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1 (Prefetching Parameters)13 34 (Web Traffic Trace)14 35 (Prefetching Time)18 36 (Performance Metrics) Prefetchable Object List Generator 22 i
2 411 HTTP/10 Response Header HTTP/11 Request Header Squid Freshness Request Generator Accuracy41 62 Wasted Bandwidth46 63 Request Saving Bandwidth Saving Summary [ ] 57 ii
3 Prefetchable Object List HTTP/10 Response Header Cache-Control General Header Field STALE Access Log Format Store Log Format iii
4 Off-peak Periods Peak Periods 51 iv
5 1 Cache Summary Statistics Off-peak Periods Cache Byte Hit Ratio with and without Prefetching Peak Periods Performance Mateics Peak Periods Off-peak Periods Peak Periods 53 v
6 vi
7 MCC 9851M08, Sook-Hyang Kim, A Statistical, Batch and Proxy-Initiated Web Prefetching Scheme for Efficient Internet Bandwidth Usage,,, 2000, 60P, Advisor: J Won-Ki Hong, Text in Korean ABSTACT As the number of World-Wide Web (Web) users grows, Web traffic continues to increase at an exponential rate Currently, Web traffic is one of the major components of Internet traffic Also, high bandwidth usage due to Web traffic is observed during peak periods while leaving bandwidth usage idle during off-peak periods One of the solutions to reduce Web traffic and speed up Web access is through the use of Web caching Unfortunately, Web caching has limitations for reducing network bandwidth usage during peak periods In this thesis, we focus our attention on the use of a prefetching algorithm for reducing bandwidth during peak periods by using off-peak period bandwidth We propose a statistical, batch, proxy-side prefetching algorithm that improves cache hit rate while only requiring a small amount of storage We present simulation results based on Web proxy trace and show that this prefetching algorithm can reduce peak time bandwidth using off-peak bandwidth
8 1 World Wide Web( ),,, [20, 21], (Internet traffic) (Web traffic) (bottleneck) [25, 26] (bandwidth) peak periods, off-peak periods 1 16 subnet 2 ( ), 2 1 1
9 2 ( byte) peak periods off-peak periods peak periods 14:00 16:00 18:00 04:00, 12 off-peak periods 04:00 14:00 16:00 18: peak periods off-peak periods peak periods off-peak periods, (Web caching) [10], [11, 12], 2
10 [7, 27, 28] peak peiods off-peak periods, off-peak periods peak periods [4, 5, 11] (caching server) (Web prefethcing) peak periods off-peak periods,, off-peak periods peak periods , 6 7 3
11 4 2 21,, Padmanabhan Mogul [2] (prediction) (prediction algorithm) Griffioen Appletion[13],
12 hyperlink dependency graph graph A B A B arc (edge) arc A B dependecy graph (Web server traces) trace-driven 45% 2 Wang Crowcroft HotList Manager delay [1] deterministic client-initiated prefetching, Deterministic prefetching Bestavros Server-initiated prefetching [14] D i, D j Bestavros D i time interval T w D j p[i,j] advice Bestavros trace-driven 10% 23% cache miss rate 5
13 Kroeger, 60% [15] Chinen Yamaguchi Wcol [3, 17] HTML (parsing) - (pre-pushing) Wcol Jacobson Cao - [18] - (push) (Web access traces) (overhead) (accuracy) - (pre-push scheme) - 10% 18%, (request) 12% Makatos 6
14 Chronak Top-10 Approach [16, 24] client-proxy-server framework Top-10 (Web server trace), 10% 40%,,, 22,, 221,, 7
15 Server-initiated prefetching :, hyperlink (pushing) [2, 14, 15], Client-initiated prefetching : (agent) [1, 2, 13] Client-initiated prefetching (Web access pattern) Proxy-initiated prefetching :, [3, 17, 18] 8
