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Live Migration(LM) Benchmark Research College of Computer Science Zhejiang University China.

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Presentation on theme: "Live Migration(LM) Benchmark Research College of Computer Science Zhejiang University China."— Presentation transcript:

1 Live Migration(LM) Benchmark Research College of Computer Science Zhejiang University China

2 Outline Background and Motives Virt-LM Benchmark Overview Further Issues and Possible Solutions Conclusion Our Possible Work under the Cloud WG

3 Background and Motives

4 Significance of Live Migration Concept: Migration: Move VM between different physical machines Live: Without disconnecting client or application (invisible) Relation to Cloud Computing and Data Centers: Cloud Infrastructures and data centers have to efficiently use their huge scales of hardware resources. Virtualization Technology provides two approaches: Server Consolidation Live Migration Roles in a Data Center: Flexibly remap hardware among VMs. Balance workload Save energy Enhance service availability and fault tolerance

5 Motives of the LM Benchmark Scale and frequency leads to a significant LM cost (TC): S(Scale): How many servers? Google: Estimated 200,000 to 500,000 servers, included in 36 data centers in 2008 MS: Added 10,000 servers per month in 2008 FaceBook: More than 30,000 servers in its data center in 2008 F(Frequency):How often it happens? Load balancing Online maintainance and proactive fault tolerance Power management C(Cost of Live Migration): Hardware and network bandwidth save and transfer VM state Workload performance: share hardware Service availability: downtime

6 Motives of the LM Benchmark A LM benchmark is in need. LM benchmark helps make right decisions to reduce cost Design better LM strategies Choose better platform Evaluation of a data center should include its LM performance VMware released VMmark 2.0 for multi-server performance in DEC, 2010 Existing evaluation methodologies have their limitations. VMmark 2.x Dedicated to the VMwares platforms A macro benchmark -- no spefic metrics about LM performance Existing research on LM ( [Vee09 Hines], [HPDC09 Liu], [Cluster09 Jin], [IWVT08 Liu], [NSDI05 Clark], … ) All dedicated to design LM strategies No unified metrics and workloads. Results are not comparable to each other. Some critical issues are not mentioned. Still lack of a formal and qualified LM benchmark

7 Virt-LM Benchmark Overview

8 Goal and Criterias Goal of Virt-LM Benchmark: Compare LM performance among different hardware and software platform, especially in data center scenarios Design Criteria: Metric Sufficient Observable Concise Workload Typical Scalable Scoring methodology Impartial Stability Produce repeatable results Compatibility Usability Workloads platform … … Metric Results Metric Results Metric Results Metric Results Metric Results Metric Results

9 System Under Test System Under Test SUT : Evaluation Target Hardware and software platform Including its VMM and the LM strategies it used Workloads SUT … … Metric Results Metric Results Metric Results Metric Results Metric Results Metric Results

10 Metrics Metrics and Measurement: Downtime Def: how long the VM is suspended Measure: ping Total migration time Def: how long a LM lasts Measure: timing the LM command Amount of migrated data Def: how many data is transferred Measure: transferred data on its exclusive TCP port Migration overhead Def: How much LM impaires performance of the workload Measure: Declined percentage of the workloadss score 9 Metrics Sufficiency: Cost : migration overhead, amount of migrated data (burden on network) QoS: downtime, total migration time migration overhead,

11 Workloads Representative to real scenarios Where: Data centers When: Load balancing power management, service enhancement and fault tolerate Platform (HW and VMM) VM … … migrate OS service

12 Workloads During a live migration, VM could run different services Mail Server Application Server File Server Web Server Database Server Standby Server Other VMs exist on the same platform Heavy during load balancing Light during power management Random during service enhancement and fault tolerance Happens at any moments (Migrations Points) 11 Platform (HW and VMM) VM … … migrate OS service

13 Workload Implementation Internal workload types Mail Server: SPECmail2008 App Server: SPECjAppServer2004 File Server: Dbench Web Server: SPECweb2005 Database Server: Sysbench Standby Server: Idle VM External workload types Heavy: more VMs to fully utilize the machine Increasing VMs until workload performances are undermined Light: single VM on the platform Platform (HW and VMM) VM … … migrate OS Internal Workload External workload

14 Migration Points Problem During the run of a workload LM happens at random time Performance varies at different points workload: 483xalancbmk of SPECcpu2006 How to fully represent a workloads performance variety Test as many migration points spreading the whole run of a workload

15 Migration Points Problem Problem too many points prolong the test significantly Soution More sample results in each run Only a few runs Implementation Divide a workloads runtime into many time sectors Each time sector is longer than total migration time Migrate at the startpoint of each sector First run Second run Third run

16 Scoring Method Goal: compute an overall score Each metric i compute a final score S i Normalize each result (P ij ) using reference system(R ij ) Sum up results of all workloads: S i of reference system is always 1000: Lower Score indicates higher performance Open Problem: merge the 4 metrics S i Different property different variation Simply adding up is not appropriate Current implementation in Virt-LM: Final result have 4 scores

17 Other Criterias Usability Easy to configure VM images Provided Workloads pre-installed Easy to run Automatically managed after launch Compatibility Successful on Xen and KVM Scalable workload: Fully utilize the hardware Heavy enough macro workload Live migration lasts a long time. (Multiple live migration) more than one are migrated concurrently

18 Benchmark Components Logical components System Under Test Migration Target Platform VM Image Storage Management Agent Benchmark components Workload VM images Distributed on VM Image Storage Running Scripts Installed on Management Agent

19 Internal Running Process For every class of workload Initialize the environment Run the workload Migrate the VM at different migration points Fetch the metrics results Collect all results and Compute an overall score Management Agent automatically control the whole process 18

