A Survey of Proxy Cache Evaluation Techniques

Brian D. Davison
Department of Computer Science
Rutgers, The State University of New Jersey
New Brunswick, NJ 08903 USA
davison@cs.rutgers.edu, http://www.cs.rutgers.edu/~davison/


Proxy caches are increasingly used around the world to reduce bandwidth requirements and alleviate delays associated with the World-Wide Web. In order to compare proxy cache performances, objective measurements must be made. In this paper, we define a space of proxy evaluation methodologies based on source of workload used and form of algorithm implementation. We then survey recent publications and show their locations within this space.

1 Introduction

Proxy caches are increasingly used around the world to reduce bandwidth and alleviate delays associated with the World-Wide Web. This paper describes the space of proxy cache evaluation methodologies and places current research within that space. The primary contributions of this paper are threefold: 1) definition and description of the space of evaluation techniques; 2) appraisal of the different methods within that space; and 3) a survey of cache evaluation techniques from the research literature.

In the next section we provide background into web caching, including the levels of caching present on the web and and an overview of some of the current research issues in web proxy caching. We then describe current proxy cache evaluation methods and place existing research in this space.

2 Background

Caching has a long history and is a well-studied topic in the design of computer memory systems (e.g. [HP87, Man82, MH99]), in virtual memory management in operating systems (e.g. [Dei90, GC97]), in file systems (e.g. [CFKL95]), and in databases (e.g. [SSV96]). Caching on the Internet is also performed for other network services such as DNS [Moc87a, Moc87b], Gopher and FTP [Pet98, Wes98, RH98, Cat92], and in fact much of today's web caching research can be traced back to the effort to reduce the bandwidth used by FTP [DHS93].

2.1 Web caching

Web caching is the temporary storage of web objects (such as HTML documents) for later retrieval.* Proponents of web caching claim three significant advantages to web caching: reduced bandwidth consumption (fewer requests and responses that need to go over the network), reduced server load (fewer requests for a server to handle), and reduced latency (since cached responses are available immediately, and closer to the client being served). A fourth is sometimes added: more reliability, as some objects may be retrievable via cache even when the original servers are not reachable. Together, these features can make the World Wide Web less expensive and better performing. One drawback of caching is the potential of using an out-of-date object stored in a cache instead of fetching the current object from the origin server. Another is the lack of logs of client viewings of pages for the purposes of advertising (although this is being addressed, e.g. [ML97]).

Caching can be performed by the client application, and is built into virtually every web browser. There are a number of products that extend or replace the built-in caches* with systems that contain larger storage, more features (such as keeping commonly used pages up-to-date and prefetching likely pages), or better performance (such as faster response times as a result of better caching mechanisms). However, while these systems cache network objects from many servers, they do so for a single user.

Caching can also be utilized between the client and the server as part of a proxy. Proxy caches are often located near network gateways to reduce the bandwidth required over expensive dedicated Internet connections. When shared with other users, these systems serve many clients with cached objects from many servers. In fact, much of the usefulness (up to 85% of the in-cache requests [DMF97]) is in caching objects requested by one client for later retrieval by another client. For even greater performance, many proxy caches are part of cache hierarchies, in which a cache can request documents from neighboring caches instead of fetching them directly from the server [CDN+96].

Finally, caches can be placed directly in front of a particular server, to reduce the number of requests the server must handle. Most proxy caches can be used in this fashion, but this form is often called a reverse cache or sometimes an httpd accelerator to reflect the fact that it caches objects for many clients but usually from only one server [CDN+96, Wes98].

2.2 Proxy caching research

A shared proxy cache serves a population of clients. When a proxy cache receives a request for a web object, it checks to see if the response is available in memory or disk, and if so, returns the response to the client without disturbing the upstream network connection or destination server. If it is not available in the cache, the proxy attempts to fetch the object directly. However, if at some point the space required to store all the objects being cached exceeds the available space, the proxy will need to replace an object from the cache. In general, cache replacement algorithms attempt to maximize the percentage of requests successfully fulfilled by the cache (called the hit ratio) by holding onto the items most likely to be requested in the future. However, a number of researchers argue that access cost should be included in replacement calculations [BH96, CI97, WA97, SSV97, SSV98, SSV99].

