Jin Hai

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Xen Summit Asia 2009

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Jin Hai

  1. 1. ® ChinaV: Experiences on Virtualization Technology Hai Jin, Huazhong University of Science and Technology
  2. 2. Outline ® • Introduction of ChinaV Project • Experiences on Virtualization Technology • Conclusions
  3. 3. What is ChinaV ® http://grid.hust.edu.cn/973 • Research on Fundamental Theory and Approach of Computing System Virtualization, supported by National 973 Basic Research Program of China under grant No.2007CB310900 • Started from 2007 to 2011 with total budget RMB 26M
  4. 4. Visions ® http://grid.hust.edu.cn/973 • Virtualized Resources Environment – Combine or divide resources: good granularity and transparence • Virtualized Tasks Environment – Build task executing environment on-demand: high utilization and efficiency • Virtualized User Environment – Desktop virtualization: high convenience, good user experiences
  5. 5. Missions ® http://grid.hust.edu.cn/973 • Theoretical model and architecture of the virtualized computing system • Single-dimensional system resource virtualization • Multi-dimensional system resource virtualization • Pervasive computing environment of virtualized system • Security and trusted scheme of the virtualized computing system • Theory and approach of evaluating virtualized computing system • High performance based virtualization technology • Application of virtualized simulation system
  6. 6. ® Research Team http://grid.hust.edu.cn/973
  7. 7. Outline ® • Introduction of ChinaV Project • Experiences on Virtualization Technology – Live Migration – Power Management – Memory Virtualization – Desktop Virtualization • Conclusions
  8. 8. Outline ® • Introduction of ChinaV Project • Experiences on Virtualization Technology – Live Migration – Power Management – Memory Virtualization – Desktop Virtualization • Conclusions
  9. 9. ® CR/TR-Motion: A Novel VM Migration Approach • Revirt is adopted • Checkpointing/recovery with trace/replay technology are used to provide fast and transparent live VM migration • We orchestrate the running source and target VM with execution trace logged on the source host H. Liu, H. Jin, X. Liao, L. Hu, and C. Yu, “Live Migration of Virtual Machine Based on Full System Trace and Replay”, Proceedings of the 18th International Symposium on High Performance Distributed Computing (HPDC'09), ACM Press, June 11- 13, 2009, Munich, Germany, pp.101-110
  10. 10. CR/TR-Motion System Structure ®
  11. 11. CR/TR-Motion: Migration Process ® Checkpoint A log1 Checkpoint B Round 1 log2 VM Recovery Round 2 Replay log1 …… log3 Round n …… Waiting and chasing phase …… Transfer log n Stop and copy Replay log n Take over A
  12. 12. CR/TR-Motion: Migration Downtime ® • Our approach reduced migration downtime by 72.4% in average compared to pre-copy approach • Our approach reduces the total migration 300 CR/TR-Motion time by 31.5% in average compared to Pre-copy 250 Pre-copy 200 100 Downtime(ms) CR/TR-Motion 90 150 Pre-copy 80 To l m ra n tim (s) 100 70 e 50 60 ta ig tio 0 50 Daily use Kernel-build Static web Dynamic UnixBench 40 app web app 30 20 10 0 Daily us e Kernel-build Static web Dynamic UnixBench app web app
  13. 13. ® CR/TR-Motion: Total Data Transferred • CR/TR-Motion reduces 900 CR/TR-Motion T ta D ta T n rre (M ) 800 B Pre-copy 700 synchronization traffic o l a ra sfe d 600 500 by 95.9% in average 400 300 200 • This improvement 100 0 brings great benefit Daily use Kernel-build Static web app Dynamic web app UnixBench when our migration scheme is applied in daily use kernel-build 0.48 (0.04) 0.53 (0.06) 38.54 (2.1) 152.44 (8.2) 98.8% 99.6% low-bandwidth WANs static web app dynamic web app 8.34 (0.21) 36.4 (0.96) 228.99 (9.4) 288.05 (12.2) 96.4% 87.4% unixbench 2.59 (0.22) 113.38 (6.4) 97.7%
  14. 14. Outline ® • Introduction of ChinaV Project • Experiences on Virtualization Technology – Live Migration – Power Management – Memory Virtualization – Desktop Virtualization • Conclusions
  15. 15. Power Management - Motivation ® • Reduce power consumption with little performance penalty • User requirement is various – Server – Average power consumption should fit the budget – Desktop – User experience should be kept – Laptop – Prolong battery lifetime • Virtualization brings challenges – Guest OS is blind to the hardware features – VMM lacks the device PM ability – it has no device drivers
  16. 16. Design of ClientVisor ® • Focus on desktop virtualization – VMs are asymmetric – Hardware power features can be exposed to VM • Dom0 – Domain 0, the control domain • SOS – Service OS, background domain for special tasks. e.g., network packet filtering • COS – Capability OS, primary domain interacted with users H. Chen, H. Jin, Z. Shao, K. Yu, and K. Tian, “ClientVisor: Leverage COTS OS Functionalities for Power Management in Virtualized Desktop Environment”, Proceedings of the 2009 ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE’09), ACM Press, March 11-13, 2009, Washington, DC, USA, pp.130-141.
