Software and Systems Research Center, Bell Labs, Murray Hill July 26, 1996 Prefetching Techniques for the Web Azer Bestavros Computer Science Departme

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1 Networking and Distributed Systems Research Center, Bell Labs, Murray Hill July 25, 1996 Taming the WWW From Web Measurements to Web Protocol Design Azer Bestavros Computer Science Department Boston University Thursday July 25 th,

2 Software and Systems Research Center, Bell Labs, Murray Hill July 26, 1996 Prefetching Techniques for the Web Azer Bestavros Computer Science Department Boston University Friday July 26 th,

3 Overview of Oceans Projects Client-based Studies (Client Caching Protocols) Prefetching Protocols (Dynamic Server Selection Protocols) Server-based Studies (Server Caching Protocols) Dissemination Protocols Speculative Service Protocols Performance Modeling Studies Web Trac Characteristics (Web Access Patterns Characteristics) 3

4 Locality of Reference in a Client-Server Environment Locality of Reference Flavors Temporal: A document accessed frequently in the past is likely to be accessed again in the future. Spatial: A document \neighboring" a recently accessed document is likely to be accessed in the future. Geographical: A document accessed by a client is likely to be accessed in the future by \neighboring" clients. 4

5 Locality of Reference in a Client-Server Environment How to Capitalize on it? On the client side, use \caching" and \prefetching" (e.g. Distributed le systems, Sun NFS, AFS, [Standberg 1985, Morriss 1986, Howard 1988], Proxy caching [Danzig 1993, Acharya 1993, Papadimitriou 1994], Cooperative client caching [Blaze 1993, Dahlin 1994]). On the server side, use \information dissemination" [Bestavros 1994], \geographical caching" [Braunh and Clayh 1994], \speculative service" [Bestavros 1995], \geographical push caching" [Gwertzman and Seltzer 1995]. Motive The scalability of Internet services hinges on ecient distribution and partitioning of system resources to reduce the amount of data that must be moved. 5

6 Information Caching versus Information Dissemination Information Caching Initiated by a client or a group of clients. Geared towards reducing service time. Relies on temporal locality of client reference patterns. Ensuring consistency is expensive. Information Dissemination Initiated by servers. Geared towards balancing load and reducing trac. Relies on temporal/geographical popularity of documents. Ensuring consistency is cheap. Requires collaboration of \server proxies". 6

7 Client-initiated Caching Study Experiment Description We instrumented Mosaic to log all user accesses on our site [BCC:95]. We studied cache performance at various levels: { Session Caching: One cache per session { Host Caching: One cache per host { LAN Caching: One cache per LAN We used the logs obtained from Mosaic to perform trace simulations for various protocols [BCCCHM:95]. Sessions 4,700 Users 591 URLs Requested 575,775 Files Transferred 130,140 Unique Files Requested 46,830 Bytes Requested 2713 MB Bytes Transferred 1849 MB Unique Bytes Requested 1088 MB Summary Statistics for Trace Data Used in This Study 7

8 Client-initiated Caching Eectiveness Experiment Results Poor Byte Hit Rate < 40% with innite cache. Sharing amongst multiple clients is limited too! Byte Hit Rate Cache Expansion Index for Constant Byte Hit Rate Slope of No Geographic Locality of Reference 6% 10% 8% 12% Slope of Perfect Geographic Locality of Reference Multiprogramming Level (# of clients) Cache Expansion Index for Remote documents 8

9 The Server's Perspective Server Log Analysis We collected the logs of our departmental HTTP server and those of the Rolling Stones Multimedia server. We used the logs to analyze the popularity of various documents and to drive trace simulations of various server-initiated protocols. cs- Period 56 days 110 days URL requests 172,635 4,068,432 Bytes transferred 1,447 MB 112,015 MB Average daily transfer 26 MB 1,018 MB Files on system 2,018 N/A Files accessed (remotely) 974 (656) N/A (1,151) Size of (accessed) le system 50 MB (37 MB) N/A (402 MB) Unique clients (10+ requests) 8,123 60,461 Summary Statistics for Log Data Used in This Study 9

10 Popular documents are VERY popular! Zipf's law governs the probability of document access 5 2 1e+03 5 Access Frequency 2 1e e e+00 1e+00 1e+01 1e+02 1e+03 Document Rank The popularity of a le is inversely proportional to the rank of that le 10

11 The Server's Perspective Log Analysis of Making 25MB ( 3%) of data available to clients at a proxy one-hop closer to them would save more than 900MB/dayofnetwork bandwidth. Server Load Reduction % Frequency/ File ID MBytes Access frequency Expected bandwidth reduction 11

