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Mining the Web Traces: Workload Characterization, Performance Diagnosis, and Applications

Mining the Web Traces: Workload Characterization, Performance Diagnosis, and Applications. Lili Qiu Microsoft Research Performance’2002, Rome, Italy September 2002. Motivation. Why do we care about Web traces? Content providers How do users come to visit the Web site?

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Mining the Web Traces: Workload Characterization, Performance Diagnosis, and Applications

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  1. Mining the Web Traces:Workload Characterization, Performance Diagnosis, and Applications Lili Qiu Microsoft Research Performance’2002, Rome, Italy September 2002

  2. Motivation • Why do we care about Web traces? • Content providers • How do users come to visit the Web site? • Why do users leave the Web site? Is poor performance the cause for this? • What content are users interested in? • How do users’ interest vary in time? • How do users’ interest vary across different geographical regions? • Where are the performance bottlenecks?

  3. Motivation (Cont.) • Web hosting companies • Accounting & billing • Server selection • Provisioning server farms: where to place servers • ISPs • How to save bandwidth by storing proxy caches? • Traffic engineering & provisioning • Researchers • Where are the performance bottlenecks? • How to improve Web performance? • Examples: Traffic measurements have influenced the design of HTTP (e.g., persistent connections and pipeline), TCP (e.g., initial congestion window)

  4. Tutorial Outline • Background • Web workload characterization • Performance diagnosis • Applications of traces • Bibliography

  5. Part I: Background • Web software components • Web semantic components • Web protocols • Types of Web traces

  6. Web Software Components • Web clients • An application that establishes connections to send Web requests • E.g., Mosaic, Netscape Navigator, Microsoft IE • Web servers • An application that accepts connections to service requests by sending back responses • E.g., Apache, IIS • Web proxies (optional) • Web replicas (optional) Internet proxy replica proxy replica proxy WebServers WebClients

  7. Web Semantic Components • Uniform Resource Identifier (URI) • An identifier for a Web resource • Name of protocol: http, https, ftp, .. • Name of the server • Name of the resource on the server • e.g., http://www.foobar.com/info.html • Hypertext Markup Language (HTML) • Platform-independent styles (indicated by markup tags) that define the various components of a Web document • Hypertext Transfer Protocol (HTTP) • Define the syntax and semantics of messages exchanged between Web software components

  8. Example of a Web Transaction DNS server 1. DNS query 2. Setup TCP connection 3. HTTP request 4. HTTP response Browser Web server

  9. Internet Protocol Stack Application layer: application programs (HTTP, Telnet, FTP, DNS) Transport layer: error control + flow control (TCP,UDP) Network layer: routing (IP) Datalink layer: handle hardware details (Ethernet, ATM) Physical layer: moving bits (coaxial cable, optical fiber)

  10. Web Protocols HTTP messages HTTP HTTP TCP segments TCP TCP IP pkt IP pkt IP pkt IP IP IP IP Ethernet Ethernet Sonet Sonet Ethernet Ethernet Sonet link Ethernet Ethernet A picture taken from [KR01]

  11. Web Protocols (Cont.) • DNS [AL01] • An application layer protocol responsible for translating hostname to IP and vice versa (e.g., perf2002.uniroma2.it  160.80.2.140) • TCP [JK88] • A transport layer protocol that does error control and flow control • Hypertext Transfer Protocol (HTTP) • HTTP 1.0 [BLFF96] • The most widely used HTTP version • A “Stop and wait” protocol • HTTP 1.1 [GMF+99] • Adds persistent connections, pipelining, caching, compression

  12. HTTP 1.0 • HTTP request • Request = Simple-Request | Full-Request Simple-Request = "GET" SP Request-URI CRLF Full-Request = Request-Line; *( General/Request/Entity Header) ; CRLF [ Entity-Body ] ; Request-Line = Method SP Request-URI SP HTTP-Version CRLF Method = "GET" ;| "HEAD" ; | "POST" ;| extension-method • Example: GET /info.html HTTP/1.0

