a fine-grained performance-based decision model for ... · 21 experimental environment setup...
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A Fine-grained Performance-based
Decision Model for Virtualization
Application Solution
Jianhai ChenCollege of Computer Science
Zhejiang University
Hangzhou City, Zhejiang Province, China
2011/08/29
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Outline
Introduction
Architecture of VirtDM
Metrics choosing
VirtDM modeling
VirtDM formulation
VirtDM implementation
Case study
Conclusion
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1.Introduction
Datacenter virtualization problem
Virtualization Application Solution(VAS)
Decision of VAS for special virtualization application scenarios
Systematic Decision method——VirtDM
Virtualization performance evaluation
MCDM: Multi-Criteria Decision-Making
Human preference
Comparable results
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Datacenter virtualization problem
Application Virtualization
Presentation Virtualization
Desktop Virtualization
Storage Virtualization
Network Virtualization
Vmware
Xen
KVM
Hyper-V
VirtualBox……
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What metrics should be taken into account to measured a VAS?
How to quantify a datacenter architect’s preference on these metrics?
How to achieve an overall decision from different metrics’ results and architects’
preference?
Problems of VirtDM
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2. Architecture of VirtDM
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What is VAS• Refers to a specific
software and
hardware
implementation of
virtualization
technologies.
• Para-virtualization &
Full virtualization
• Hardware with Intel
VT or ADM SVM
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3. Metrics Choosing
Virtualization application solution
Virtualization Overhead
Manageability
Isolation
Consolidation
Live migration
……
Consolidation
CPU
MEMory
Network IO
Managability
Live MigrationCreate/delete
Suspend VM
Save/Restore
Migration
PerformanceScalability
Isolation
Metricssystem
Disk IO
Overhead
Resource Scalability
...
VCPU/vMem/...
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Four workloads used to measure
CPU task, memory task, I/O task and Context Switch task.
Performance degradation percentage is used to express the quantities of overhead
Virtualization Overhead
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Include 5 metrics
immeasurable metrics
VM resource scalability
Migration function
Consolidation functional scalability
Measure with response time
VM snapshot save/resume efficiency
VM start/shutdown efficiency
Manageability
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Isolation
run different stress tests - CPU bomb,
memory bomb, I/O bomb
cause extreme resource consumption and refer their VMs as bad VMs
measure the performance degradation of the normal workloads on a well-behaving VM.
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Consolidation
Uneasy to measure
A good method is to use benchmark tool: SPECvirt sc2010.
Scales the workloads on the System Under Test (SUT) until the SUT reaches its peak performance.
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Migration
Use Virt-LM benchmark
It provides the results of four metrics
downtime
total migration time
the amount of migrated data
migration overhead
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4. VirtDM Modeling
VirtDM Formulation
Is a formulation for the VAS Multi-Criteria Decision-Making problem
Main parts include: VAS candidates,Metrics,Decision-making Matrix
VirtDM Implementation
Metrics quantification
Metrics normalization with dimensionless method
Weight identification \& pairwise comparison method
AHP
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Hierarchical structure of VirtDM
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Problems
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Definitions
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Metrics: quantitative and qualitative
Normalization means:
vector normalization
linear scale transformation
(0-1) interval conversion
VirtDM implementation
Metrics quantification
Metrics normalization
Weight identification
Weight combination
Human Preference
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Suppose a virtualized datacenter deployed preferring I/O performance
Given and setup three VAS platforms environment for VirtDM implementation
The purpose is to make decision the best VAS candidate
Measuring the performance
Overall decision process
5. Case Study
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Experimental Environment Setup
1)VAS-XEN-HV
Physical host is a Dell PowerEdge T710, with dual quadcore Intel Xeon processor E5620 at 2.4GHZ and 24GB of memory.
VMM is Xen-3.3.1 with Linux Kernel 2.6.18.8-xen
2)VAS-XEN-PV
Using the same host and VMM as VAS-XEN-HV but with a para-virtualized VM.
3)VAS-KVM
Using the same host and VM as VAS-XEN-HV but with a different VMM — KVM.
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Hierarchical Metrics for decsion
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Performance Measurement
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Overall Decision Process
Give examples to demonstrate the usefulness of VirtDM
Performance data comes from measurement
Data is standardized and normalized
MCDM of VirtDM is used to get score for each VAS
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Normalize the metrics
Construct decision-making matrix
Constructing the decision-making matrix
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AHP method is appliedPairwise comparison matrix
Identifying weights based on preference
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Combining weights
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7. CONCLUSIONS AND FUTURE WORK
Design and implement the VirtDM model to serve the VAS decision making in a datacenter
Provide a fine-grained, in-depth, and human friendly metrics system to cover essential performance characteristics of a VAS
Many aspects of VirtDM are far from satisfying:
metrics system are to be improved
other MCDM methods excludes AHP method
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Thank You!