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A U nified, L ow-overhead F ramework to Support Continuous Profiling and Optimization

22nd IEEE International Performance Computing and Communications Conference (IPCCC’2003). A U nified, L ow-overhead F ramework to Support Continuous Profiling and Optimization. Xubin (Ben) He (hexb@tntech.edu) Storage Technology & Architecture Research( STAR ) Lab

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A U nified, L ow-overhead F ramework to Support Continuous Profiling and Optimization

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  1. 22nd IEEE International Performance Computing and Communications Conference (IPCCC’2003) A Unified, Low-overhead Framework to Support Continuous Profiling and Optimization Xubin (Ben) He (hexb@tntech.edu) Storage Technology & Architecture Research(STAR) Lab Department of Electrical and Computer Engineering

  2. Outline • Introduction • Architecture and Design • Performance Evaluations • Conclusions and Future Work ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  3. Introduction ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  4. Motivations • System profiling is an important mechanism to observe system activities. • Profiling-based optimization has become a key technique. • Continuous and online optimization is needed because of changed system usage patterns. ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  5. Current State-of-the-art • Traditional approaches bring high overhead to already overloaded system. • Profiling and optimization overhead: • Raw Data Gathering • Data Recording • Data Processing • Feedback ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  6. 1 2 ULF Host 3 Introducing Unified, Low-overhead Framework (ULF) • Offload computing overheads from host processors to an embedded processor; • Continuous feedback loop model: • 1. Low overhead profiling to gather system event data; • 2. Parallel processing raw data and policy generation; • 3. Apply policy to host; ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  7. Introduction • Architecture and Design Performance Evaluations Conclusions and Future Work ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  8. Components • ULF board: an embedded processor with a sufficient amount of RAM • Host-side module: APIs as a library or kernel module • Board-side module:embedded os, a libray, plug-ins ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  9. ULF Board • Low cost, low power embedded processor. • Expandable with secondary PCI slot. • Interface with host via standard PCI slot ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  10. Interaction between Plug-ins and Boards Initial stage-->Running--->Cleanup ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  11. Example Applications • Low overhead profiling • On-line program optimizer • On-line file system cache optimizer • … ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  12. Introduction Architecture and Design • Performance Evaluaitons Conclusions and Future Work ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  13. Experimental Setup • Methodology • Prototype using Intel IOP310 processor, Linux 2.4.16 • I/O profiling tool: LTT(Linux Trace Toolkit) • Workloads • Postmark of Network Appliances: throughput • 20k initial files, transactions ranging from 150k to 300k. • Iozone • 4 configurations • NTNR: Neither Traced Nor Recorded • TNR: Traced but Not Recorded • TDR: Traced and Disk Recorded • TFR: Traced and ULF Recorded ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  14. PostMark Results ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  15. Different W/R ratio ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  16. Iozone results ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  17. Introduction Architecture and Design Performance Evaluations • Conclusions and Future Work ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  18. Conclusions • A unified, low-overhead framework helps profiling tools to save profiling data rapidly and perform run-time parallel processing. • Reduces profiling overhead • LTT: 40%-->0.4%. ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  19. Future Work • Apply ULF to more case studies • Performance: • Adaptively adjust system prefetching and caching policy; • Online code rewrite and recompilation; • Security: • Monitor abnormal system access and high risk events. • Intrusion detection ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  20. Acknowledgements • Dr. Ken Yang • Ming Zhang • NSF • Manufacturing Center at T.T.U ULF Storage Technology & Architecture Research lab(STAR) T.T.U

  21. A Unified, Low-overhead Framework to Support Continuous Profiling and Optimization IPCCC’2003 Xubin He (hexb@tntech.edu) http://www.ece.tntech.edu/hexb/starlab.htm Storage Technology & Architecture Research(STAR) Lab Department of Electrical and Computer Engineering

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