  13. HTTP 1.0 (Cont.) • HTTP response • Response = Simple-Response | Full-Response Simple-Response = [ Entity-Body ]Full-Response = Status-Line; *( General/Response/Entity Header ); CRLF [ Entity-Body ] ; • Example:HTTP/1.0 200 OKDate: Mon, 09 Sep 2002 06:07:53 GMTServer: Apache/1.3.20 (Unix) (Red-Hat/Linux) PHP/4.0.6Last-Modified: Mon, 29 Jul 2002 10:58:59 GMTContent-Length: 21748Content-Type: text/html…<21748 bytes of the current version of info.html>

  14. HTTP 1.1 • Connection management • Persistent connections [Mogul95] • Use one TCP connection for multiple HTTP requests • Pros: • Reduce the overhead of connection setup and teardown • Avoid TCP slow start • Cons: • head-of-line blocking • increase servers’ state • Pipeline [Pad95] • Send multiple requests without waiting for a response between requests • Pros: avoid the round-trip delay of waiting for each response • Cons: connection abortion is harder to deal with

  15. HTTP 1.1 (Cont.) • Caching • Continues to support the notion of expiration used in HTTP 1.0 • Add a cache-control header to handle the issues of cacheability and semantic transparency [KR01] • E.g., no-cache, only-if-cache, no-store, max-age, max-stale, min-fresh, … • Others • Range request • Content negotiation • Security • …

  16. Types of Web Traces • Application level traces • Server logs: CLF and ECLF formats • CLF format<remoteIP,remoteID,usrName,Time,request,responseCode,contentLength>e.g., 192.1.1.1, -, -, 8/1/2000, 10:00:00, “GET /news/index.asp HTTP/1.1”, 200, 3410 • Proxies logs: CLF and ECLF formats • Client logs: no standard logging formats • Packet level traces • Collection method: monitor a network link • Available tools: tcpdump, libpcap, netmon • Concerns: packet dropping, timestamp accuracy

  17. Types of Web Traces • Flow level traces • <srcIP, destIP, nextIP, input, output, pkts, bytes, sTime, fTime, srcPort, destPort, pad, flags, prot, tos, srcAs, destAs, srcMask, destMask, seq>

  18. Tutorial Outline • Background • Web workload characterization • Performance diagnosis • Applications of traces • Bibliography

  19. Part II: Web Workload Characterization • Overview • Content dynamics • Access dynamics • Common pitfalls • Case studies

  20. Overview • Process of trace analyses • Common analysis techniques • Common analysis tools • Challenges in workload characterization

  21. Process of trace analyses • Collect traces • where to monitor, how to collect (e.g., efficiency, privacy, accuracy) • Determine key metrics to characterize • Process traces • Draw inferences from the data • Apply the traces or insights gained from the trace analyses to design better protocols & systems

  22. Common Analysis Techniques - Statistics • Mean • Median • Variance and standard deviation • Geometric mean: less sensitive to outliers • Confidence interval • A range of values that has a specified probability of containing the parameter being estimated • Example: 95% confidence interval 10  x  20

  23. Common Analysis Techniques – Statistics (Cont.) • Cumulative distribution (CDF) • P(x  a) • Probability density function (PDF) • Derivative of CDF: f(x) = dF(x)/dx • Check for heavy tail distribution • Log-log complementary plot, and check its tail • Example: Pareto distribution If 2, distribution has infinite variance (a heavy tail)If 1, distribution has infinite mean

  24. Common Analysis Techniques – Data Fitting • Visually compare the empirical distribution with a standard distribution • Chi Squared tests [AS86,Jain91] • If , then two distributions are close, where • need enough samples • Kolmogorov-Smirnov tests [AS86,Jain91] • Compares two distributions by finding the maximum differences between two variables’ cumulative distribution functions