Even though clients can only retrieve documents from a cache, there is still the issue of cache consistency, as source documents may be updated after the document was loaded into the cache [GS96, CDN+96, SSV98, SSV99]. Therefore, proxy caches generally do not guarantee strong consistency since there is a possibility of returning an out-of-date object to the client.

Some caching proxies exist as extensions or options of HTTP servers, such as the publicly available servers Apache [Apa98] and Jigsaw [Wor98]. Proxies are also available as stand-alone systems, such as the publicly available Squid [Wes98] and the commercial CacheFlow [Cac98a], Cisco Cache Engine [Cis98], and the Microsoft Proxy Server [Mic98]. While somewhat dated, Cormack [Cor96] provides a good introduction to web caching and includes an overview of many of these caching systems and a few others.

Much like the memory hierarchy of today's hardware systems, many researchers claim benefits of building hierarchies of web caches [CDN+96, Nat98, Dan98]. Difficulties include finding a nearby cache [CC95, CC96, ZFJ97], knowing what is in the neighboring caches [GRC97, GCR97, FCAB98, RCG98], and communication between caches [WC97b, WC97a, RW98, VR98, Vix98].

3 Evaluating Proxy Cache Performance

It is useful to be able to assess the performance of proxy caches, both for consumers selecting the appropriate system for a particular situation and also for developers working on alternative caching mechanisms. By evaluating performance across all measures of interest, it is possible to recognize drawbacks with particular implementations. For example, one can imagine a proxy with a very large disk cache that provides a high hit rate, but because of poor disk management it noticeably increases the client-perceived latency. One can also imagine a proxy that prefetches inline images but releases those images from the cache shortly afterward (to make room for other objects). This would allow it to report high request hit rates, but not perform well in terms of bandwidth savings. Finally, it is possible to select a proxy based on its performance on a workload of requests generated by dialup users and have it perform unsatisfactorily as a parent proxy for a large corporation with other proxies as clients. These examples illustrate the importance of appropriate evaluation mechanisms for proxy systems.

3.1 The Space of Cache Evaluation Methods

The most commonly used cache evaluation method is that of simulation on a benchmark log of object requests. The byte and page hit rate savings can then be calculated as well as estimates of latency improvements. In a few cases, an artificial dataset with the necessary characteristics (appropriate average and median object sizes, or similar long-tail distributions of object sizes and object repetitions) is used. More commonly, actual client request logs are used since they arguably better represent likely request patterns and include exact information about object sizes and retrieval times.

  Workload Source
Algorithm Implementation artificial captured logs
simulated systems/networks A1 A2
real systems/isolated network B1 B2

Table 1: The space of traditional evaluation methodologies for web systems.  

We characterize web/proxy evaluation architectures along two dimensions: the source of the workload used and form of algorithm implementation. Table 1 shows the traditional space with artificial vs. captured logs and simulations vs. implemented systems. With the introduction of prefetching proxies, the space needs to be expanded to include implemented systems on a real network connection, and in fact, evaluation of an implemented proxy in the field would necessitate another column, that of live requests. Thus, the expanded view of the space of evaluation methodologies can be found in Table 2 and shows that there are at least three categories of data sources for testing purposes, and that there are at least three forms of algorithm evaluation. While it may be possible to make finer distinctions within each area, such as the representativeness of the data workload or the fidelity of simulations, the broader definitions used in the expanded table form a simple yet useful characterization of the space of evaluation methodologies.

  Workload Source
Algorithm Implementation artificial captured logs current requests
simulated systems/networks A1 A2 A3
real systems/isolated network B1 B2 B3
real systems/real network C1 C2 C3

Table 2: The expanded space of evaluation methodologies for web systems.  