16 222 (statistical prefetching)[5, 8, 9] (deterministic prefetching) [1] Statistical prefetching : (access log) Deterministic prefetching :, 223 (response time) 9
17 Prefetching for Response Time Reduction :, [2, 5, 13, 16, 18] peak periods Prefetching for Balanced Bandwidth Usage : peak periods off-peak periods Batch prefetching balanced bandwidth usage 10
18 3 Server-initiated Prefetching Client-initiated Prefetching peak bandwidth usage, (parameter) real-world traffic pattern 16 (subnet) (performance metrics) 11
19 31??,???? 32?? ( :, 1 )??, (accuracy) prefetchable objects 12
20 (expiration time) (cache miss)?? 33 (Prefetching Parameters),,,,??,?? Traditional prefetching scheme n 13
21 ?? 100M?? off-peak periods peak periods off-peak periods off-peak periods off-peak periods (Web Traffic Trace) real-world traffic pattern 16 (subnet) ( ) ( ) (cache object hit rate) 5496% (cache byte hit rate) 3142% (request) 4,077,308 total bytes 502G bytes 36G 1 14
22 1 Cache Summary Statistics Number of Client Making Requests 447 Cache Object Hit Rate 5496 % Cache Byte Hit Rate 3142 % Total Number of Objects Requested 4,077,308 Total Bytes to Clients 502 Gbyte 2 (reference count) 7075% % 121, (%) 1 86, , , , , Total 121,
23 3 3 3 total size %, % total bytes Mbyte 4 3 (Mbyte) (%) Total
24 4 5 6 total bytes
25 35 (Prefetching Time) off-peak periods peak periods off-peak periods off-peak periods off-peak periods off-peak periods off-peak periods off-peak periods 80% ( byte) off-peak peirods off-peak periods 04:00 13:00, peakperiods 13:00 04:00 off-peak periods off-peak periods (04:00 13:00) prefetchable object 7 80% 7 18
26 36 (Performance Metrics),?? Request Saving : hit Request saving?? Bandwidth Saving : Bandwidth saving peak periods bandwidth usage peak periods peak periods?? Accuracy : accuracy (prefetched object hit rate) (prefetched byte hit rate) total bytes total bytes Accuracy?? Wasted Bandwidth : 19
27 offpeak periods off-peak periods 20
28 4 8 Prefetching system Caching Server prefetchable object list Prefetchable Object List Generator accesslog storelog cache Request Generator Caching Server clients request response request response request response Internet 8, Squid[7] Squid freeware, Cobalt Network CacheRaQ[29] Packetstorm Technologies Webspeed[30] Squid Squid 21
29 Squid transparent Transparent (browser) (data path) [31] configuration default gateway Prefetchable Object List Generator Request Generator (Squid) Squid, Squid request access log, store log squid (accesslog, storelog) Prefetchable Object List Generator prefetchable object list Request Generator off-peak periods prefetchable object list (request) Squid Squid add-on 41 Prefetchable Object List Generator Prefetchable Object List Generator 22
30 Squid access log store log access log Squid requests store log Prefetch Object List Generator Squid (refresh algorithm) prefetchable object HTTP response message Squid (3 days) [7] 9 prefetchable object list 9 Prefetchable Object List Prefetchable Object List Generator Squid, store log store log HTTP response header HTTP response header HTTP request header Squid 23
31 411 HTTP/10 Response Header HTTP [22, 23, 33] / (Request/Response), ( : GET, HEAD, POST) TCP,, TCP HTTP method, URI, protocol version,,,,,, HTTP response header HTTP/10 response header 10 Full-Response = Status-Line *(General-Header Response-Header Entity-Header) CRLF [Entity-Body] Status-Line = HTTP-Version Status-Code Reason-Phrase CRLF General-Header = Date Progma Response-Header = Location Server WWW-Authenticate Entity-Header = Allow Content-Encoding Content-Length Content-type Expires Last-Modified extension-header extension-header = HTTP-header 10 HTTP/10 Response Header 24