20 Experiments on Xen and KVM Experiment Setup SUT-XEN VMM Xen 3.3 on Linux Hardware DELL OPTIPLEX 755, 2.4GHz Intel Core Quad Q6600 2GB memory, sata disk, 100Mbit network SUT-KVM VMM KVM-84 on Linux Hardware Same as SUT-XEN VM Linux , 512MB mem, one core Workload Internal: SPECjvm2008, cpu/mem intensive workloads External: Light: single VM Migration Points:Spreading the whole running

21 Experiments on Xen and KVM Analysis SUT-KVM intensively compress the data Less migrated data and less total time More overhead

22 Experiments on Xen and KVM Analysis SUT-XEN strictly control the downtime Less downtime More migrated data Due to more rounds of pre-copy to decrease downtime

23 Experiments on Xen and KVM Analysis Conclusion SUT-XEN less downtimeand overhead, But more consumption of network

24 Further Issues and Possible Solutions

25 1. Workload Complexity Total test takes a long time When workloads has too many combination (I) Internal workload types: Mail Server,App Server, File Server, Web Server, DBServer, Standby Server (E) External workload types: Heavy, Light (P) Migration points quantity: Considerable due to the long run time of each workload Internal workload External workload Multiple migration Migration Points Total time = Runtime * N workload types N = I * E * P (* M )

26 Possible Solutions Speed up for migration points: (Virt-LMs current implementation) More samples in a run Using time-insensitive workloads Micro operation: CPU, Memory, IO… Different memory r/w intensity Advantage: Eliminate the Migration Points dimension Internal workloads are reduced Runtime of each each workload is shorten Disadvantage: Different from real scenarios Hybrid Test time-insensitive micro workloads Analysis and predict typical workloads results Redefine an average workload

27 2. Multiple/Concurrent Live Migration Problem: Define overall metrics Representative for platforms maxium performance Other concerns: When average results decreased obviously When system cannot afford Possible solutions Maximum sum of metrics Define different thresholds Platform (HW and VMM) VM … … Average decreased Obviously System cannot afford Thresholds: Concurrent numbers Maximum sum Sum decreased Obviously

28 3. Other Issues Overall score computation Virt-LM produces 4 scores as the final result Definition of external workloads Current implementation is simple Repeatability Need more experiment to exam Migration points are not precisely arranged Compatibility Should be compatible to other VMM, besides Xen and KVM Usability More easy to configure and run

29 Conclusion

30 Current Work Investigation on recent work on LM Summarize the critical problems Migration points Workload complexity Scoring methods Multiple live migration Present some possible solutions Implement a benchmark prototype – Virt-LM More details in Virt-LM: A Benchmark for Live Migration of Virtual Machine(ICPE2011)

31 Future work Improve and complete Virt-LM Implement and test other solutions Workload complexity Multiple live migration Overall score computation Others Test and compare their effectiveness and choose best one 30

32 Our Possible Work under the Cloud WG

33 Possible Work Relation to the cloud benchmark Enough migration cost in the workload Although the cost maybe not a metric, we have to ensure workload could cause enough cost. How fast could a cloud reallocate resources? If implemented by live migration technology, it regards to following two factors: 1. how many migrations (determined by) resource management and reallocation strategies 2. how fast for each migration live migration efficiency & cost Possible future work under cloud benchmark We may work on how to ensure the workload produce enough live migration cost 32

34 Possible Work We hope to cooperate with other members, maybe join a sub-project related to live migration. We hope can contribute to the design of the Cloud Benchmark 33

35 Team Members Prof. Dr. Qinming He Kejiang Ye Representative of the SPEC Research Group Assoc. Prof. Dr. Deshi Ye Jianhai Chen Dawei Huang …….

36 Appendix: Teams Recent Work

37 Virtualization Performance Virtualization in Cloud Computing System IEEE Cloud2011, IEEE/ACM GreenCom2010 Performance Evaluation & Benchmark of VM ACM/SPEC ICPE2011, IWVT2008 (ISCA Workshop), EUC2008 Performance Optimization of VM ACM HPDC2010, IEEE HPCC2010, IEEE ISPA2009 Performance Modeling of VM IEEE HPCC2010, IFIP NPC2010 Performance Testing Toolkit for VM IEEE ChinaGrid

38 Publications [1] Live Migration of Multiple Virtual Machines with Resource Reservation in Cloud Computing Environments (IEEE Cloud2011, Accept) [2] Virt-LM: A Benchmark for Live Migration of Virtual Machine (ACM/SPEC ICPE2011) [3] Virtual Machine Based Energy-Efficient Data Center Architecture for Cloud Computing: A Performance Perspective (IEEE/ACM GreenCom2010) [4] Analyzing and Modeling the Performance in Xen-based Virtual Cluster Environment, (IEEE HPCC2010 ) [5] Two Optimization Mechanisms to Improve the Isolation Property of Server Consolidation in Virtualized Multi-core Server, (IEEE HPCC2010) [6] Evaluate the Performance and Scalability of Image Deployment in Virtual Data Center, (IFIP NPC2010) [7] vTestkit: A Performance Benchmarking Framework for Virtualization Environments, (IEEE ChinaGrid2010) [8] Improving Host Swapping Using Adaptive Prefetching and Paging Notifier, (ACM HPDC2010) [9] Load Balancing in Server Consolidation, (IEEE ISPA2009) [10] A Framework to Evaluate and Predict Performances in Virtual Machines Environment, (IEEE EUC2008) [11] Performance Measuring and Comparing of Virtual Machine Monitors, (IWVT2008, ISCA Workshop) 37

39 Thank you!

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