Web Prefetching Lili Qiu Microsoft Research March 27, 2003

Overview of Prefetching Over 40% cache misses are compulsory misses [PQ00] Client response time –DNS query + connection establishment + server process time + data transfer + client rendering time Prefetching DNS name resolution –Idea: send a DNS query before a requests –Cost: higher load on the network and DNS servers Prefetching TCP connections –Idea: establish a TCP connection before a request –Cost: higher load on the network, client, server Prefetching HTTP responses –Idea: transfer web content before a request –Cost: higher load on the network, client, server

Questions in Prefetching What are performance metrics? –Response time, network bandwidth, server load, client load Who does prefetch? –Client-based: based on requests from a single client –Server-based: based on requests from all clients to a single server and web content (e.g., hyperlink structure) –Proxy-based: based on aggregated clients’ requests to aggregated servers What to prefetch? –DNS query –TCP connection –HTTP responses Entire content A part of content (e.g., prefix) Meta data (e.g., HEAD request)

Questions in Prefetching (Cont.) How to predict? –Content-based: hyperlink structure, topics, … –Access-based: popularity, request sequence, … How to prefetch? –How to coordinate transfers of prefetched content vs. requested content? –How to allocate cache space to prefetched content vs. requested content? How to get & use feedback –Notify proxy/server when there’s a cache hit

Prediction Algorithms Letizia [Lie95] prefetch the documents pointed by the requested document –The depth of hyperlink to follow is controllable –Prefetch all or part of hyperlinks (e.g., appear earlier in the page or more popular)

Prediction Algorithms (Cont.) Predictive prefetching [PM96] –Dependency graph with weight being the ratio of # accesses to B within a window after A to # accesses to A –When A is accessed, prefetch B if the associated edge has a weight larger than a threshold –Other optimizations adjust weights using aging, and consider time window in which data is useful C A DB HI DD C

Prediction Algorithms (Cont.) Prefetching between low-bandwidth clients and proxies [FJCL99] –Prediction by Partial Matching (PPM) m: # accesses used to predict l: # steps the algorithms tries to predict t: threshold used to weed out candidates C A BG HI DD D B CE F FE D E C DJ DF E LC D K

Prediction Algorithms: PPM (Cont.) Implementation variations –No proxy notification upon browser cache hits reduces prediction accuracy by 5% –Prefetching without knowledge of the content of browser caches increases wasted transfer –Limiting the size of history structure reduces prediction accuracy slightly

Transfer Prefetched Content Prefetching has serious performance impacts even under perfect prediction [CB98] –Assumption: prefetching imposes significant network load –Straightforward prefetching may increase traffic burstiness  increase network queue and thus network delay –Prefetching under rate control helps to smooth traffic (compared to w/o prefetching), and reduce response time –Important to schedule prefetched content vs. requested content appropriately

Content Distribution Prefetching can be applied in combination with caching and content distribution CDN has similar flavor as caching, with the following differences –CDN: server oriented Improve performance of requests to a server Ease cache updates –Caching: client oriented CDN research –Server selection: locality + server load + content availability –Redirection techniques: DNS-based redirection, HTTP redirection, URL rewriting, … –Server/content placement [QPV01]

References [CB98] M. Crovella and P. Barford. The Network Effects of Prefetching, February 7, In Proc. of IEEE INFOCOM '98. Available at archive/infocom98.ps [FJCL99] Li Fan, Quinn Jacobson, Pei Cao and Wei Lin. Web Prefetching Between Low-Bandwidth Clients and Proxies: Potential and Performance. In Proc. Of SIGMETRICS'99. Available at [KR01] Bala Krishnamurthy and Jennifer Rexford. Web Protocols and Practice, HTTP/1.1, Networking Protocols, Caching, and Traffic Measurement. Addison Wesley, [Lie95] H. Lieberman. Letizia: An Agent that assists Web Browsing. In Proc. International Joint Conference on Artificial Intelligence, Aug Available at a/Letizia.html

References (Cont.) [PM96] Venkata N. Padmanabhan and Jeffrey C. Mogul. Using Predictive Prefetching to Improve World Wide Web Latency. ACM Computer Communication Review, 26(3):22-36, Jul Available at july96.ps [PQ00] Venkata N. Padmanabhan and Lili Qiu. The Content and Access Dynamics of a Busy Web Site: Findings and Implications. In Proceedings of SIGCOMM 2000, Stockholm, Sweden, August Available at [QPV01] Lili Qiu, Venkata N. Padmanabhan, and Geoffrey M. Voelker. On the Placement of Web Server Replicas. In Proceedings of INFOCOM 2001, Anchorage, AK, USA, April Available at