Internet Performance Dynamics.ppt

1、Internet Performance Dynamics,Boston University Computer Science Department,http:/cs-people.bu.edu/barford/,Fall, 2000,Paul Barford,Motivation,What are the root causes of long response times in wide area services like the Web? Servers? Networks? Server/network interaction?,A Challenge,Histograms of

2、file transfer latency for 500KB files transferred between Denver and Boston,Day 1,Day 2,HS mean = 8.3 sec. LS Mean = 13.0 sec.,HS mean = 5.8 sec. LS Mean = 3.4 sec.,Precise separation of server effects from network effects is difficult,HS mean = 8.3 sec. LS mean = 13.0 sec.,HS,LS,HS,LS,HS mean = 5.8

3、 sec. LS mean = 3.4 sec.,What is needed?,A laboratory enabling detailed examination of Web transactions (Web “microscope”) Wide Area Web Measurement (WAWM) project testbed Technique for analyzing transactions to separate and identify causes of delay Critical path analysis of TCP,Web Transactions “un

4、der a microscope”,WebServer,Distributed Clients,Global Internet,Generating Realistic Server Workloads,Approaches: Trace-based: Pros: Exactly mimics known workload Cons: “black box” approach, cant easily change parameters of interest Analytic: synthetically create a workload Pros: Explicit models can

5、 be inspected and parameters can be varied Cons: Difficult to identify, collect, model and generate workload components,SURGE: Scalable URL Reference Generator,Analytic Web workload generator Based on 12 empirically derived distributions Explicit, parameterized models Captures “heavy-tailed” (highly

6、 variable) properties of Web workloads SURGE components: Statistical distribution generator Hyper Text Transfer Protocol (HTTP) request generator Currently being used at over 130 academic and industrial sites world wide Adopted by W3C for HTTP-NG testbed,Seven workload characteristics captured in SU

7、RGE,Characteristic Component Model System Impact,File Size Base file - body Lognormal File System *Base file - tail Pareto *Embedded file Lognormal *Single file1 Lognormal *Single file 2 Lognormal * Request Size Body Lognormal Network *Tail Pareto * Document Popularity Zipf Caches, buffers Temporal

8、Locality Lognormal Caches, buffers OFF Times Pareto * Embedded References Pareto ON Times * Session Lengths Inverse Gaussian Connection times,* Model developed during the SURGE project,BF,EF1,EF2,Off time,SF,Off time,BF,EF1,HTTP request generator,Supports both HTTP/1.0 and HTTP/1.1 ON/OFF thread is

9、a “user equivalent”,SURGE Client System,SURGE Client System,SURGE Client System,Network,ON/OFF Thread,ON/OFF Thread,ON/OFF Thread,Web Server System,SURGE and SPECWeb96 exercise servers very differently,Surge,SPECWeb96,SURGEs flexibility allows easy experimentation,HTTP/1.0,HTTP/1.1,Web Transactions

10、“under a microscope”,WebServer,Distributed Clients,Global Internet,WAWM Infrastructure,13 clients distributed around the global Internet Execute transactions of interest One server cluster at BU Local load generators running SURGE enable server to be placed under any load condition Active and passiv

11、e measurements from both server and clients Packet capture via “tcpdump” GPS timers,WAWM client systems,Harvard University, MA Purdue University, IN University of Denver, CO ACIRI, Berkeley, CA HP, Palo Alto, CA University of Saskatchewan, Canada University Federal de Minas Gerais, Brazil University

12、 Simon Bolivar, VenezuelaEpicRealm - Dallas, TX EpicRealm Atlanta, GA EpicRealm - London, England EpicRealm - Tokyo, JapanInternet2/SurveyorOthers?,What is needed?,A laboratory enabling detailed examination of Web transactions (Web “microscope”) Wide Area Web Measurement (WAWM) project testbed Techn

13、ique for analyzing transactions to separate and identify causes of delay Critical path analysis of TCP,Identifying root causes of response time,Delays can occur at many points along the end-to-end path simultaneously Pinpointing where delays occur and which delays matter is difficult Our goal is to

