An Adaptive Prefetching Method for Web Caches

Transcription

1 An Adaptive Prefetching Method for Web Caches Jaeeun Jeon, Gunhoon Lee, Ki ong Lee, and Byoungchul Ahn Yeungnam nievrsity, School of Electrical Engineering and Computer Science aedong, Gyungsan, Korea Abstract. This paper presents a new approach to predict web documents by tracking search patterns of users and by managing documents depending upon the number of hits. Several hot spot documents and their linked documents are stored in cache servers and transmitted them to clients for fast response. The adaptive prefetching method, using search patterns of users, analyzes documents along with the navigation patterns and marks several popular documents. If one of these marked documents is hit, all marked documents are loaded into the cache but only the requested document is transmitted to clients. Cache servers can save their cache memory space as well as provide fast response to clients. The results show that the average response time of the proposed method decreases to 20% compared with other methods and the cache hit rate is increased to 18% of other methods. 1 Introduction The World Wide Web documents are increasing very rapidly as the internet services are growing very fast. Also the contents of web documents are being changed aperiodically. A lot of web documents including texts and images are transferred through the internet and have important influence on a network performance. Nowadays, most contents delivered to the networks are multimedia data such as image and digital video. The network traffic generated by multimedia data reduces the bandwidth of networks and gives clients as a slow response. In order to reduce access latency, web documents are stored temporarily near clients and are delivered to clients immediately. The basic principle of web caching is that documents referred once have a very high possibility to be referred again in the near future. Therefore, web caching has become one of interesting research areas to guarantee good performance and fast response. Web cache servers make decisions based upon document names and RLs. Instead of storing individual document or whole documents of a site, a collection of popular documents accessed several times will save space complexity and time complexity. Also popular documents are likely to be requested repeatedly in the near future. There are some studies about the relation of user access patterns. Tauscher and Greenberg studied that 60 percent of web documents were requested more than once by the same user [10]. In the web client proxy cache study, uska said that up to 85 percent was the result of multiple users requesting the same documents [11]. A. Laganà et al. (Eds.): ICCSA 2004, LNCS 3045, pp , Springer-erlag Berlin eidelberg 2004

2 An Adaptive Prefetching Method for Web Caches 567 Web caching servers implemented on specialized servers in the network called caching proxies. A proxy requests to a server instead of users and plays the role to return to users with searched documents. Web caching servers can be implemented in various points of the networks. There are three kinds of web caching: forward caching, reverse caching and transparent caching. Forward caching is usually deployed near a user on the edge of a network so that it can serve a many number of users. The use of forward proxy caches benefits in wide-area bandwidth savings with improved response time. Reverse proxy caching is deployed through the network near a server. This is a useful mechanism when supporting web hosting servers. Transparent proxy caches are different from other two methods. This method is not modified by the request of response in anything more than a superficial manner. Then users can not recognize activity of the proxy caches. ocuments which users request repeatedly are saved to a proxy-based caching. Therefore, for the response of users about web document requests, the proxy cache server provides documents to users without going to the original server. As a result, the bandwidth waste and delay of networks are reduced. The possibility to visit identical sites is high when users visit their preferred sites. We propose a variation of the prefetching web caching method to improve the performance and reduce web access latencies by analyzing search patterns of users adaptively. The following section discusses several prefetching algorithms. In section 3, the proposed algorithm of web prefetching is described. Section 4 describes the experiment results are discussed. And we summarize our observations and conclude the paper in Section 5. 2 Related Work Two important factors must be considered to deliver web documents to users efficiently. One is the document search of servers and the other is the traffic of networks. The traffic reduction of networks decreases unnecessary bandwidth waste and improves the response time. It is very hard to reduce the load of networks because the connectivity of networks is very complex. 2.1 Web Caching Issues Many research activities are concentrated on reducing the load of servers by implementing web caching methods. Generally, there are three research areas in web caching: cache replacement policy, cache consistency and prefetching. Cache replacement policy determines what existing documents in the web cache should be replaced when the web cache is full and new documents are loaded. The goal of replacement policy is to optimize the usage of cache space and to improve cache performance. One complication to implement cache replacement policy in the web cache is that documents to be cached are not the same size as the memory cache. There are some of the important cache replacement policies: first-in/first-out (FIFO), least recently used(lr), least frequently used(lf), and so on. Cache consistency is concerned with ensuring that the documents in the cache are the same as documents in the original web servers. There are four well-known cache