  25. Common Analysis Techniques – Data Fitting (Cont.) • Anderson-Darling Test [Ste74] • Modification of the Kolmogorov-Smirnov test, giving more weight to the tails • If A  critical value, two distributions are similar; otherwise they are not (F is CDF, and Yi are ordered data) • Quantile-quantile plots [AS86,Jain91] • Compare two distributions by plotting the inverse of the cumulative distribution function F-1(x) for two variables, and find best fitting line • If the slope of the line is close to 1, and y-intercept is close to 0, the two data sets are almost identically distributed

  26. Common Analysis Tools • Scripting languages • Perl, awk, UNIX shell scripts, VB • Databases • SQL, DB2, … • Statistics packages • Matlab, S+, R, SAS, … • Write our own low level programs • C, C++, C#, Java, …

  27. Challenges in Workload Characterization • Each of the Web components provides a limited perspective on the functioning of the Web • Workload characteristics vary both in space and in time Internet proxy replica proxy replica proxy Servers Clients

  28. Views from Clients • Capture clients’ requests to all servers • Pros • Know details of client activities, such as requests satisfied by browser caches, client aborts • The ability to record detailed information, as this does not impose significant load on a client browser • Cons • Need to modify browser software • Hard to deploy for a large number of clients

  29. Views from Web Servers • Capture most clients’ requests (excluding those satisfied by caches) to a single server • Pros • Relatively easy to deploy/change logging software • Cons • Requests satisfied by browser & proxy caches will not appear in the logs • May not log detailed information to ensure fast processing of client requests

  30. Views from Web Proxies • Depending on the proxy’s location • A proxy close to clients see requests from a a small client group to a large number of servers [KR00] • A proxy close to the servers see requests from a large client group to a small number of servers [KR00] • Pros • See requests from a diverse set of clients to a diverse set of servers, and determine the popularity ranking of different Web sites • Useful for studying caching policies • Ease of collection • Cons • Requests satisfied by browser caches will not appear in the logs • May not log detailed information to ensure fast processing of requests

  31. Workload Variation • Vary with measurement points • Vary with sites being measured • Information servers (news site), e-commercial servers, query servers, streaming servers, upload servers • US vs. Italy, … • Vary with the clients being measured • Internet clients vs. wireless clients • University clients vs. home users • US vs. Italy, … • Vary in time • Day vs. night • Weekday vs. weekend • Changes with new applications, recent events • Evolve over time, …

  32. Part II: Web Workload • Overview • Content dynamics • Access dynamics • Common pitfalls • Case studies

  33. Content Dynamics • File types • File size distribution • File update patterns • How often files are updated • How much files are updated

  34. File Types • Text files • HTML, plain text, … • Images • Jpeg, gif, bitmap, … • Applications • Javascript, cgi, asp, pdf, ps, gzip, ppt, … • Multimedia files • Audio, video

  35. File Size Distribution • Two definitions • D1: Size of all files on a Web server • D2: Size of all files transferred by a Web server • D1  D2, because some files can be transferred multiple times or not in completion and other files are not transferred • Studies show that the distribution of file sizes in both definitions exhibit heavy tails (i.e., P[F > x] ~ x-, 0    2)

  36. File Update Interval • Varies in time • Hot events and fast changing events require more frequent update, e.g., Worldcup • Varies across sites • Depending on server update policies & update tools • Depending on the nature of content (e.g., University sites have slower update rate than news sites) • Recent studies • Study of the proxy traces collected at DEC and AT&T in 1996 showed the rate of change depended on content type, top-level domains etc. [DFK+97] • Study of 1999 MSNBC logs shows that modification history yields a rough predictor of future modification interval [PQ00]

  37. Extent of Change upon Modifications • Varies in time • Different events trigger different amount of updates • Varies across sites • Depending on servers’ update policies and update tools • Depending on the nature of the content • Recent studies • Studies of 1996 DEC and AT&T proxy [MDF+97] and 1999 MSNBC log [PQ00] show that most file modifications are small  delta encoding can be very useful

