows
Linux
Windows
Xen hypervisor
Xen hypervisor
Hardware
Hardware
31
Optimizing Network Communication
Dom0
Dom0
Dom2
Dom1
Dom2
Dom1
Mysql database
Apache
Mysql
Quake 1
Quake 2
Linux
Windows
Linux
Windows
Xen hypervisor
Xen hypervisor
Hardware
Hardware
32
Optimizing Network Communication
Dom0
Dom0
Dom2
Dom1
Dom2
Dom1
Mysql database
Apache
Mysql
Quake 1
Quake 2
Linux
Windows
Linux
Windows
Xen hypervisor
Xen hypervisor
Hardware
Hardware
33
Optimizing Network Communication
Dom0
Dom0
Dom2
Dom1
Dom2
Dom1
Mysql database
Apache
Mysql
Quake 1
Quake 2
Linux
Windows
Linux
Windows
Xen hypervisor
Xen hypervisor
Hardware
Hardware
34
Optimizing Network Communication

• (-) Increased CPU processing for coalescing and
  splitting packets
• () Reduced interrupt processing at receiver

Dom0
Dom0
Dom2
Dom1
Dom2
Dom1
Mysql database
Apache
Mysql
Quake 1
Quake 2
Linux
Windows
Linux
Windows
Xen hypervisor
Xen hypervisor
Hardware
Hardware
35
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?

Dom0
Dom0
Dom2
Dom1
Dom2
Dom1
Mysql database
Apache
Mysql
Quake 1
Quake 2
Linux
Windows
Linux
Windows
Xen hypervisor
Xen hypervisor
Hardware
Hardware
36
Outline

• 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

37
Provisioning a Directional Antenna-based Network

• Directional antennas
• Longer reach
• Less interference gt Increased capacity

38
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

39
Concluding 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