12 Information Dissemination Protocol Underlying Model A set of service proxies act as information \outlets" on the Internet. These service proxies oer space/bandwidth \for-rent" to other servers or proxies that constitute its Cluster. A server may belong to several clusters, thus allowing some of its les to be dessiminated to multiple service proxies. Service proxies are themselves servers who may be members of other clusters. Server Proxy of Server Proxy of Server Proxy of (Proxy of Server) Underlying Model for Information Dissemination 12

13 Information Dissemination Protocol Questions to be answered Given the access pattern at a server, which clusters should the server choose to join? Given the access pattern at a server, which les should the server disseminate? and where? Given the popularity prole of all servers in a cluster, how should the resources (space/bandwidth) at the service proxy be allocated? Assumptions The dissemination protocol should not require any \special" features/capabilities from other protocols. File popularity is a \universal" phenomenon (i.e. the probabilty of accessing a le is independent of who is accessing it). This is a conservative simplifying assumption. File popularity does not change drastically in a short period of time. This assumption has been veried. 13

14 Optimal Allocation of Storage at the Proxy Notation C = S 0 S 1 S 2 ::: S n is the set of servers in a cluster. S 0 is the service proxy of C. R i is the total number of bytes per unit time serviced by server S i to clients outside C. H i (b) is the probability that a request to S i will be to the most popular b bytes disseminated to S 0. B i is the number of bytes that S 0 duplicates from S i. B 0 = B 1 + B 2 + :::+ B n is the total storage space available at S 0. Goal Choose B i to maximize the percentage of trac serviced at S 0. C = P n i=1 R i H i (B i ) P n i=1 R i 14

15 Which Proxies Should be Contracted? Characterizing the Client Tree and Choosing Proxies Using the record route option of TCP/IP, it is possible to build a complete tree originating at the server with clients at the leaves. For this tree consisted of 18,000 nodes. The most popular les are disseminated down the tree and stored at proxies closer to the clients. The location of such proxies depends on the demand from the various parts of the tree. Analysis of logs for a consecutive 26-week period suggests that the shape of the tree (especially internal nodes) and the distribution of load is quite static over time. 15

16 How Does the Internet Look to a Server? Server Clients 16

17 How Much Bandwidth is Saved? How far could we \push" information towards clients? At least 8-9 hops! Replicating the most popular 25 MB from on few proxies yields a whoping saving of > 8 GBHops of network bandwidth per day Frequency Hops from Server to Clients How far away are clients? 17

18 How Much Bandwidth is Saved? Bytes x Hops Saved % Proxies Expected reduction in bandwidth as a result of dissemination 18

19 How Much History Should be Used to Disseminate? Short histories are better for shallow dissemination. Long histories are better for deep dissemination Weeks Reduction in Bandwidth (%) Weeks 2 Weeks 1 Week Replicas Disseminated weekly based on last N weeks usage 19

20 Dissemination Depth Maximum Depth Average Depth Minimum Depth Hops Replicas How far down the tree does dessimination go? 20

21 How Much Reduction in Server Load? Reduction in Server Load (%) Replicas Expected reduction in server load as a result of dissemination 21

22 How Much Reduction in Server Load? (closeup) Reduction is Server Load (%) Replicas Expected reduction in server load as a result of dissemination 22

23 Spatial Locality of reference Zipf's law governs the probability of sequences of document access! The popularity of a sequence of le requests is inversely proportional to the rank of that sequence. 1e+03 5 Access Frequency 2 1e e+01 5 K=1 K=2 K=3 2 1e+00 1e e e e+03 Sequence Rank 23

24 Spatial Locality of reference There are far less sequences in a server trace than randomly possible! Frequency in Trace Random Actual Sequence Length Could one use the knowledge of \popular" sequences for prefetching? 24

25 The Notion of Speculative Service Could client requests be predicted by the server? In many cases, the answer is yes. Servers could \speculatively" service documents before they are requested (a.k.a. server-initiated prefetching). Traversal D i D j D k Embedding D i1 D i2 D i3 D j1 D j2 D j3 D k1 D k2 D k3 Genesis of spatial locality of reference 25

26 Document Access Interdependency Matrix Notation Let p[i j] denote the conditional probability that document D j will be requested, within T w units of time after the request for D i. Let P denote the square matrix p[i j], for all possible documents 0 i j N. Let P denote the transitive closure of P. Thus, p [i j] is the probability that there will be a sequence of requests (inter-request time < T w ) starting with D i and ending with D j. Server log analysis Using server logs, the P and P matrices could be easily constructed Frequency Frequency P[i,j] P*[i,j] Histogram for ranges of p[i j] Histogram for ranges of p [i j] 26