32 Full-Response Status-Line HTTP (Status-Code) (Reason-Phrase) CRLF Entity CRLF Status-Code?? 1xx : Informational -?? 2xx : Success -?? 3xx : Redirection -?? 4xx : Client Error -?? 5xx : Server Error - (response message), General-Header, Request-Header, Response-Header, Entity-Header RFC [32] 25
33 General Header Full-Request Full-Response, General Header Date Progma Date Progma / Response Header Status-Line Request-URI Entity-Header Entity-Body,,, (metainformation), entity body Squid HTTP response header?? Date : General Header Date, RFC 822[32] orig-date Date : Wed, 15 Oct :12:31 GMT?? Expires : Entity Header Expires, 26
34 Expires Expires : Mon, 01 Nov :00:00 GMT?? Last-Modified : Entity Header Last-Modified Last-Modified Last-Modified : Tue, 14 Oct :45:26 GMT 412 HTTP/11 Request Header HTTP/10, URL TCP / / congestion congestion 27
35 , HTTP/11 [23] HTTP/11, Squid Cache-Control Request Header max-age HTTP/10 HTTP HTTP/11, HTTP/11 General Header Cache-Control Cache-Control (directive) (cache directive) Cache-Control cache-response-diective Cache-Control Cache-Control = "Cache-Control" ":" 1#cache-directive cache-directive = cache-request-directive cache-response-directive cache-request-directive = "no-cache" "no-store "max-age" "=" delta-seconds "max-stale" [ "=" delta-seconds ] "min-fresh" "=" delta-seconds "no-transform" "only-if-cached" cache-extension cache-extension = token [ "=" ( token quoted-string ) ] 11 Cache -Control General Header Field 28
36 cache-request-directive max-age Squid (maximum object age) max-age, max-age max-age Squid CLIENT_MAX_AGE 413 Squid, (expiration time) (cache miss) Squid 13 Squid default value OBJ_DATE OBJ_LASTMOD Expires OBJ_DATE, OBJ_LASTMOD, Expires HTTP Respons Header [22] CLIENT_MAX_AGE HTTP/11 Cache-Control Request Header 29
37 [23] Squid refresh_pattern CONF_MIN, CONF_PERCENT, CONF_MAX squidconf HTTP Response Header Expires default Expires [7] NOW, OBJ_AGE LM_AGE LM_FACTOR OBJ_AGE LM_AGE 12 OBJ_DATE, Expires, OBJ_LASTMOD millisecond UNIX time ( : Fri Oct 15 04:00: GMT = ) CLIENT_MAX_AGE, OBJ_AGE, LM_AGE, LM_FACTOR second CONF_MAX CONF_MIN minute CONF_PERCENT % OBJ_DATE Expires OBJ_LASTMOD : : : CLIENT_MAX_AGE : OBJ_AGE = NOW - OBJ_DATE (sec) LM_AGE = OBJ_DATE - OBJ_LASTMOD (sec) LM_FACTOR = OBJ_AGE / LM_AGE (sec) CONF_MAX CONF_MIN : 4320 (min, 3 days) : 0 (min) CONF_PERCENT : 02 (20%) 12 30
38 Is CLIENT_MAX_AGE present in the request? yes Is OBJ_AGE more than CLIENT_MAX_AGE? yes STALE(1) no no Is Expires present in the response? yes Has the object already expired? yes no STALE(2) FRESH no Is OBJ_AGE more than CONF_MAX? yes STALE(3) no Is OBJ_DATE more than OBJ_LASTMOD? yes LM_FACTOR less than the CONF_PERCENT? yes no FRESH STALE(4) no Is OBJ_AGE less than CONF_MIN? yes no FRESH STALE(5) STALE 5 Expires, OBJ_DATE, OBJ_LASTMOD, NOW HTTP CLIENT_MAX_AGE 100 sec OBJ_AGE 120 sec OBJ_AGE CLIENT_MAX_AGE STALE (STALE(1)) Expires NOW, (STALE(2)) OBJ_AGE CONF_MAX (STALE(3)) 3 OBJ_DATE OBJ_LASTMOD LM_FACTOR (30%) CONF_PERCENT (20%) (STALE(4)) 31
39 OBJ_AGE (5760 min) CONF_MIN (0 min) STALE (STALE(5)) STALE(1) CLIENT_MAX_AGE : 100 (sec) OBJ_AGE : 120 (sec) STALE(2) EXPIRES : Fri, 01 Oct :00:00 GMT NOW : Sun, 03 Oct :01:20 GMT STALE(3) OBJ_AGE : 5760 min (4 days) CONF_MAX :4320 min (3 days) STALE(4) OBJ_LASTMOD : Wed, 29 Sep :00:00 GMT OBJ_DATE : Fri, 01 Oct :00:00 GMT LM_FACTOR : 30 % CONF_PERCENT : 20 % STALE(5) OBJ_AGE : 5760 min (4 days) CONF_MIN : 0 min 14 STALE 414 Freshness FRESH, STALE STALE squid OBJ_DATE, 32