For the systems represented in each area of the space, we find it worthwhile to consider how the desired qualities of web caching (reduced bandwidth/server load and improved response time) are measured. As will be seen below, each of the areas of the space represents a trade-off of desirable and undesirable features. In the rest of this section we point out the characteristic qualities of systems in each area of the space and list work in web research that can be so categorized.

3.2 Methodological Appraisal

In general, both realism and complexity increase as you move diagonally downward and to the right in the evaluation methodology space. Note that only methods in areas A1, A2, B1 and B2 are truly replicable, since live request stream samples change over time, as do the availability and access characteristics of hosts via a live network connection.

893252015.307 14 TCP_HIT/200 227 GET
    http://images.go2net.com/metacrawler/images/transparent.gif - NONE/- image/gif
893252015.312 23 TCP_HIT/200 4170 GET
    http://images.go2net.com/metacrawler/images/head.gif - NONE/- image/gif
893252015.318 38 TCP_HIT/200 406 GET
    http://images.go2net.com/metacrawler/images/bg2.gif - NONE/- image/gif
893252015.636 800 TCP_REFRESH_MISS/200 8872 GET
    http://www.metacrawler.com/ - DIRECT/www.metacrawler.com text/html
893252015.728 355 TCP_HIT/200 5691 GET
    http://images.go2net.com/metacrawler/images/market2.gif - NONE/- image/gif
893252016.138 465 TCP_HIT/200 219 GET
    http://images.go2net.com/metacrawler/templates/tips/../../images/pixel.gif -
    NONE/- image/gif
893252016.430 757 TCP_REFRESH_HIT/200 2106 GET
    http://images.go2net.com/metacrawler/templates/tips/../../images/ultimate.jpg -
    DIRECT/images.go2net.com image/jpeg

Figure 1: This excerpt of a proxy log generated by Squid 1.1 records the timestamp, elapsed-time, client, code/status, bytes, method, URL, client-username, peerstatus/peerhost and objecttype for each request. It is also is an example of how requests can be logged in an order inappropriate for replaying in later experiments.  

As can be seen, caching mechanisms are most commonly evaluated by simulation on captured client request logs like the sample log shown in Figure 1. These logs are generally recorded as the requests pass through a proxy, but it is also possible to collect logs by packet sniffing (as in [WWB96, MDFK97, GB97]) or by appropriately modifying the browser to perform the logging (e.g. [TG97, CBC95, CP95]).

Simulated systems.
Simulation is the simplest mechanism for evaluation as it does not require full implementation. However, simulating the caching algorithm requires detailed knowledge of the algorithms which is not always possible (especially for commercial implementations). Even then, simulation cannot accurately assess document retrieval delays [WA97].

It is also impossible to accurately simulate caching mechanisms that prefetch on the basis of the contents of the pages being served (termed content-based prefetching), such as those in CacheFlow [Cac98a] and Wcol [CY97], since they need at least to have access to the links within web pages -- something that is not available from server logs. Even if page contents were logged (as in [CBC95, MDFK97]), caches that perform prefetching may prefetch objects that are not on the user request logs and thus have unknown characteristics such as size and web server response times.

Real systems/isolated networks.
In order to combat the difficulties associated with a live network connection, measurement techniques often eliminate the variability of the Internet by using local servers and isolated networks, which may generate unrealistic expectations of performance. In addition to not taking into consideration current web server and network conditions, isolated networks do not allow for retrieval of current content (updated web pages).

Real systems and networks.
Because the state of the Internet changes continuously (i.e. web servers and intermediate network connections may be responsive at one time, and sluggish the next), tests on a live network are generally not replicable. Additionally, to perform such tests the experiment requires reliable hardware, robust software, and a robust network connection to handle the workload applied. Finally, connection to a real network requires compatibility with, and no abuse of, existing systems (e.g. one cannot run experiments that require changes to standard httpd servers or experimental extensions to TCP/IP).

Artificial workloads.
Synthetic traces are often used to generate workloads that have characteristics that do not currently exist to help answer questions like whether the system can handle twice as many requests per second, or whether caching of non-web objects is feasible. However, artificial workloads often make significant assumptions (e.g. that all objects are cachable, or that requests follow a particular distribution) which are necessary for testing, but not necessarily known to be true.

