1 / 29

Presented by Kim, Hongseok ( 김홍석 ) Mobile Embedded System Lab.

Friendly Virtual Machines Leveraging a Feedback-Control Model for Application Adaptation Yuting Zhang, Azer Bestavros, Mina Guirguis ACM/USENIX VEE’05. Presented by Kim, Hongseok ( 김홍석 ) Mobile Embedded System Lab. Agenda. Introduction FVM: Framework FVM: Model and Analysis

avel
Download Presentation

Presented by Kim, Hongseok ( 김홍석 ) Mobile Embedded System Lab.

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Friendly Virtual MachinesLeveraging a Feedback-Control Model for Application AdaptationYuting Zhang, Azer Bestavros, Mina Guirguis ACM/USENIX VEE’05 Presented by Kim, Hongseok (김홍석) Mobile Embedded System Lab.

  2. Agenda • Introduction • FVM: Framework • FVM: Model and Analysis • FVM: Implementation • FVM: Performance Evaluation • Conclusion

  3. Virtual Machine Model • VMs share the resources of the underlying system

  4. Problem • Problem • Excessive resource request of VMs can cause performance degradation • How could the resources of the underlying system be managed fairly and efficiently ? • Terminologies • Efficiency • Implies that underlying system resources are neither overloaded nor unnecessarily underutilized • Fairness • Implies that each VM is allocated a proportionate share of the bottleneck resource for that VM

  5. Solutions • Host-Level (Centralized) Resource Management • Hard to achieve fairness • Due to the diverse nature and dynamic characteristics of applications (VMs) • Ex) Resource manager may reduce memory use of a IO-bound VM when host system experiences memory contention. • Host OS or VMM must be modified • Application-Level (Distributed) Resource Management • VMs adapts their resource demand when resource contention occurs  Frendly Virtual Machines (FVM) • Advantage • Transparent to the applications and the underlying system • Resource agnostic: unnecessary to know the type of bottlenecked resource • Allowing different adaptation strategy to be used in different VMs

  6. Agenda • Introduction • FVM: Framework • Feedback Signal: Overload Detection • Control Signal: Resource Consumption • Controller: Adaptation Strategy • FVM: Model and Analysis • FVM: Implementation • FVM: Performance Evaluation • Conclusion

  7. Feedback Signal: Overload Detection • By monitoring resource states • Such as CPU utilization, network utilization, and page fault ratio • Requiring special support of host system • By monitoring VM states • Such as response time, jitter, throughput • Requiring no support of host system

  8. Feedback Signal: Overload Detection • Virtual Clock Time (VCT) • Real-time interval between two consecutive virtual clock cycles • Increasing when resource contention increasing • Congestion Signal Generation • Exponentially-Weighted Moving Average (EWMA) of VCT • The minimum VCT in time-window ω • Congestion signal driving condition

  9. Control Signal: Resource Consumption • Multi Programming Level (MPL) Control • Adjusting the number of threads running on the VM • Can not be used only when MPL is to small • Rate Control • Adjusting sleep time or sleep frequency of VMs

  10. Controller: Adaptation Strategy • Demand Increase/decrease rules must guarantee desirable properties of efficiency and fairness • Additive-Increase/Multiplicative-Decrease (AIMD) • VM can increase its resource demand linearly as long as resources isn’t overloaded • VM must decrease its resource demand by exponentially when resource overload detected

  11. Controller: Adaptation Strategy • Why is AIMD fair ? equal resource share R Additive increase (by 1) Multiplicative decrease (by factor of 2) VM 2 Resource Demand VM 1 Resource Demand R

  12. Agenda • Introduction • FVM: Framework • FVM: Model and Analysis • Model Derivation • Simulation Result • FVM: Implementation • FVM: Performance Evaluation • Conclusion

  13. Model Derivation • Demand from vth VM: • Total resource usage • VCT slowdown at time t • EWMA of : • Congestion signal • Amount of demand adjusted by vth VM

  14. Model Derivation • Block diagram for application adaptation

  15. Simulation Result • Simulation environment • Model parameters: • 5 VMs running on host system • Initial condition :4, 7, 10, 13, and 16 threads running on each VM

  16. Agenda • Introduction • FVM: Framework • FVM: Model and Analysis • FVM: Implementation • FVM: Performance Evaluation • Conclusion

  17. FVM: Implementation • FVM implementation is based on a modified version of User Mode Linux (UML) • FVM Control Model

  18. FVM: Implementation • Parameterized implementation • Baseline Settings of FVM Prototype

  19. Agenda • Introduction • FVM: Framework • FVM: Model and Analysis • FVM: Implementation • FVM: Performance Evaluation • Performance Metrics • Benchmark Application Experiments • Web Server Experiments • Conclusion

  20. Performance Metrics • Efficiency metrics • VCT (indicating responsiveness) • Throughput • Fairness and efficiency metrics • Fairness Index (FI) ( Where, is the performance metric for is the optimal value of the metric under a perfectly fair and efficient allocation ) ☞ FI = 1.0 implies optimal performance with respect to both efficiency and fairness

  21. Benchmark Application Experiments • Using memory-intensive benchmark • Benchmark program grabs 1MB of memory buffer, reads data from a file into the buffer, performs some computations, writes back the content of the buffer to another file, and then frees the buffer. • Each VM has a number of threads • Each thread executes benchmark program • Number of VMs and threads can be varied as experimet • Experiment environment • 2.4 GHz Pentium IV with 512 KB of cache • 1.2 GB of RAM • To ensure memory-bottleneck, a background application used to lock 800MB

  22. Benchmark Application Experiments • Benchmarking results showing performance metrics vs. number of VMs (# threads = 50)

  23. Benchmark Application Experiments • Benchmarking results showing performance metrics vs. number of threads per VM (# VMs = 2)

  24. Web Server Experiments • Apache 2.0 web server used • A total of 4 VMs • Each VM hosting an Apache server • Session-based workload used • To vary the workload on the VMs, httperf clients used to genrate HTTP session based workload • 4 client machine are used to run the httperf synthetic workload generator • Experiment environment • Sever machine • 1.4 GHz Pentium IV with 512 MB of RAM • Client machine • 2.4 GHz Pentium IV with 1.2 GB of RAM

  25. Web Server Experiments • Performance of VM-hosted Web severs under varying number of httperf sessions

  26. Web Server Experiments • Evolution of Virtual Clock Time (VCT) for each VM over time

  27. Web Server Experiments • Achievable throughput per VM-hosted Web sever over time

  28. Agenda • Introduction • FVM: Framework • FVM: Model and Analysis • FVM: Implementation • FVM: Performance Evaluation • Conclusion

  29. Conclusion • Delegating the regulation of resource usage to the VM itself • Enabling applications to be partitioned into different congestion equivalence classes • Enabling different adaptation strategy coexist, thus allowing VMs to select the most appropriate manner • Enabling the design complexity of underlying hosting systems to be significantly reduced

More Related