27 Speculative Service Experiments Simulation Model Successive requests separated by lessthanstridetimeout units of time belong to the same \stride". Clients maintain a session cache. The session cache is purged if the time between successive requests exceeds SessionTimeout. Four performance metrics are used: Bandwidth ratio, Server Load ratio, Service Time ratio, and Miss rate ratio. Parameter Meaning Base Value CommCost Cost of communicating 1 Byte 1 unit ServCost Setup cost for a service request 10,000 unit StrideTimeout Value of time window T w 5.0 secs SessionTimeout Cache invalidation timeout 1 secs MaxSize Maximum size to prefetch 1 (no limit) Policy Speculative service algorithm p [i j] T p HistoryLength Length of the logs used for P 60 days UpdateCycle Frequency of recomputing P 1 day 27

28 Speculative Service Experiments Baseline Results Signicant improvement in performance (above what is achievable by client caching) could be achieved for a miniscule increase in trac. 5% extra bandwidth results in a whopping 30% reduction in server load, a 23% reduction in service time, and a 18% reduction in client miss-rate. Beyond some point, speculation does not seem to pay o. Ratio Ratio Server load Service time Miss rate Bandwidth Server load Timeliness Miss rate Tp more speculative less speculative Level of Speculation Traffic increase Metrics vs Speculation Level Metrics vs Bandwidth 28

29 Speculative Service Experiments Stability of the P and P Relations We varied the UpdateCycle from 1 to 7 days, while keeping the HistoryLength at 60 days. This change resulted in a 3% degradation in all measured metrics, suggesting that P and P do change (albeit very slowly) with time. Also, we varied the HistoryLength from 60 to 30 days, while keeping the UpdateCycle at 1 day. This change resulted in a 5% improvement in all measured metrics, suggesting that an aging mechanism must be used to phase-out dependencies exhibited in on older server traces, in favor of dependencies exhibited in more recent ones. Ratio Ratio day cycle 7-day cycle Server load Timeliness Miss rate Server load Timeliness Miss rate Server load Timeliness Miss rate Traffic increase 0.45 Traffic increase Eect of slower UpdateCycle Eect of shorter HistoryLength 29

30 Speculative Service Experiments Eect of Client Caching We compared simulations with SessionTimeout equal to 3,600 seconds (large cache) and to 120 seconds (small cache). The presence of client caching (even if modest) is likely to further improve the performance of speculative service. In order to reap all the benet from speculative service, client must cache \prefetched" documents long enough Ratio Smaller Cache Server load Timeliness Miss rate Larger Cache Server load Timeliness Miss rate Ratio Extra traffic = 10% Server load Timeliness Miss rate Extra traffic = 50% Server load Timeliness Miss rate Traffic increase Session Timeout Seconds Eect of client caching Eeciency of client caching 30

31 Speculative Service Experiments Cooperative Clients Performance could be further improved if documents \already cached at the client" are not speculatively serviced! Our simulations showed that speculative service with cooperative clients results in better bandwidth utilization, especially when the client performs \some" caching. Ratio Ratio Cooperative Clients Server load Timeliness Miss rate Non-cooperative Clients Server load Timeliness Miss rate Cooperative Clients Server load Timeliness Miss rate Non-cooperative Clients Server load Timeliness Miss rate Traffic increase Traffic increase Cooperative client (small cache) Cooperative client (innite cache) 31

32 Speculative Service Experiments Eect of Document Size The benets of speculation are most pronounced when documents serviced speculatively are small. We studied this by varying the MaxSize parameter. Ratio Ratio Server load Timeliness Miss rate Server load Timeliness Miss rate Low level of speculation MaxSize in KBytes High level of speculation MaxSize in KBytes 32

33 Client Prefetching Could the next request be predicted by the client? Using client traces, it is possible for the client software to perform \prefetching" [Bestavros and Cunha: 1995]. Performance of client-initiated prefetching is highly dependent on user access patterns. URL Id URL Id Reference Reference A \Mostly Surng" User A \Mostly Working" User 33

34 Performance of Client Prefetching Trace simulations indicate that client-initiated prefetching is very eective for \frequently traversed documents" but ineective for \newly/rarely traversed documents" Miss Rate Ratio Extra Bandwidth (%) Performance improvement for a \creature of habit" ::^ 34

35 Flip-Flop Between Server and Browser Hints By monitoring the \surng index", we favor either the server hints or the browser hints for prefetching purposes. Reference ID G W Time Use Server Hints to Prefetch User is Surfing G W > h Use Browser Hints to Prefetch User is Working G W < h 35

36 Implementation of Prefetching Agents Server-assisted Prefetching We have modied the NCSA HTTP server to allow it to build rst and second Markov models for all documents on a site. Everytime a client requests a page, the server automatically appends PREFETCH hints to the end of the served document: <PREFETCH AGENT="agent" HREF="url" RANK="rank%" PROB="prob"> Also, we have modied the NCSA Mosaic browser to allow it to interpret the server hints and perform prefetching accordingly. Client-initiated Prefetching We have modied the NCSA Mosaic browser to allow it to build rst and second Markov models for all documents accessed by a user. Everytime a client requests a document, the browser (may) automatically prefetch a number of other documents and keep them in the disk cache. Integrating Server and Client agents The above two implementations need to be integrated. We are working on that! 36