40 OBJ_LASTMOD, Expires squid storelog, CONF_MAX, CONF_MIN, CONF_PERCENT squid squidconf CLIENT_MAX_AGE 15 Squid access log, 16 Squid store log 15 Access Log Format?? Time UNIX time milliseconds?? Elapsed (connection)?? Remotehost IP?? Code/Status Code ( : TCP_HIT, TCP_MISS, etc) Status HTTP status code?? Byte?? Method HTTP request Method ( : GET, HEAD, POST)?? URL URL 33
41 Time Action Status OBJ_DATE OBJ_LASTMOD Expires Type Len Method URL 16 Store Log Format store log Time, Status, Method, URL access log OBJ_DATE, OBJ_LASTMOD, Expires 413?? Action RELASE, SWPIN, SWPOUT RELASE SWAPOUT SWAPIN?? Type, text/html, image/gif?? Len expect-len real-len, expect-len HTTP Content-Length Response Header, real-len NOW, fresh stale access log stale stale Squid NOW 34
42 NOW? Current Pr efetchngtime? PrefetchingFrequency Current PrefetchingTime : Pr efetchingfrequency : off-peak periods (prefetching frequency) , (16 04:00) : :00 access log store log Last prefetching time Currnt Prefetching time Next prefetching time Input Data (logs) Prefetching Frequency 10/15 4:00 10/16 4:00 10/17 4:
43 42 Request Generator Request generator Prefetchable Obejct List off-peak periods Prefetchable Object List Generator Prefetchable Object List request off-peak periods request HTTP command-line Web client wget [19] crontab off-peak periods Request Generator Off-peak periods off-peak WAN SNMP agent SNMP agent (polling), 36
44 5 (input parameter) 51 trace-driven CacheRaQ[29] 16 (subnet) Squid (input parameter)?? Logs : access log store log?? Off-peak periods : 35 off-peak periods 04:00 13:00 04:00 37
45 ?? : 33 52?? : Squid, 413 NOW (Web traffic trace),, (Prefetchable Web Objects) (expiration time) 1, 2, 3, 4, 5 38
46 off-peak periods Prefetchable Object List Generator off-peak periods prefetchable object list performance analyzer prefetchable object list request saving bandwidth saving accracy wasted bandwidth Prefetchable Object List Generator access log store log prefetchable object list 39
47 Simulation Model accesslog storelog Prefetchable Object List Generator Prefetchable Object List Performance Analyzer Performance Metrics With Prefetching Performance Metrics Without Prefetching 18 Performance Analyzer prefetchable object list access log prefetchable object list access log request saving ( ), bandwidth saving ( ) accuracy (, ) access log wasted bandwidth request saving bandwidth saving accuracy wasted bandwidth 40
48 6 3 accuracy wasted bandwidth, performance parameter request saving bandwidth saving 61 Accuracy Accuracy daily prefetchable object list % % 1 prefetchable object 5064% 1955% linear 41
49 4 (%) (%) 1 16,274 (5064 %) 2,476 (1955 %) 1522 % 2 5,525 ( 172 %) 2,286 (1805 %) 4138 % 3 3,008 ( 936 %) 1,804 (1424 %) 5996 % 4 1,966 ( 612 %) 1,430 (1129 %) 7276 % 5 5,358 (1668 %) 4,469 (3687 %) 8714 % Total 32,131 (10000 %) 12,665 (10000 %) 3942 % 19 prefetched object hit
50 % % accuracy 5 (%) (%) 1 545,399,428 (6343%) 47,336,276 (2502%) 868 % 2 154,356,860 (1795%) 56,170,230 ( 297%) 3639 % 3 64,124,072 ( 746%) 25,570,556 (1352%) 3989 % 4 33,972,272 ( 395%) 15,026,692 ( 794%) 4423 % 5 62,024,975 ( 721%) 45,057,416 (2382%) 7264 % Total 859,877,607 (100%) 189,161,170 (100%) 22 % 21 43
51 , 2, 3,
52 2 6426% %25% 2 461% 1 22% % 22 % % 451% % 535 % % 626 % % 726 % 23 45
