1. Profiling Application Performance and Resource Utilization in Virtualized Environments UmeshBellur PuruKulkarni http://www.cse.iitb.ac.in/synerg Systems and Networks Research Group Department of Computer Science and Engineering Indian Institute of Technology Bombay

3. Virtual machines execute on virtual systems

4. Virtual machine monitor multiplexes physical resources across virtual resourcesBenefits Direct: Rapid provisioning, Interoperability, Performance Isolation, Increased resource utilization Derived: Server Consolidation, Flexible resource provisioning, Efficient replication, backup and migration

5. Feb 16th, 2011 3 Virtualisation-based Provisioning Issues Flexible Resource Provisioning SLA-based migration Power-aware consolidation Cloud bursting Physical to Virtual Resource requirements mapping and modelling Capacity planning VM placement policies Deployment & Manageability Resource accounting in Clouds Prototype Testbed setups VM Optimisations Shared memory based I/O for co-located VMs Network I/O guarantees VMs for resource-constrained platforms

7. Throughput, response time, network, CPU, memory ...

8. With finite resources which SLAs can be satisfied?

9. How will the application behave with varying resources?

11. All I/O through privileged VM

12. Impact of VM migration

13. Workload generation and app. performance separate from resource monitoring 5 01/12/10

15. xenmon, xentop, sysstat, Hyperic etc.

16. Load generation benchmarks

17. SPEC, TPCW ...

18. Isolated from profiling framework

19. Load generation and control

20. Resource configuration control

22. Multi-tier, multi-VM based (web-based) application

23. Implications on

24. Capacity planning

25. Migration and Consolidation Strategies

26. virtPerf*

27. Application profiling tool for virtualized environments

28. Built on top of AutoPerf** – a similar tool for physical environments

29. Current scope: Xen and kvm* The tool forms the thesis of a current MTech student - PrajaktaPatil. ** S. Shirodkar, “Autoperf: An automated load generator and performance measurement tool for multi-tier software systems,”, Master’s thesis, IIT Bombay, Mumbai, India, June 2007

31. Thread startup, caching etc.

35. CPU utilization

36. Network & disk I/O

37. Memory usage

38. Performance metrics

39. Average response time

40. Throughput

42. #clients, think time distributions, total number of requests issued

43. AutoPerf provides automatic load level section, warmup detection

44. VM resource configuration

45. #cores, CPU allocation (min, max), CPU pinning

46. To both Dom0 and each of the DomUs individually

47. Impact of Migration

48. Resource requirements, Performance hit (drop in throughput, increase in response time)

49. VM Collocation

51. CPU Utilization:

52. A service that can consume a specific amount of CPU with a knob to vary this from 10-90%.

53. virtPerf reports utilization within 3% of this at any level.

54. Network I/O:

55. A service will read/write a known amount of I/O at a specified rate.

56. virtPerf reports back this number with a 400 byte overhead per request caused by HTTP and the virtPerf control overhead

57. Disk I/O:

59. Virtual platforms: XenWebCalendar & RUBiS - 2 tier applications with a MySQL backend and a HTTP-based front-end

61. virtPerf can automatically detect saturation

63. Feb 16th, 2011 18 1 core vs 4 cores Maximum Throughput 16 req/s : 65 req/s Response time 900 ms : 3000 ms Performance metrics ratio 1 : 4 VM CPU Utilization ~80 % : 375 % VM Configuration (#cores)

65. c1 >> c2 >> c3

66. Response time

68. Single VM app (c1)

69. 2 VMs on 1 PM (c2)

71. Feb 16th, 2011 VM Migration while under Load

72. Feb 16th, 2011 Effects of Migration

75. Feature set (profiling with ...)

76. Load distribution control

77. VM resource configuration and control

78. Performance and resource metrics with migration

79. Profiling with VM interference (to do)

81. Session, probabilistic request workloads

82. Profiling with interfering VMs

83. Evaluation and testing with kvm

84. Experimentation

85. Testing at scale

86. Black-box vs. Gray-box profiling - Remove profiling within VM

87. Bottleneck identification with multi-tier applications

88. Open-source releasehttp://www.cse.iitb.ac.in/synerg

90. Affinity-aware resource profiling and prediction

91. Benchmarking, estimating and tuning cost of migration

92. p2v placement tool

93. Design and evaluation of virtualisation-aware placement techniques

94. vSim

95. A simulator for virtualisation-based systems