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CPSC 601.38: Project Brainstorming Session

CPSC 601.38: Project Brainstorming Session. Carey Williamson Department of Computer Science University of Calgary. PROJECT OVERVIEW. A “typical” course project might involve: design/build/obtain appropriate testbed, environment, or platform for your project extend/customize as needed

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CPSC 601.38: Project Brainstorming Session

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  1. CPSC 601.38:Project Brainstorming Session Carey Williamson Department of Computer Science University of Calgary

  2. PROJECT OVERVIEW • A “typical” course project might involve: • design/build/obtain appropriate testbed, environment, or platform for your project • extend/customize as needed • obtain relevant data/measurements needed • design suitable experiment: clear goal, identify factors, levels, performance metrics • obtain and present (new/interesting) results

  3. Some Data Sets and Traces • Web server access logs (1996) • Web proxy access logs (1998) • MPEG video traces (20 x 40,000 frames) • ISP measurements (4 traces, 1-2 minutes) • FrameRelay/ATM traces (5 traces) • Bellcore Ethernet LAN trace (1989) • TCP/IP packet traces (LBL, 24 hours, 1.8M) • See also the “Internet Traffic Archive”

  4. Some Available Simulators • ATM-TN simulator (ATM cell-level) • Clustered Web server simulator (dws) • Web proxy caching hierarchies (Muda) • Distributed Web proxy simulator • IP-TN simulator (U of C) • IP-TNE (emulator) (U of C) • LBL’s ns-2 simulator (TCP packet level)

  5. Some Useful Tools • Synthetic Web proxy workload generation • Web client traffic model (mosaic, 1995) • LRD traffic analysis (R/S, V-T, AC, etc) • GUI for traffic modeling/analysis (synTraff) • Wavelet-based traffic model (Ram) • Synthetic MPEG video trace generation • SimKit programming language (UofC)

  6. Issues and Ideas • Improving/extending ProWGen • temporal locality; document mods; scaling • Web proxy caching hierarchies • Hierarchical vs distributed caching • Web response time modeling • Improving network TCP flow model (dws) • Wavelet-based traffic forecasting • Wavelength assignment in WDM networks

  7. 1. ATM-TN System Overview Input Data Set ATM MF ATM-T ATM-N TMF SimKit ESS WarpKit SMTW Output Data Set workstation UNIX Report Generation Scripts Hardware SPARC, KSR, SGI Report

  8. CBR Poisson Ethernet JPEG/MPEG Web TCP/IP/AAL5 ABR Traffic Models ATM MF TMF Switch and Network Models SimKit ESS WarpKit WaiKit SMTW UNIX Operating System X Sequential: UNIX Workstations (SGI, SPARC, DEC, HP) Parallel: SGI Power Challenge, SPARC 1000

  9. 2. Distributed Web Server Model 1 File Server 2 3 Cache Dispatcher (Front End) Manager N Object Store Web Clients Server Nodes

  10. Num server nodes Mem cache size Disk cache size Cache replacement policy for each (LRU, LFU, SIZE, DUAL) Comm. latency Cache consistency Dispatch policy (DNS, RR, Redirect, Load) Request distribution policy (requests, bw, conns, affinity, ...) Server bandwidth Per-request bandwidth BW scaling model Server Parameters

  11. Load balancing requests bytes bandwidth connections clients Relative improvement versus RR, Rand, etc Cache performance document hit rate byte hit rate Comm. overhead Avg response time Avg inflation factor Others... Performance Metrics

  12. 3. Web Proxy Caching Model Aggregate Workload Proxy server Web Servers Web Clients

  13. Upper Level (Parent) Complete Overlap No Overlap Lower Level (Children) Partial Overlap (50%) Proxy server Proxy server Proxy server Hierarchical Proxy Caching Simulation Model Web Servers Web Clients

  14. Factors and Levels • Cache size • Cache Replacement Policy • Recency-based LRU • Frequency-based LFU-Aging • Size-based GD-Size • Workload Characteristics • One-timers, Zipf slope, tail index, correlation, temporal locality model

  15. Zipf LLCD P F Correlation r s -1 0 +1 ProWGen Conceptual View ProWGen Software Input Parameters Synthetic Workload 1 Z a c L

  16. Key Workload Characteristics • “One-timers” (60-70% useless!!!) • Zipf-like document referencing popularity • Heavy-tailed file size distribution (i.e., most files small, but most bytes are in big files) • Correlations (if any) between document size and document popularity (debate!) • Temporal locality (temporal correlation between recent past and near future references) [Mahanti et al. 2000]

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