53 62 Wasted Bandwidth Wasted bandwidth Mbyte ( Mbyte) 178% Mbyte 46% M 178 % M 46 % M 20 % M 10 % M 05 % off-peak periods off-peak periods off-peak Mbyte 1 off-peak 519%, off-peak periods 2 283%, 3 167%, 4 108%, 5 72% 8 off-peak periods 24 46
54 8 Off-peak Periods Off-peak periods % % % % 5 72 % 24 Off-peak Periods 63 Request Saving 9 55%, 1 47
55 43% 593% %, 3 272%, 4 21%, 5 161% % 9 ( ) 55 % ( + ) ( 1 ) 593 % ( + ) ( 2 ) 585 % ( + ) ( 3 ) 577 % ( + ) ( 4 ) 571 % ( + ) ( 5 ) 567 %
56 64 Bandwidth Saving %, 1 501%, %, 3 227%, 4 159%, 5 119% % 10 ( ) 314 % ( + ) ( 1 ) 364 % ( + ) ( 2 ) 352 % ( + ) ( 3 ) 337 % ( + ) ( 4 ) 330 % ( + ) ( 5 ) 326 % 26 Request saving 49
57 26 peak periods 11 peak periods Peak Periods Peak periods 1 64 % 2 48 % 3 29 % 4 20 % 5 15 % 50
58 27 Peak Periods 65 Summary 12 Request saving 12 Performance Mateics Bandwidth saving Wasted bandwidth Prefetched object hit rate Accuracy Prefetched byte hit rate % 501 % 178 % 394 % 22 % % 378 % 46 % 643 % 451% % 227 % 20 % 765 % 535 % % 159 % 10 % 833 % 626 % % 119 % 05 % 871 % 726 % off-peak periods peak periods 51
59 off-peak periods peak periods Peak Periods Off-peak Periods Peak periods off-peak periods 1 64 % 519 % 2 48 % 283 % 3 29 % 167 % 4 20 % 108 % 5 15 % 72 % E p? B s / B w E B B s p w : : ( Mbyte) : ( Mbyte) 1, 2, 3, 4, 5, 52
60 peak periods peak periods peak periods peak periods 4% peak periods Peak Periods B s (Mbyte) B w (Mbyte) E p (Mbyte) Peak periods % % % % % 14 peak periods 4% 1, peak periods 4% 2 53
61 2 141,824,895 bytes peak periods 54,000 seconds (19 hours) peak periods peak periods = ( *8)/54000 = 2052 Kbps 2 peak periods 2052 Kbps 54
62 7 peak periods, off-peak periods peak periods request saving 353% bandwidth saving 378% %, 451% peak bandwidth usage 2052 Kbps peak periods prefetching scheme squid add-on feature 55
63 Web traffic trace off-peak periods (eg, 20:00~08:00),, real-time prefetchable object list 56
64 [1] Z Wang and J Crowcroft, Prefetching in World Wide Web, IEEE Globecom 96, [2] V Padmanabhan and J Mogul, Using Predictive Prefetching to Improve World Wide Web Latency, Computer Communication Review, 26(3):22-36, July 1996 [3] Ken-ichi Chinen and Suguru Yanaguchi, An Interactive Prefetching Proxy Server for Improvement of WWW Latency, INET 97, 1997, [4] Arthur Goldberg, Ilya Pevzner and Robert Buff, Caching Characteristic of Internet and Intranet Web proxy Traces, In Computer Measurement Group Conference (CMG 98), Anaheim, CA, December 1998, [5] David Barnes and Neil G Smith, An Analysis of World-Wide Web Proxy Cache Performance and its Application to the Modelling and Simulation of Network Traffic, In Proceedings of the Fourth International Conference on Telecommunication Systems Modeling and Analysis, March 1996, [6] Themistoklis Palpanas and Alberto Mendelzon, Web Prefetching Using Partial Match Prediction, In Web Caching Workshop WCW 99, 1999, [7] Squid Internet Object Cache, available from [8] Gihan VDias, Graham Cope and Ravi Wijayaratne, A Smart Internet Caching System, INET 96 Conference, 1996, whatis/conferences/inet/96/proceedings/a4/a4_3htm [9] Katsuo Doi, WWW Access by Proactively Controlled Caching Proxy, Sharp Technical Journal, No 66, December 1996 [10] Brad Duska, David Marwood, and Michael JFeeley, The Measured Access Characteristics of World-Wide Web Client Proxy Caches, In Usenix Symposium on Internet Technologies and Systems (USITS), Monterey, CA, USA, December , Usenix, Projects/SPA/Report/Reporthtml 57
