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1 Wireless Internet Performance Research Carey Williamson iCORE Professor Department of Computer Science University of Calgary www.cpsc.ucalgary.ca/~carey carey@cpsc.ucalgary.ca
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2 Internet Protocol Stack r Application: supporting network applications and end-user services m FTP, SMTP, HTTP, DNS, NTP r Transport: end to end data transfer m TCP, UDP r Network: routing of datagrams from source to destination m IPv4, IPv6, BGP, RIP, routing protocols r Data Link: hop by hop frames, channel access, flow/error control m PPP, Ethernet, IEEE 802.11b r Physical: raw transmission of bits Application Transport Network Data Link Physical 001101011...
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3 The Wireless Web r The emergence and convergence of these technologies enable the “wireless Web” m the wireless classroom m the wireless workplace m the wireless home r My iCORE mandate: design, build, test, and evaluate wireless Web infrastructures r Holy grail: “anything, anytime, anywhere” access to information (when we want it, of course!)
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4 Research Interests r Wireless Internet Technologies r MAC Protocol Design r Network Traffic Measurement r Workload Characterization r Traffic Modeling r Network Simulation r Web Performance
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5 Wireless Internet Technologies r Mobile devices (e.g., notebooks, laptops, PDAs, cell phones, wearable computers) r Wireless network access m Bluetooth (1 Mbps, up to 3 meters) m IEEE 802.11b (11 Mbps, up to 100 meters) m IEEE 802.11a (55 Mbps, up to 20 meters) r Operating modes: m Infrastructure mode (access point) m Ad hoc mode
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6 Example: Infrastructure Mode Carey Internet Access Point (AP) cnn.com
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7 Example: Ad Hoc Mode r Multi-hop “ad hoc” networking Carey Sean
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8 Example: Ad Hoc Mode r Multi-hop “ad hoc” networking Carey Sean
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9 Example: Ad Hoc Mode r Multi-hop “ad hoc” networking Carey Sean
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10 Example: Ad Hoc Mode r Multi-hop “ad hoc” networking Carey Sean
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11 MAC Protocol Design r Identify performance problems in wireless Medium Access Control (MAC) protocols r Examples: IEEE 802.11b WLANs m Unfairness problems [Xiao MSc 2004] m Effects of node mobility [Bai 2004] m “Bad Apple” phenomenon [Cao 2004] m TCP on multi-hop ad hoc networks [Gupta 2004] m Multi-channel MAC protocols [Kuang 2004] m Multi-rate multi-channel protocols [Wu 2005]
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12 Network Traffic Measurement r Collect and analyze packet-level traces from a live network, using special equipment r Process traces, statistical analysis r Diagnose performance problems (network, protocol, application) 101101
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13 Example: tcpdump 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
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14 Example: TELUS Mobility Project r Data Template and Example – XYZ Platform Code Definition ============== 20FSCH Data Rate 21FSCH Data Burst Start Time 22FSCH Data Burst End Time 200FSCH Active Set Report Time 21x FSCH Active Set Cell ID ('x' is a number) 22x FSCH Active Ste Sector ID ('x' is a number) 30 RSCH Data Rate 31RSCH Data Burst Start Time 32 RSCH Data Burst End Time 300RSCH Active Set Report Time 31xRSCH Active Set Cell ID ('x' is a number) 32xRSCH Active Ste Sector ID ('x' is a number) 40FCH Data Start Time 41FCH Data End Time 100FCH Active Set Report Time 11xFCH Active Set Cell ID ('x' is a number) 12x FCH Active Ste Sector ID ('x' is a number) 50IMSI 60Frequency 70SID 50000006048421781 510x804ce0401aa89666 7016422 60384 402004041375333.680 412004041375443.020 2002004041375337.940 21132 2213 2016 212004041375338.200 222004041375339.200 204 212004041375357.860 222004041375357.880 2016 212004041375371.720 222004041375372.700
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15 Workload Characterization r Try to understand the salient features of network, protocol, application, and user behaviour on the Internet r Example: Web server workloads [Arlitt96] m Zipf-like document referencing behaviour m Lots of “one-time” referencing of documents m Heavy-tailed file size distributions m Self-similar network traffic profile m Session duration and call arrival process
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16 Traffic Modeling r Construct programs and statistical models that capture the empirically-observed network traffic behaviours r Allows flexible, controlled, repeatable generation of workloads for experiments r Examples: m Web client workload model m MPEG compressed video model m Self-similar Ethernet LAN traffic model m WebTraff GUI: Web proxy workload generator
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17 Example: Web Workload Generation
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18 Network Simulation r Use computer simulation to study the packet-level behaviour of the Internet, its protocols, its applications, and its users r Examples: m Improving Web performance over ADSL m Understanding the effects of user mobility on Mobile IP routing and protocol performance m Studying the design, scalability, and performance of Web server and Web proxy caching architectures
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19 Web Performance r Explore techniques to improve the performance and scalability of the Web r Examples: m Clustered Web servers m Load balancing policies m Web prefetching strategies m Web proxy caching architectures m Improvements to HTTP and TCP protocols
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20 Web Server Client 1 Client 2 Client 3 Client C... Example: Web Server Benchmarking
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21 Summary r Wireless Internet Performance Lab (UofC) r Experimental Laboratory for Internet Systems and Applications (UofS/UofC,CFI) r iCORE Research Team: m Five full-time research staff (Web, perf. eval., simulation, wireless, traffic modeling, network measurement) plus 8 graduate students r Research Collaborations: m UofC, UofA, UofS, TRLabs, CS/ECE m HP, TELUS Mobility, SaskTel, Nortel… r Industrially-relevant experimental research on network protocol performance
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