Captured logs.
Using actual client request logs can be more realistic than artificial workloads, but since on average the lifetime of a web page is short (less than two months [Wor94, GS96, Kah97, DFKM97]), any captured log loses its value quickly as more references within it are no longer valid, either by becoming inaccessible or by changing content (i.e., looking at a page a short time later may not give the same content). In addition, unless the logs are recorded and processed carefully, it is possible for the logs to reflect an inaccurate request ordering as the sample Squid logs show in Figure 1. Note that the request for the main page follows requests for three subitems of that main page. This is because entries in the log are recorded when the request is completed, and the timestamp records the time at which the socket is closed. Finally, proxy cache trace logs are inaccurate when they return stale objects since they may not have the same characteristics as current objects. Note that logs generated from non-caching proxies or from HTTP packet sniffing may not have this drawback. In other work [Dav99b], we further examine the drawbacks of using standard trace logs and investigate what can be learned from more complete logs that include additional information such as page content.

Current request stream workloads.
Using a live request stream produces experiments that are not reproducible (especially when paired with live hardware/networks). Additionally, the test hardware and software may have difficulties handling a high real load. On the other hand, if the samples are large enough and have similar characteristics, an argument for comparability might be made.

3.3 Placement of current work in the space of methodologies

In this section, we place work from the research literature into the evaluation space described above. Note that inclusion/citation in a particular area does not necessarily indicate the main thrust of the paper mentioned.

Area A1: Simulations using artificial workloads.
It can be difficult to characterize a workload sufficiently to be able to generate a credible artificial workload. This, and the wide availability of a number of proxy traces, means that only a few researchers attempt this kind of research:

Area A2: Simulations using captured logs.
Simulating proxy performance is much more popular than one might expect from the list of research in area A1. In fact, the most common mechanism for evaluating an algorithm's performance by far is simulation over one or more captured traces.

Area A3: Simulation based on current requests.
We are aware of no published research that could be categorized in this area. The algorithms examined in areas A1 and A2 do not need the characteristics of live request streams (such as contents rather than just headers of HTTP requests and replies). Those researchers who use live request streams all use real systems of some sort (as will be seen below).

Area B1: Real systems on an isolated network using an artificial dataset.
A number of researchers have built tools to generate workloads for web systems in a closed environment. Such systems make it possible to generate workloads that are uncommon in practice (or impossible to capture) to illuminate implementation problems. These include:

Area B2: Real systems on an isolated network using captured trace logs.
Some of the research projects listed in area B1 may be capable of using captured trace logs. In addition, we place the following here:

Area B3: Real systems on an isolated network using current requests.
Like area A3, we found no research applicable to this area. Since live requests can attempt to fetch objects from around the globe, it is unlikely to be useful within an isolated network.

Area C1: Real systems on a live network using an artificial dataset.
Some of the research projects in area B1 may also be extensible to the use of a live network connection. The primary restriction is the use of real, valid URLs that fetch over the Internet rather than particular files on a local server.

Area C2: Real systems on a live network using captured logs.
With captured logs, the systems being evaluated are as realistically operated without involving real users as clients. Additionally, some of the tools from area B1 may be usable in this type of experiment.

Area C3: Real systems on a live network using the current request stream.
In many respects, this area represents the strongest of all evaluation methodologies. However, most real installations are not used for the comparison of alternate systems or configurations, and so we report on only a few research efforts here:

4 Discussion and Conclusions

Evaluation is a significant concern, both for consumers and for researchers. Objective measurements are essential, as are comparability of measurements from system to system. Furthermore, it is important to eliminate variables that can affect evaluation.

Selection of an appropriate evaluation methodology depends on the technology being tested and the desired level of evaluation. Some technologies, such as protocol extensions, are often impossible to test over a live network because other systems do not support it. Similarly, if the goal is to find bottlenecks in an implementation, one may want to stress the system with very high synthetic loads since such loads are unlikely to be available in captured request traces. In fact, this reveals another instance in which a live network and request stream should not be used -- when the goal of the test is to drive the system into a failure mode to find its limits.