37 Web Trac Characteristics Motive Trac attributed to the use of the Web makes up an increasing percentage of internet trac. Understanding the nature of web trac is important for scaling and capacity planning purposes. Conclusions Trac attributed to the use of the Web exhibits self-similar properties. Web trac self-similarity is due to the heavy-tailed nature of transmission time distribution, which is related to Web le size distributions. Related Work Leland, Taqqu, Willinger, and Wilson's paper in SIGCOMM'93 established the self-similarity of LAN trac and Willinger, Taqqu, Sherman, and Wilson's paper in SIGCOMM'95 expalined self-similarity for LAN using ON/OFF model. 37

38 Web Trac Characteristics: Data Set used in this study Sessions 4,700 Users 591 URLs Requested 575,775 Files Transferred 130,140 Unique Files Requested 46,830 Bytes Requested 2713 MB Bytes Transferred 1849 MB Unique Bytes Requested 1088 MB Summary Statistics for Trace Data Used in This Study 38

39 Web Trac Characteristics: Burstiness at varying scales Bytes 0 5*10^6 10^7 1.5*10^7 2*10^7 2.5*10^7 3*10^7 Bytes 0 2*10^6 4*10^6 6*10^6 8*10^ Chronological time (slots of 1000 sec) Chronological time (slots of 100 sec) Bytes Bytes Chronological time (slots of 10 sec) Chronological time (slots of 1 sec) Trac Bursts over Four Orders of Magnitude Upper Left: 1000, Upper Right: 100, Lower Left: 10, and Lower Right: 1 Second Aggegrations. 39

40 Web Trac Characteristics: Transmission Time Distribution Web trac self similarity may be the result of superimposing many ON/OFF processes, where ON and/or OFF times are heavy-tailed (Pareto). p(x) = k x ;;1 Log10(P[X>x]) Log10(Transmission Time in Seconds) The t to a straight line of the Log-Log Complementary Distribution (LLCD) suggests that transmission times are heavy-tailed (i.e. Pareto with parameter ). 40

41 Heavy-tailed Transmission Times: The CLT Test 0 Aggregated Samples from Pareto Distribution 0 Aggregated Samples from Lognormal Distribution Log10(P[X>x]) Aggregated 100-Aggregated 10-Aggregated All Points Log10(P[X>x]) Aggregated 100-Aggregated 10-Aggregated All Points Log10(x) Log10(x) 0 Aggregated Transmission Times of WWW Files -1 Log10(P[X>x]) Aggregated 100-Aggregated 10-Aggregated All Points Log10(x) Comparison of CLT Test for Pareto (upper left) and Lognormal (upper right) and actual trac distribution (bottom). 41

42 Heavy-tailed Transmission Times: Genesis Transmission times are heavy-tailed because the size distribution of transmitted les is heavy-tailed. The size distribution of transmitted les is heavy-tailed because the size distribution of unique requests is heavy-tailed. File Requests File Transfers Unique Files (46,830) (130,140) (575,775) 42

43 Distributions of Requested, Unique, and Transferred Files Distribution Requested Files 1.16 Transferred Files 1.06 Unique Files Log10(P[X>x]) Unique Files File Transfers File Requests Log10(File Size in Bytes) 43

44 Why is the Size Distribution of Unique Files Heavy-Tailed? The size distribution of unique requests is heavy-tailed because it reects the size distribution of available les on the Web, which we have shown to be heavy-tailed by surveying Web le systems at 32 dierent sites. Distribution Unique Files 1.05 Available Files Log10(P[X>x]) Unique Files Available Files Log10(File Size in Bytes) 44

45 Relationship Between Various File Sets Transmission Times File Transmission File Transfers Unique Files Available Files Caching File Requests One-to-One Relation Subset Relation 45

46 How Typical Are Web Files? 100 WWW Document Sizes Unix File Sizes Percent of All Files e+06 Size in Bytes 46

47 Relationship Between Heavy Tail and Media 0 Size Distribution by File Type -1 Log10(P[X>x]) All Files a=1.06 Image Files Audio Files Video Files Text Files a= Log10(File Size in Bytes) 47

48 Conclusion: Overview of Oceans Projects Client-based Studies Client Caching Protocols Prefetching Protocols Dynamic Server Selection Protocols Server-based Studies Server Caching Protocols Dissemination Protocols Speculative Service Protocols Performance Modeling Studies Web Trac Characteristics Web Access Patterns Characteristics 48

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