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Network-Level Impacts on User-Level Web Performance

Network-Level Impacts on User-Level Web Performance. Carey Williamson Nayden Markatchev University of Calgary. Blessing made the Internet available to the masses shields users from the low-layer technical details of networking

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Network-Level Impacts on User-Level Web Performance

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  1. Network-Level Impacts on User-Level Web Performance Carey Williamson Nayden Markatchev University of Calgary SPECTS 2003

  2. Blessing made the Internet available to the masses shields users from the low-layer technical details of networking provides seamless exchange of information, in a time-independent, location-independent, and platform-independent manner Curse made the Internet available to the masses placed a lot of stress on the Internet infrastructure traffic volume, sustained growth demands on the TCP/IP protocol suite (i.e.,TCP is not really a good “fit” for Web traffic demands) Introduction “The Web has been both a blessing and a curse.” -- CLW 2001 SPECTS 2003

  3. Related Work: TCP and the Web • Persistent-connection HTTP [Mogul 1995] • Larger TCP initial window size [Allman et al 1998] • TCP “fast start” to reduce Web transfer latency [Padmanabhan/Katz 1998] • Parallel (concurrent) TCP connections supported in most Web browsers today (e.g., 4) • Ensemble-TCP to manage aggregation of TCP connections to same dest. [Eggert et al 2000] • Rate-based pacing of TCP packets for the Web [Aggarwal et al 2000] [Ke/Williamson 2000] • Context-aware TCP/IP [Williamson/Wu 2002] SPECTS 2003

  4. Most of the current Web performance literature is focused on either: Web caching simulation studies (i.e., with an application-layer view, focusing on hit ratios, but ignoring network-level issues and protocol effects); or TCP performance studies (i.e., packet-level studies, but often focusing on throughput for bulk transfers, rather than response times for (short) Web transfers) Our Objective: To explore the relationships between TCP, network-level effects, Web caching, and user-perceived Web response time Motivation SPECTS 2003

  5. Research Methodology Overview • Network simulation (ns2) • Synthetic Web workloads (WebTraff) • Simple network model: • two-level Web proxy caching hierarchy • settable parameters for link capacity, propagation delay, cache hit ratio, etc • Packet-level simulation study (TCP Reno) • Performance metric: object transfer time SPECTS 2003

  6. Network Model Web Server C3 d3 Proxy2 C2 d2 Proxy1 C1 d1 Clients SPECTS 2003

  7. Network Model Web Server (Hit at Proxy1) Proxy2 Proxy1 Clients SPECTS 2003

  8. Network Model Web Server (Hit at Proxy2) Proxy2 Proxy1 Clients SPECTS 2003

  9. Network Model Web Server (Download from server) Proxy2 Proxy1 Clients SPECTS 2003

  10. Simulation Model Assumptions • Two-level Web proxy caching hierarchy • All Web content is cacheable static content • Data transfers are unidirectional toward the clients (i.e., we ignore the HTTP request step) • One-way TCP model (i.e., models the data transfer only, using DATA/ACK; no SYN/FIN) • TCP Reno, with segment size of 512 bytes • Proxy caches behave as store-and-forward routers (on a per-packet basis) SPECTS 2003

  11. Simulation Methodology • Multi-step process: • Workload generation using WebTraff (makes a time-ordered sequence of 5000 Web object transfer sizes, with desired request arrival rate) • Modify workload file to randomly associate transfers with either Proxy1, Proxy2, or Server based on desired cache hit ratios (HR1, HR2) • Use the ns2 network simulator to model the TCP transfers on the desired network model SPECTS 2003

  12. Experimental Design Factors Levels Link Capacity C(Mbps) 10, 100, 1000 Propagation Delay d (msec) 1, 5, 10, 30, 60 Request Arrival Rate (req/sec) 10, 20, 40, 80 Child Proxy Hit Ratio HR1 20%, 30%, 40% Parent Proxy Hit Ratio HR2 7%, 10%, 15% Network Parameters Workload Parameters Full-factorial experiment (540 possible combinations) Performance metric: TCP transfer time for each Web object download (plotted versus transfer size) SPECTS 2003

  13. Web Workload Model • 5000 HTTP transfers synthetically generated by the WebTraff tool [Markatchev/Williamson 2002] • Poisson arrival process assumed for Web requests • Four different request arrival rates considered: • Light: 10 req/sec (approx. 0.77 Mbps offered load) • Moderate: 20 req/sec (approx. 1.54 Mbps offered load) • Medium: 40 req/sec (approx. 3.08 Mbps offered load) • Heavy: 80 req/sec (approx. 6.16 Mbps offered load) SPECTS 2003

  14. Web Workload Characteristics SPECTS 2003

  15. Baseline Scenario • Link Capacity • C1 = C2 = C3 = 10 Mbps • Propagation Delay • d1 = 1 msec; d2 = 5 msec; d3 = 30 msec • Hit Ratios • HR1 = 40%; HR2 = 15% • Request Arrival Rate • Light: 10 requests/sec SPECTS 2003