65 [11] Marc Abrams, CRStandridge, GAbdulla, SWilliams, and EAFox, Caching Proxies: Limitations and Potentials, In Proceedings of the Fourth International WWW Conference, 1995, WWW4html [12] Anawat Chankhunthod et al, A Hierarchical Internet Object Cache, Technical Conference, Usenix 1996, [13] James Griffioen and Randy Appleton Reducing File System Latency using a Predictive Approach, Proceddings of the 1994 Summer USENIX Technical Conference, Boston, Massachusetts, USA, 1994, [14] Azer Bestavros, Speculative Data Dissemination and Service to Reduce Server Load, Network Traffic and Service Tome in Distributed Information System, In International Conference on Data Engineering, pages , New Orleans, LO, February 1996 [15] Tomas M Kroeger, Darrell D E Long, and Jeffrey C Mogul Exploring the bounds of web latency reduction from caching and prefetching, In Proceedings of USENIX Symposium on Internet Technology and Systems, December 1997, usits97/kroegerhtml [16] Evangelos P Margatos and Catherine E Chronaki, A top-10 Approach to Prefetching on the Web, Technical report, In Proceedings of INET' 98 (The Internet Summit), Geneva, Switzerland, July 1998, proj/arch-vlsi/os/wwwhtml [17] Wcol Group, WWW Collector the prefetching proxy server for WWW, 1997, [18] Li Fan, Quinn Jacobson, Pei Cao, and Wei Lin, Web Prefetching Between Low-Bandwidth Clients and Proxies: Potential and Performance, In Proceedings of the Joint International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS '99), Atlanta, GA, May 1999, [19] Wget, available from 142/wget_tochtml [20] Tim Bray, Measuring the Web, In Proceedings of the Fifth International 58
66 World Wide Web Conference, pages , Paris, France, May 1996 [21] Allison Woodruff, Paul M Aoki, Eric Brewer, Paul Gauthier, and Lawrence A Rowe, An Investigation of Documents from the WWW, In Proceedings of the Fifth International WWW Conference, pages , Paris, France, May 1996 [22] T Berners-Lee, R Fielding, and H Frystyk, Hypertext Transfer Protocol HTTP/10, RFC 1945, May, 1996 [23] R Fielding, J Gettys, J Mogul, H Frystyk, L Masinter, P Leach and T Berners-Lee, Hypertext Transfer Protocol - HTTP/11, RFC 2616, June 1999 [24] Evangelos Markatos, Catherine E Chronaki, A Top-10 Approach to Prefetching on the Web, Technical Report No 173, ICS-FORTH, Heraklion, Crete, Greece,August 1996 [25] Ghaleb Abdulla, Edward A Fox, Marc Abrams, and Stephen Williams, WWW Proxy Traffic Characterization with Application to Caching, Technical Report TR-97-03, Computer Science Department, Virginia Tech, March 1997, [26] James E Pitkow, Summary of WWW characterizations, In Proceedings of the Seventh International World Wide Web Conference, Brisbane, Australia, April 1998, com1877htm [27] Pei Cao, Edward W Felten, Anna R Karlin, and Kai Li, A Study of Integrated Prefetching and Caching Strategies, In Proceedings of the ACM SIGMETRICS Conference on Measurement and Modeling of Computer Systems, May 1995, [28] Eric A Brewer, Paul Gauthier, and Dennis McEvoy, The long-term viability of large-scale caching, In Proceedings of the Third International WWW Caching Workshop, Manchester, England, June 1998, [29] CacheRaQ of Cobalt Network, available from [30] Webspeed of Packetstorm Technologies, available from [31] Bert Williams, Transparent web caching solutions, In Proceedings of the Third International WWW Caching Workshop, Manchester, England, June 59
67 [32] David H Crocker, Standard For The Format Of Arpa Internet Text Message, RFC 822, August 13, 1982 [33], Protocals for the World-Wide Web, available form 60
68 61
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