In general, we argue for the increased believability of methodologies in the evaluation space as you move down and to the right when objectively testing the successful performance of a web caching system. If the tested systems make decisions based on content, a method from the bottom row is likely to be required. When looking for failure modes, it will be more useful to consider methodologies near the upper left of the evaluation space.

This survey provides a sampling of the published web-caching research and presents one of potentially many spaces of evaluation methodologies. In particular, we haven't really considered aspects of testing inter-cache communication, cache consistency, or low-level protocol issues such as connection caching which are significant in practice [CDF+98].

In this paper we have described a space of evaluation methodologies and shown where current research efforts fall within it. By considering the space of appropriate evaluation methodologies, one can select the best trade-off of information provided, implementation costs, comparability, and relevance to the target environment.


Thanks are due to Haym Hirsh, Brett Vickers, and anonymous reviewers for their helpful comments on earlier drafts of this paper.


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Junho Shim, Peter Scheuermann, and Radek Vingralek. Proxy Cache Design: Algorithms, Implementation and Performance. IEEE Transactions on Knowledge and Data Engineering, 1999. To appear.

Linda Tauscher and Saul Greenberg. How People Revisit Web Pages: Empirical Findings and Implications for the Design of History Systems. International Journal of Human Computer Studies, 47(1):97-138, 1997.

Renu Tewari, Harrick M. Vin, Asit Dan, and Dinkar Sitaram. Resource-based Caching for Web Servers. In Proceedings of the SPIE/ACM Conference on Multimedia Computing and Networking (MMCN), San Jose, CA, January 1998.

Paul Vixie. Hyper Text Caching Protocol -- HTCP/0.0. Internet Draft, March 1998.

Vinod Valloppillil and Keith W. Ross. Cache Array Routing Protocol v1.0. Internet Draft, February 1998.

Roland P. Wooster and Marc Abrams. Proxy caching that estimates page load delays. In Proceedings of the Sixth International World Wide Web Conference, pages 325-334, Santa Clara, CA, April 1997.

Stephen Williams, Marc Abrams, Charles R. Standridge, Ghaleb Abdulla, and Edward A. Fox. Removal Policies in Network Caches for World-Wide Web Documents. In Proceedings of ACM SIGCOMM, pages 293-305, Stanford, CA, 1996. Revised March 1997.

Duane Wessels and Kimberly Claffy. Application of Internet Cache Protocol (ICP), version 2. RFC 2187, http://nic.ddn.mil/ftp/rfc/rfc2187.txt, September 1997.

Duane Wessels and Kimberly Claffy. Internet Cache Protocol (ICP), version 2. RFC 2186, http://nic.ddn.mil/ftp/rfc/rfc2186.txt, September 1997.

Duane Wessels. Squid Internet object cache documentation. Available at http://squid.nlanr.net/Squid/, 1998.

Roland Peter Wooster. Optimizing response time, rather than hit rates, of WWW proxy caches. Master's thesis, Virginia Polytechnic Institute, December 1996. http://scholar.lib.vt.edu/theses/materials/public/etd-34131420119653540/etd-title.html.

Kurt Worrell. Invalidation in large scale network object caches. Master's thesis, University of Colorado, Boulder, CO, 1994.

World Wide Web Consortium. Jigsaw HTTP server documentation. Available at http://www.w3c.org/Jigsaw/, 1998.

Roland O. Wooster, Stephen Williams, and Patrick Brooks. HTTPDUMP: Network HTTP packet snooper. Working paper available at http://www.cs.vt.edu/~chitra/work.html, April 1996.

Lixia Zhang, Sally Floyd, and Van Jacobson. Adaptive web caching. In NLANR Web Cache Workshop, Boulder, CO, June 1997.

Actually, web caches store HTTP responses, but we will use the more generic phrase, web object, as a simplification throughout.
See http://www.web-caching.com/ for lists of proxies and browser extensions.

Brian D. Davison
Sun Mar 28 22:12:57 EST 1999