  16. Simulation Results (Baseline Scenario) SPECTS 2003

  17. Simulation Results (Baseline Scenario) SPECTS 2003

  18. Simulation Results (Baseline Scenario) SPECTS 2003

  19. Simulation Results (Baseline Scenario) “slower” “faster” SPECTS 2003

  20. Simulation Results (Baseline Scenario) Queueing Delays SPECTS 2003

  21. Simulation Results (Baseline Scenario) Packet Losses/Retransmissions SPECTS 2003

  22. Results Interpretation • TCP slow start is evident (for large RTT) • The “width” of steps increases exponentially • The vertical separation reflects propagation delay component of RTT • Queuing delays, packet losses, timeouts, and retransmissions manifest themselves as deviations from the normal structure SPECTS 2003

  23. Effects of Network Link Capacity • To model current network infrastructures, we considered four sets of link capacities: • C1 =10 Mbps, C2 =10 Mbps, C3 =10 Mbps (baseline) • C1 =100 Mbps, C2 =10 Mbps, C3 =10 Mbps • C1 =100 Mbps, C2 =100 Mbps, C3 =10 Mbps • C1 =1000 Mbps, C2 =100 Mbps, C3 =10 Mbps • This models increasingly faster client network access to the Internet, while the WAN backbone to the server remains slow (10 Mbps) SPECTS 2003

  24. Results for Link CapacityC1 = 10 Mbps (baseline) SPECTS 2003

  25. Results for Link CapacityC1 = 100 Mbps (upgrade) SPECTS 2003

  26. Effect of Propagation Delay • Values for propagation delay • d1 = 1 msec, d2 = 5 msec, d3 = 30 msec • d1 = 1 msec, d2 = 5 msec, d3 = 60 msec • d1 = 1 msec, d3 = 10 msec, d3 = 30 msec • d1 = 1 msec, d2 = 10 msec, d3 = 60 msec • Representing LAN, MAN, WAN scenarios SPECTS 2003

  27. Propagation Delay (d2 = 5 msec) SPECTS 2003

  28. Propagation Delay (d2 = 10 msec) SPECTS 2003

  29. Effect of Request Arrival Rate • Vary the offered load: • 10 requests/sec • 20 requests/sec • 40 requests/sec • 80 requests/sec • Makes network more and more congested SPECTS 2003

  30. Effect of Request Arrival Rate (Light Offered Load: 10 req/sec) SPECTS 2003

  31. Effect of Request Arrival Rate (Moderate Offered Load: 20 req/sec) SPECTS 2003

  32. Effect of Request Arrival Rate (Medium Offered Load: 40 req/sec) SPECTS 2003

  33. Effect of Request Arrival Rate (Heavy Offered Load: 80 req/sec) SPECTS 2003

  34. Effect of Cache Hit Ratio • Vary the Cache Hit Ratio at each of the Web proxy caches in the simulated network • “Good”: HR1 = 40%, HR2 = 15% (baseline) • “Average”: HR1 = 30%, HR2 = 10% • “Poor”: HR1 = 20%, HR2 = 7% • Assess user-perceived Web response time for fairly realistic range of possible cache hit ratios, and consideration of “cache filter effects” SPECTS 2003

  35. Effect of Cache Hit Ratio(“Good” HR1 = 40%; HR2 = 15%) SPECTS 2003

  36. Effect of Cache Hit Ratio(“Average” HR1 = 30%; HR2 = 10%) SPECTS 2003

  37. Effect of Cache Hit Ratio(“Poor” HR1 = 20%; HR2 = 7%) SPECTS 2003

  38. Effect of Cache Hit Ratio SPECTS 2003

  39. Effect of Cache Management Policy • Suppose that the two caches are coordinated using a size-based thresholding policy • One cache for “small” items • One cache for “large” items • Is this a good idea? • Scenario considered: • Child Proxy: items <= 8 KB • Parent Proxy: items > 8 KB • Same hit ratios as in baseline SPECTS 2003

  40. Cache Management Policies(Default Policy; C1 = 10 Mbps) SPECTS 2003

  41. Cache Management Policies(Threshold Policy; C1 = 10 Mbps) SPECTS 2003

  42. Cache Management Policies(Default Policy; C1 = 100 Mbps) SPECTS 2003

  43. Cache Management Policies(Threshold Policy; C1 = 100 Mbps) SPECTS 2003

  44. Summary of Simulation Results for Cache Management Policies SPECTS 2003

  45. Summary and Conclusions • Packet-level network simulation study of TCP effects on user-perceived Web perf. • Link capacity, propagation delay, network congestion, and TCP protocol behaviors can all have significant impact on the user-perceived Web response time • Relationship between Web cache hit ratio and user-perceived response time tricky • Cache management and placement hard! SPECTS 2003

  46. The End! • Thanks for your attention! • For more information: • Email: {nayden,carey}@cpsc.ucalgary.ca SPECTS 2003

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