14、identify precisely the determiners of response time in TCP transactions,Client,Router 1,Router 2,Router 3,Server,Critical path analysis (CPA) for TCP transactions,CPA identifies the precise set of events that determine execution time of a distributed application Web transaction response time Decreas

15、ing duration of any event on the CP decreases response time not true for events off the CP Profiling the CP for TCP enables accurate assignment of delays to: Server delay Client delay Network delay (propagation, network variance and drops) Applied to HTTP/1.0 Could apply to other applications (eg. F

16、TP),Window-based flow control in TCP,Client,Server,1 or more data packets,ACK packet,Client,Server,D,D,D,D,D,D,D,D,D,D,A,A,A,D,A,A,D,D,D,D,System Time line Graph,D,D,A,A,D,D,A,A,A,D,D,D,D,D,D,D,D,D,D,D,TCP flows as a graph,Vertices are packet departures or arrivals Data, ACK, SYN, FIN Directed edges

17、 reflect Lamports “happens before” relation On client or server or over the network Weights are elapsed time Assumes global clock synchronization Profile associates categories with edge types Assignment based on logical flow,tcpeval,Inputs are “tcpdump” packet traces taken at end points of transacti

18、ons Generates a variety of statistics for file transactions File and packet transfer latencies Packet drop characteristics Packet and byte counts per unit time Generates both timeline and sequence plots for transactions Generates critical path profiles and statistics for transactions Freely distribu

19、ted,Implementation Issues,tcpeval must recreate TCP state at end points as packets arrive Capturing packets at end points makes timer simulation unnecessary “Active round” must be maintained Packet filter problems must be addressed Dropped packets Added packets Out of order packets tcpeval works acr

20、oss platforms for RFC 2001 compliant TCP stacks,CPA results for 1KB file,Latency is dominated by server load for BU to Denver path,6 packets are typically on the critical path,CP time line diagrams for 1KB file,Low Server Load,High Server Load,CPA results for 20KB file,Both server load and network e

21、ffects are significant,14 packets are typically on the critical path,The Challenge,Histograms of file transfer latency for 500KB files transferred between Denver and Boston,Day 1,Day 2,HS mean = 8.3 sec. LS Mean = 13.0 sec.,HS mean = 5.8 sec. LS Mean = 3.4 sec.,HS mean = 8.3 sec. LS mean = 13.0 sec.

22、,HS,LS,HS,LS,HS mean = 5.8 sec. LS mean = 3.4 sec.,CPA results for 500KB file,Latency is dominated by network effects,Day 1,Day 2,56 packets are typically on the critical path,Active versus Passive Measurements,Understanding active (Zing) versus passive (tcpdump) network measurements Figure shows ac

23、tive measures are a poor predictor of TCP performance Goal is to be able to predict TCP performance using active measurements,Related work,Web performance characterization Client studies Catledge95,Crovella96 Server studies Mogul95, Arlitt96 Wide area measurements NPD Paxson97, Internet QoS Huitema0

24、0, Keynote Systems Inc. TCP analysis TCP modeling Mathis97, Padhye98,Cardwell00 Graphical TCP analysis Jacobson88, Brakmo96 Automated TCP analysis Paxson97 Critical path analysis Parallel program execution Yang88, Miller90 RPC performance evaluation Schroeder89,Conclusions,Using SURGE, WAWM can put

25、realistic Web transactions “under a microscope” Complex interactions between clients, the network and servers in the wide area can lead to surprising performance Complex packet transactions can be effectively understood using CPA CP profiling of BU to Denver transactions allowed precise assignment o

26、f delays Latency for small files is dominated by server load Latency for large files is dominated by network effects Relationship between active and passive measurement is not well understood Future work lots of things to do!,Acknowledgements,Mark Crovella Vern Paxson, Anja Feldmann, Jim Pitkow, Drue Coles, Bob Carter, Erich Nahum, John Byers, Azer Bestavros, Lars Kellogg-Stedman, David Martin Xerox, Inc., EpicRealm Inc., Internet2 Michael Mitzenmacher, Kihong Park, Carey Williamson, Virgilio Almeida, Martin Arlitt,