  17. 17. ® Design of ClientVisor Frontend A: Px operation Driver COS (primary user domain) Backend C B: Cx operation SOS Driver Domain0 Device Device (control domain) Driver OSPM VA Driver C: Dx operation C C B Xen VMM Coordination Logic A D: Cx operation after coordination D E E: Dx operation after Physical Platform Devices CPU coordination
  18. 18. Design of ClientVisor ® • Basic instruments – What guest OS does? – Processor PM instruments – working state PM (P-state scaling) & idle state PM (C-state transition) – Device PM instruments – D-state transition • Interception policies – What VMM does? – Passing-through – for P-state operation – Coordination – for C-state & D-state operation
  19. 19. ® ClientVisor • Preliminary of passing-through – Root Exposing hardware power features – ACPI tables Bridge Endpoint Bridge – CPUID – Device hierarchy Endpoint Endpoint Endpoint Endpoint
  20. 20. Performance Evaluation ® • Static power consumption • Dynamic power – Leave the whole system in idle 18 consumption 30.00 25.00 23.50 28.43 26.85 26.27 26.21 16.71 15.36 16 – SPECpower_ssj2008 is 20.00 Power (W) 13.34 13.01 14 15.00 12 10.91 used as workload Power (W) 10 10.00 8 5.00 6 4 0.00 Native Xen CV/Orig CV/Cx_op t CV/Cx_Timer_op t 2 0 (a) Overall Native Xen CV/Orig CV/Cx_op t CV/Cx_Timer_op t 40,000 35,000 40 Cx mapping optimization – Change Cx operation 35 ops) 30,000 of port I/O way to MWAIT way erform nce(ssj_ 30 o er ) P w (W 25,000 20,000 25 Timer optimization – Disable some timer a 15,000 20 10,000 handlers when CPU resides in Cx P 15 5,000 0 10 100% 90% 80% 70% 60% 50% Load Level 40% 30% 20% 10% 0% Balance of power and Native Xen CV/Orig CV/Cx_opt CV/Cx_Timer_opt CV/Cx_opt Native CV/Cx_Timer_opt Xen CV/Orig performance
  21. 21. Outline ® • Introduction of ChinaV Project • Experiences on Virtualization Technology – Live Migration – Power Management – Memory Virtualization – Desktop Virtualization • Conclusions
  22. 22. ® Dynamic Memory Balancing for Virtual Machines • Motivation – Allocating appropriate machine memory to a VM is hard • Memory requirement varies during running • OS only reports the amount of used/free memory • The amount of actively used memory is more important – If we know the relationship between memory allocation size and performance gain/loss • Idle or inactive memory can be reclaimed without notable performance loss • Better memory resource utilization – Ballooning • The amount to increase/decrease is typically specified manually
  23. 23. ® Dynamic Memory Balancing for Virtual Machines • Dynamic memory balancing – LRU-based predictor – Memory growth prediction – Automatic memory resizing inflate/deflate VM1 VM2 Controller balancer WSS Mon Mon estimator Data store LRU Hist. LRU Hist. VMM W. Zhao and Z. Wang, “Dynamic Memory Balancing for Virtual Machines”, Proceedings of the 2009 ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE’09), ACM Press, March 11-13, 2009, Washington, DC, USA, pp.21- 30.
  24. 24. ® Dynamic Memory Balancing for Virtual Machines • Estimation Accuracy (within Xen) – VM is allocated with 214MB Monotonic (40 ~ 170 MB) Random (40 ~ 170 MB)
  25. 25. Outline ® • Introduction of ChinaV Project • Experiences on Virtualization Technology – Live Migration – Power Management – Memory Virtualization – Desktop Virtualization • Conclusions
  26. 26. Challenge of Desktop Virtualization ® • User experience – Fast, convenient, mobility • Security – Safeguard user private data • Stability – Reliability of the virtual desktop environment • Serviceability – Efficient use of CPU and memory resources X. Liao, H. Jin, L. Hu, and H. Liu, “Towards Virtualized Desktop Environment”, Concurrency and Computation: Practice and Experience, John Wiley & Sons, Ltd (accepted)
  27. 27. ® System Architecture of Virtual Desktop Data Server APP Server …… Xen Xen server server Internet Virtualized VCM PC Domain 0 Domain U Thin PDA Xen Client
  28. 28. ® Save & Restore (Checkpointing) • Multi-VM collaborative save & restore – Recoverable long-running desktop applications – User environment mobility – High availability • Multi-host checkpointing – Checkpoint synchronization (Lamport clocks) – Transparent rolling checkpoints (Copy-on-write) – Memory image saving optimization
  29. 29. Virtual Appliance ® App Server • USB devices and printers on the client can be remote desktop Network delivering accessed by the remote access mount application on a USB device local network or client Plug in the Internet
  30. 30. ® VM Life Cycle Management • Role-based life cycle monitor scheme • VM suspending management • VM process priority policy • VM template life cycle management • VM checkpoint life cycle management
  31. 31. ® All-in-one Desktop Environment
  32. 32. Outline ® • Introduction of ChinaV Project • Experiences on Virtualization Technology • Conclusions
  33. 33. Conclusions ® • As the technology base of cloud computing, virtualization technology provide – Support new architectures, devices – High Utilization of IT facilities – High Manageability – Highly secure and isolate guaranteed environment – Maintain good user experiences • Challenges still exist in virtualization technology – Scheduling – Live Migration – Power Management – Memory/IO Virtualization – Desktop Virtualization – ……
  34. 34. ® Thank you!

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