3 568 J. Jeon et al. consistency maintenance techniques to deal with detecting such instances: client polling, invalidation callbacks, time to live, and if-modified-since [8]. Commonly Ifmodified-since is used in the web cache to check cache consistency. ocuments are not modified from last access if cache servers receive '304 Not Modified' response. Prefetching is always an important issue in the web cache. The key idea of web prefetching is to save searching time of documents if they are stored in the web cache. Therefore, some documents are predicted to be used in the near future and stored in the cache. The average waiting time of users can be reduced if documents are hit. The web cache needs to estimate what additional contents will be needed in the next few user access, and store some of them to the local disk or memory. If the prefetched content is indeed requested, the user can access without the delay [1]. Table 1. Comparison of Web prefetching methods. Table 1 shows advantages and disadvantages of four prefetching methods. Method Advantage isadvantage Prefetching hyperlinks Simple computation Waste of space Association relation igh hit ratio Burden of servers istory-based Low cache space ifficult to apply in reality Page rank-based igh hit ratio, Low cache space Complex computation 2.2 Prefetching Methods There are four prefetching methods, which are Prefetching hyperlinks, Association relation, istory-based prefetching and Page rank-based prefetching. Table 1 shows comparison of four prefetching methods. Prefetching hyperlinks method uses hyperlinks in documents because it is very high to request a document several times. This prefetching method has applied to all links on the document currently through the web browser [3]. A key drawback of this method is that it typically requires a lot of memory or disk spaces, because all documents of a site must be saved. There is no guarantee that users click the same links on documents. The location and number of documents linked from a document are different. Therefore, if several hundreds links exist on a document, prefetching hyperlinks becomes to be a burden to cache servers without any benefit to users. Finally prefetching weights the load of the networks and brings about the waste of a network bandwidth. Association relation method maintains a graph each other for document requests of users. It brings all objects to be satisfied specific condition if a user requests one document [4], [5]. This method has had the waste of the bandwidth because it must bring all documents which are related to every request. To maintain the association relation to the graph gives a burden to cache servers. istory-based prefetching uses the history log of servers. Web servers keep the list of popular documents and maintain it by requests of clients or proxies. Proxy brings

4 An Adaptive Prefetching Method for Web Caches 569 the popularity list of web documents periodically from web servers[6]. This method is difficult to apply in reality because all web servers don t have a popularity list. Page rank-based prefetching approach uses the link structure of a requested document to determine the most important linked document and to identify the documents to be prefetched [10]. If the requested documents have many links to some important documents, those documents have a higher probability of being the next request. 3 Adaptive Prefetching It is important that cache servers store web documents to be used in the near future. Generally users visit a specific site with similar search patterns. Also web documents of sites are modified or upgraded aperiodically. Therefore, web cache servers update or modify not only documents but also the popularity of documents according to the changes. A new prefetching method is required to improve performance. The underlying premise of the method is that the next documents requested by users are typically based on the previous requested documents. The relative importance of documents is calculated using a hit counter. Popular ocuments General ocuments Level - 0 Level Level Level Fig. 1. Search pattern level of cache servers. Level-0 is a site RL and other levels are linked documents searched by users adaptively. 3.1 Adaptive Prefetching Algorithm When a site is called by users, the main document and the several popular documents are loaded into the cache server at the same time. After documents are loaded, documents are counted by the web cache server whenever they are called. Fig. 1 shows a tree structure of documents to be searched. Every document of a tree has a hit counter. In Fig. 1, Level-0, root node, means a site home-page. All documents increase their hit counter by whenever they are visited by clients. As time elapses, the shaded nodes have larger hit counters than other documents. If users visit

5 570 J. Jeon et al. one of these shaded documents, all shaded documents are loaded into the cache server. In Fig 1, document, 2, 21, and 212 are loaded into cache. The adaptive prefetching groups documents and maintain counters as following four steps: 1. Step 1. Set a threshold value for adaptive prefetching. Set all counters to 0. At the initial stage, the cache server operates as the page-ranked prefetching until it collects more than the threshold value. 2. Step 2. If a node is accessed, add all counters of nodes in the path from the root nodes. For example, if node 21 is accessed in Fig. 1, counters of, 2, and 21 are incremented. 3. Step 3. If a requested document has larger counter value than the threshold, load all documents which are in the link path of the tree. In Fig. 1, if node 212 is requested and its counter value is greater than the threshold value, load, 2, 21 and 212 into the cache server and increment their counters. 4. Step 4. pdate counters of the documents visited by users and decide documents to be removed from the cache or loaded into the cache. 3.2 Adaptive Search Pattern This adaptive prefetching method updates counters of documents to predict the surfing patterns. Fig. 2 shows the adaptive transition of the prefetching document as time elapses. In the beginning, the popular documents are gathered in the right side of the tree. The popularity is changed to documents in the left sided of the tree after time has elapsed. The cache server uses a threshold value to decide whether documents are loaded into cache. The threshold value is evaluated by the percentage ratio of total visited numbers. The cache server needs to optimize the tradeoff between its resource usage and the hit ratio. The hit ratio indicates how many times requested documents of users hit on the prefetched cache contents. The server resource indicates memory spaces, disk spaces and the cost of processing the prefetcher. At the beginning, the threshold value is set between 1% to 5% and the cache server will prefetch documents with a relative access probability equal to or greater than the threshold. Since the size of prefetched documents affects the resource usage, the threshold may increase or decrease adaptively. Therefore it prevents the waste of cache spaces and maintains high prediction ratio. 4 Simulation The squid simulation tool is used to test, verify and evaluate the proposed algorithm[13]. In the simulations, the trace logs of the cache hits are analyzed. arious experiments have been conducted to compare the response times. The results of simulations show that the response time of the proposed method is less than that of the prefetching hyperlinks.