  38. Part II: Web Workload • Motivation • Limitations of workload measurements • Content dynamics • Access Dynamics • File popularity distribution • Temporal stability • Spatial locality • User session and request arrivals & duration • Synthetic workload generation • Common pitfalls • Case studies

  39. Document Popularity • The Web requests follow Zipf-like distribution • Request frequency  1/i, where i is a document’s ranking • The value of  depends on the point of measurements • Between 0.6 and 1 for client traces and proxy traces • Close to or larger than 1 for server traces [ABC+96, PQ00] • The value of  varies over time (e.g., larger  during hot events)

  40. Impact of the value  • Larger  means more concentrated accesses on popular documents caching is more beneficial • 90% of the accesses are accounted by • Top 36% files in proxy traces [BCF+99, PQ00] • Top 10% files in small departmental server logs reported in [AW96] • Top 2-4% files in MSNBC traces

  41. Temporal Stability • Metrics • Coarse-grained: likely duration that a current popular file remains popular • e.g., overlap between the set of popular documents on day 1 and day 2 • Fine-grained: how soon a requested file will be requested again • e.g., LRU stack distance [ABC+96] File 5 File 2 File 4 File 5 Stack distance = 4 File 3 File 4 File 2 File 3 File 1 File 1

  42. Spatial Locality • Refers to if users in the same geographical location or same organization tend to request the same documents • E.g., degree of a request locally shared vs. globally shared

  43. Spatial Locality (Cont.) Domain membership is significant except when there is a “hot” event of global interest

  44. User Request Arrivals & Duration • User workload at three levels • Session: a consecutive series of requests from a user to a Web site • Click: a user action to request a page, submit a form, etc. • Request: each click generates one or more HTTP requests • Exponential distribution [LNJV99,KR01] • Session duration • Heavy-tail distribution [KR01] • # clicks in a session, most in the range of 4-6 [Mah97] • # embedded references in a Web page • Think time: time between clicks • Active time: time to download a Web page and its embedded images

  45. Common Pitfalls • Trace analyses are all about writing scripts & plotting nice graphs • Challenges • Trace collection: where to monitor, how to collect (e.g., efficiency, privacy, accuracy) • Identify important metrics, and understand why they are important • Sound measurements require disciplines [Pax97] • Dealing with errors and outliers • Draw implications from data analyses • Understanding the limitation of the traces • No representative traces: workload changes in time and in space • Try to diversify data sets (e.g., collect traces at different places and different sites) before jumping into conclusions • Draw inferences more than what data show

  46. Part II: Web Workload • Motivation • Limitations of workload measurements • Content dynamics • Access dynamics • Common pitfalls • Case studies • Boston University client log study • UW proxy log study • MSNBC server log study • Mobile log study

  47. Case Study I: BU Client Log Study • Overview • One of the few client log studies • Analyze clients’ browsing pattern and their impact on network traffic [CBC95] • Approaches • Trace collection • Modify Mosaic and distribute it to machines in CS Dept. at Boston Univ. to collect client traces in 1995 • Log format: <client machine, request time, user id, URI, document size, retrieval time> • Data analyses • Distribution of document size, document popularity • Relationship between retrieval latency and response size • Implications on caching strategies

  48. Major Findings • Power law distributions • Distribution of document sizes • Distribution of user requests for documents • # requests to documents as a function of their popularity • Caching strategies should take into account of document size (i.e., give preference to smaller documents)

  49. Case Study II: UW Proxy Log Study • Overview • Proxy traces collected at the University of Washington and Microsoft • Approaches [WVS+99a, WVS+99b] • Trace collection: deploy a passive network sniffer between the Univ. of Washington and the rest of the Internet in May 1999 • Set well-defined objectives • Understand the extent of document sharing within an organization and across different organizations • Understand the performance benefit of cooperative proxy caching

  50. Major Findings • Members of an organization are more likely to request the same documents than a random set of clients • Most popular documents are globally popular • A large percentage (40%) of requests are uncachable • Cooperative caching is most beneficial for small organizations • Cooperative caching among large organizations yield minor improvement if any

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