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Wireless Internet Performance Research

Wireless Internet Performance Research. Carey Williamson iCORE Professor Department of Computer Science University of Calgary. Application: supporting network applications and end-user services FTP, SMTP, HTTP, DNS, NTP Transport: end to end data transfer TCP, UDP

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Wireless Internet Performance Research

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  1. Wireless InternetPerformance Research Carey Williamson iCORE Professor Department of Computer Science University of Calgary

  2. Application: supporting network applications and end-user services FTP, SMTP, HTTP, DNS, NTP Transport: end to end data transfer TCP, UDP Network: routing of datagrams from source to destination IPv4, IPv6, BGP, RIP, routing protocols Data Link: hop by hop frames, channel access, flow/error control PPP, Ethernet, IEEE 802.11b Physical: raw transmission of bits Application Transport Network Data Link Physical Internet Protocol Stack 001101011...

  3. The Wireless Web • The emergence and convergence of these technologies enable the “wireless Web” • the wireless classroom • the wireless workplace • the wireless home • My iCORE mandate: design, build, test, and evaluate wireless Web infrastructures • Holy grail: “anything, anytime, anywhere” access to information (when we want it, of course!)

  4. Research Interests • Wireless Internet Technologies • Web Performance • Network Traffic Measurement • Workload Characterization • Traffic Modeling • Network Simulation • Network Emulation

  5. Wireless Internet Technologies • Mobile devices (e.g., notebooks, laptops, PDAs, cell phones, wearable computers) • Wireless network access • Bluetooth (1 Mbps, up to 3 meters) • IEEE 802.11b (11 Mbps, up to 100 meters) • IEEE 802.11a (55 Mbps, up to 20 meters) • Operating modes: • Infrastructure mode (access point) • Ad hoc mode • Classroom area networks (CRAN)

  6. Example: • Multi-hop “ad hoc” networking Gwen Carey

  7. Example: • Multi-hop “ad hoc” networking Gwen Carey

  8. Example: • Multi-hop “ad hoc” networking Gwen Carey

  9. Example: • Multi-hop “ad hoc” networking Gwen Carey

  10. Example: • Multi-hop “ad hoc” networking Gwen Carey

  11. Example: • Multi-hop “ad hoc” networking Gwen Carey

  12. Example: • Multi-hop “ad hoc” networking Gwen Carey

  13. Example: • Multi-hop “ad hoc” networking Gwen Carey

  14. Example: • Multi-hop “ad hoc” networking Gwen Carey

  15. Example: • Multi-hop “ad hoc” networking Gwen Carey

  16. Web Performance • Explore techniques to improve the performance and scalability of the Web • Examples: • Clustered Web servers • Load balancing policies • Web prefetching strategies • Web proxy caching architectures • Improvements to HTTP and TCP protocols

  17. Network Traffic Measurement • Collect and analyze packet-level traces from a live network

  18. Network Traffic Measurement • Collect and analyze packet-level traces from a live network, using special equipment 101101

  19. Network Traffic Measurement • Collect and analyze packet-level traces from a live network, using special equipment • Process traces, statistical analysis • Diagnose performance problems (network, protocol, application) 101101

  20. Example Trace 0.000000 192.168.1.201 -> 192.168.1.200 60 TCP 4105 80 1315338075 : 1315338075 0 win: 5840 S 0.003362 192.168.1.200 -> 192.168.1.201 60 TCP 80 4105 1417888236 : 1417888236 1315338076 win: 5792 SA 0.009183 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338076 : 1315338076 1417888237 win: 5840 A 0.010854 192.168.1.201 -> 192.168.1.200 127 TCP 4105 80 1315338076 : 1315338151 1417888237 win: 5840 PA 0.014309 192.168.1.200 -> 192.168.1.201 52 TCP 80 4105 1417888237 : 1417888237 1315338151 win: 5792 A 0.049848 192.168.1.200 -> 192.168.1.201 1500 TCP 80 4105 1417888237 : 1417889685 1315338151 win: 5792 A 0.056902 192.168.1.200 -> 192.168.1.201 1500 TCP 80 4105 1417889685 : 1417891133 1315338151 win: 5792 A 0.057284 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338151 : 1315338151 1417889685 win: 8688 A 0.060120 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338151 : 1315338151 1417891133 win: 11584 A 0.068579 192.168.1.200 -> 192.168.1.201 1500 TCP 80 4105 1417891133 : 1417892581 1315338151 win: 5792 PA 0.075673 192.168.1.200 -> 192.168.1.201 1500 TCP 80 4105 1417892581 : 1417894029 1315338151 win: 5792 A 0.076055 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338151 : 1315338151 1417892581 win: 14480 A 0.083233 192.168.1.200 -> 192.168.1.201 1500 TCP 80 4105 1417894029 : 1417895477 1315338151 win: 5792 A 0.096728 192.168.1.200 -> 192.168.1.201 1500 TCP 80 4105 1417896925 : 1417898373 1315338151 win: 5792 A 0.103439 192.168.1.200 -> 192.168.1.201 1500 TCP 80 4105 1417898373 : 1417899821 1315338151 win: 5792 A 0.103780 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338151 : 1315338151 1417894029 win: 17376 A 0.106534 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338151 : 1315338151 1417898373 win: 21720 A 0.133408 192.168.1.200 -> 192.168.1.201 776 TCP 80 4105 1417904165 : 1417904889 1315338151 win: 5792 FPA 0.139200 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338151 : 1315338151 1417904165 win: 21720 A 0.140447 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338151 : 1315338151 1417904890 win: 21720 FA 0.144254 192.168.1.200 -> 192.168.1.201 52 TCP 80 4105 1417904890 : 1417904890 1315338152 win: 5792 A

  21. + Key: X Data Packet + Ack Packet X + X + X + X + X SeqNum + X + X X + + X + X + X + X + X + X Time

  22. Workload Characterization • Try to understand the salient features of network, protocol, application, and user behaviour on the Internet • Example: Web server workloads [Arlitt96] • Zipf-like document referencing behaviour • Lots of “one-time” referencing of documents • Heavy-tailed file size distributions • Self-similar network traffic profile

  23. Traffic Modeling • Construct programs and statistical models that capture the empirically-observed network traffic behaviours • Allows flexible, controlled, repeatable generation of workloads for experiments • Examples: • Web client workload model • MPEG compressed video model • Self-similar Ethernet LAN traffic model • WebTraff GUI: Web proxy workload generator

  24. Network Simulation • Use computer simulation to study the packet-level behaviour of the Internet, its protocols, its applications, and its users • Examples: • Improving Web performance over ADSL • Understanding the effects of user mobility on Mobile IP routing and protocol performance • Studying the design, scalability, and performance of Web server and Web proxy caching architectures

  25. Network Emulation • A hybrid performance evaluation methodology that combines simulation and experimental implementation • A simulator that “talks back” (IP packets) • Examples: • Web server benchmarking • Wide Area Network (WAN) emulation • Web proxy cache performance • Distributed applications (Internet games)

  26. Summary • Wireless Internet Performance Lab (UofC) • Experimental Laboratory for Internet Systems and Applications (UofS/UofC,CFI) • iCORE Research Team: • Five full-time research staff (Web, perf. eval., simulation, wireless, traffic modeling, network measurement) plus 7 graduate students • Research Collaborations: • UofC, UofA, UofS, TRLabs, CS/ECE • HP, Telus Mobility, SaskTel, Sun, Nortel… • Do cool, “hands on”, industrially-relevant, applied, practical, and exciting stuff!!

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