6 An Adaptive Prefetching Method for Web Caches (a) time = 0 (b) time = n Fig. 2. Example of search pattern change. ot documents in the cache server are changed adaptively as time elapses. P L 7 Q R S 5 J Y $ 3 I W FKL QJ +\S O L QN $GW L Y 3 I W FKL QJ &FK 6L ] *% Fig. 3. Comparison of average response time of prefetching hyperlinks and adaptive. The response time of the adaptive prefetching method shows faster than that of the prefetching hyperlinks for smaller cache size. Fig. 3 shows differences of the response time depending upon the cache sizes. The response time of the adaptive prefetching shows faster response time that of the prefetching hyperlinks for smaller cache size. As the cache size increases, the differences of the response time are smaller and smaller. The reason is that the prefetching hyperlinks method has enough space to hold data in the cache as the cache size is increased. This means that prefetching hyperlinks requires much more cache space for prefetching.

7 572 J. Jeon et al. P L 7 Q R S 5 J Y $ 3 I W FKL QJ +\S O L QN $GSW LY3IWFKLQJ '\ Fig. 4. Comparison of average response time of the prefetching hyperlinks and the adaptive prefetching for a week. The response tome of the adaptive prefetching method shows faster than that of the prefetching hyperlinks as time elapses. 3 I W FKL QJ +\S O L QN $GWLY3IWFKLQJ P L 7 Q R S 5 J Y $ $ % & ' ( :E 6L W Fig. 5. Comparison of average response time of the prefetching hyperlinks and the adaptive prefetching for the five different web sites. 3IWFKLQJ+\SOLQN $GSWLY 3IWFKLQJ % 0 W I Q W W ' +R X Fig. 6. Comparison of data transfer rate during 24 hours. The adaptive prefetching method transfers more data to clients. This means that cache hit rate of the adaptive prefetching method is higher than that of the prefetching hyperlinks.

8 An Adaptive Prefetching Method for Web Caches 573 Fig. 4 shows that the response time of the adaptive prefetching method shows 20% faster than that of prefetching hyperlinks as the cache server runs several days. At the beginning, the adaptive prefetching method collects data of search patterns. Also it does not have enough information to decide the proper threshold value. Therefore, first several days the response time of the proposed method is very close to that of the prefetching hyperlinks. After several days pass, the adaptive prefetching shows better performance. Fig. 5 compares the average response time of the two different methods for the five different web sites. The size and depth of the prefetching documents of each site are different. But each site contains documents, graphics, and multimedia data such as audio, video and etc. The results in Fig. 5 show that the adaptive prefetching performs better than the prefetching hyperlinks. In case of the site, the response time of the adaptive prefetching is about 40 % faster than that of the prefetching hyperlinks. ata transfer rate is compared to analyze the performance. ata transfer rate during 24 hours is shown in Fig. 6. The adaptive prefetching method transfers data about 18% higher than prefetching hyperlinks does. This means that cache hit rate of the adaptive prefetching method is higher than that of the prefetching hyperlinks. The adaptive prefetching saves and manages more popular documents in the web cache. 5 Conclusion The proposed algorithm, adaptive prefetching, is tested under the Squid cache environment. The average response time of the adaptive prefetching is much faster than that of the prefetching hyperlinks. The average response time of the adaptive prefetching method is decreased to 20% compared with the prefetching hyperlinks when cache server runs for a week. The adaptive prefetching method shows very good performance for the small cache sizes and provides potential benefits to cache servers which frequent cache replacements are expected. By measuring data transfer rate, the performance of the adaptive prefetching method shows 18% higher. This means that the adaptive prefetching method manages the web cache efficiently. Since the contents of web sites are modified aperiodically, search patterns are changing continuously. Also the size of documents becomes large and they are stored as disorganized. To solve these problems, the simulation results have revealed the possibility of the adaptive popularity-based prediction method. The adaptive prefetching based upon search patterns of users is an effective web caching method because it satisfies high prediction accuracy as well as a low space requirement. References 1. Z. Jiang and L. Kleinrock : An Adaptive Network Prefetch Scheme, IEEE J. Selected Areas Comm. ol. 17, no. 4 (1998) L. Breslau, P. Cao, L. Fan, G. Philips, and S. Shenker : Web Cache and Zipf-like istributions: Evidence and Implications, In Preceedings of Infocom (1999) 3.. uchamp : Prefetching yperlinks : Proc. of senix Symp. Internet Technologies and Systems, senix (1999)

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