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Related Work

2 Related Work

Here we survey 14 prior separate efforts relevant to prefetching in the Web, divided into three categories: software systems; papers describing algorithms, simulations, and/or prototypes; and papers that establish bounds.

2.1 Software Systems

Smiley is similar to our work in that prefetching is decided by the client based on usage statistics about embedded HREFs. The client also continually monitors its available bandwidth. One major difference is that images embedded within prefetched pages are not themselves prefetched. The Smiley implementation is only a demonstration, and results [16, 17] are obtained by a simulation study of accesses to two frequently accessed pages at UCLA.

Major differences in the gathering and use of usage statistics between Smiley and our work are that there is no method for clients to inform servers of their usage patterns, and that usage statistics are gathered not in real time but over many days and then they are not aged.

Although Smiley does not include a method for clients to inform servers of their usage patterns, client-side and server-side observations of usage patterns are merged in the simulation. Jiang and Kleinrock conclude that using server-side statistics in combination with those of the client yields a higher hit rate than using client-side statistics alone. A basic hypothesis of our work is that an individual client can prefetch accurately based upon usage statistics gathered from a large population. The Smiley results suggest that our hypothesis is sound.

Wcol [5] is a research prototype available on the Web. It prefetches embedded hyperlinks top-to-bottom without regard to likelihood of use. Embedded images of prefetched pages are also prefetched. Bandwidth waste can be capped by configuring Wcol to prefetch no more than a certain number of hyperlinks, and no more than a certain number of images embedded within prefetched hyperlinks.

PeakJet2000 [26] is the second major version of a commercial product. PeakJet runs on the client machine. It maintains a separate cache and provides a set of tools for speeding Web access, some of which require user action. True prefetching exists in two modes, ``history-based'' and ``link-based.'' The user picks the mode. History-based prefetching prefetches an embedded link only if that client has used it before (i.e., it performs an IMS GET of a cached page). Link-based prefetching prefetches all embedded HREFs.

NetAccelerator 2.0 [24] is a similar commercial product for Windows clients. Unlike PeakJet, it prefetches into the browser's disk cache. Its prefetching algorithm is the same as PeakJet's link-based prefetching: all hyperlinks and their embedded images are prefetched.

2.2 Algorithms and Simulations

The method proposed by Bestavros in [2] is that ``a server responds to a clients' [sic] request by sending, in addition to the data (documents) requested, a number of other documents that it speculates will be requested by that client in the near future.''

A Markov algorithm is used to determine related ``documents.'' The server examines the reference pattern of each client separately. Two documents are considered related if the client has accessed them in the past within a certain time interval. When a document is fetched, the server also pushes to the client any other document that is transitively related to it and whose likelihood of use is greater than a threshold value.

Usage statistics are gathered over 60 days, updated daily. Documents are considered related if requests from same client come within 5 seconds of each other. Among the results of this study are (1) that more recent usage statistics yield better results, as does more frequent updating of the ``related'' relation; (2) and ``speculation is most effective when done conservatively.'' That is, with each incremental decrease in client latency, the extra bandwidth consumed and extra load imposed upon the server becomes greater.

The main point of the Dynamic Documents prototype [18] was to investigate how implementing documents as programs rather than static files might provide a means to help mobile clients adjust to enormous variations in bandwidth. Prefetching was added more as demonstration of a possibility rather than as a serious proposal. Pages in the history list are prefetched, with the result that bandwidth use is increased approximately 50% but only 2% of prefetched pages are used.

In the work of Padmanabhan and Mogul [25], a server maintains per-client usage statistics and determines related-ness through a graph-based Markov model similar to that of Bestavros. The graph contains a node for ``every file that has ever been accessed'' and is updated off-line, nightly for example. Related-ness is determined by edges through the nodes weighted by the probability that one will be accessed soon after the other. Whereas Bestavros defines ``soon'' by an amount of time, Padmanabhan and Mogul define it by a number of accesses from the client that occur in between.

When a GET is serviced, the server calculates a list of its pages that are likely to be requested in the near future, using some probability threshold. This list is appended to the GET response, and the client decides whether to actually prefetch. Another HTTP extension allows the client to indicate to the server that a certain GET is a prefetch, so that the server will not recursively compute related-ness for the prefetched page.

Trace-driven simulations show that average access time can be reduced approximately 40%, at the cost of much increased network traffic (70%). Another result suggests that prefetching is more beneficial than increasing bandwidth. That is, when prefetching causes a 20% increase in traffic, the latency is lower than it would be without prefetching but with 20% extra bandwidth. A third result is that prefetching increases access time variability, but very little.

The basic idea of Top10 [21] is for servers (and proxies) to publish their most-accessed pages (``Top 10''). Downstream components (clients and proxies) prefetch some fraction of the list. The approach is parameterized two ways. One parameter indicates how many times a client must have contacted a server before it will prefetch at all. The other parameter indicates the maximum number of pages it will prefetch from a server. Results are by trace-driven simulation with traces from 5 sites. As more pages are prefetched, the percent of prefetched pages that are eventually used rises quickly and levels off at between 3% and 23%, depending on the trace. This suggests that the size of ``TopN'' should be small.

Fan et al. [13] evaluates several techniques for reducing client requests and observed latency. The evaluation is based mostly on trace-driven simulations of dialup users [15]. The authors have also implemented their prefetching ideas using CERN httpd. Pages are prefetched into a simulated browser cache only from a shared proxy cache, never from servers, so no extra wide-area traffic is generated. Consequently, prefetching is limited by the degree to which one client is expected to use a page that it or some other client sharing the same proxy has used in the past, and which is still in the proxy's cache.

Simulation results indicate that (perfect) prefetching and delta-compression [22] reduce latency considerably more than either HTML compression or merely increasing the size of the browser's cache. Using all three techniques with a finite browser cache resulted in only 30.3% latency reduction. However, prefetching reduced the number of client requests by 50%.1 The 50% request savings, in turn, is limited by the fact that pages are prefetched only from the proxy.

Image objects were prefetched more often than HTML objects (64-74% versus 13-18%), and the prediction accuracy was higher for image objects (approximately 65% for JPEG and 58% for GIF versus 35% for HTML and ``other'' types). One possible explanation for this pattern is that this work uses a Markov-model prediction algorithm. Such algorithms view objects as independent, and often simply re-discover which images are embedded in an HTML page.

In the implementation, there is a proxy on the client side and one on the modem side. As in our work, the path of a request is browser to client-side proxy to modem-side proxy to server. Also like our work, The client-side proxy apparently piggybacks hit info on requests. After the modem-side proxy processes a request, it keeps the connection open, generates a list of URLs to prefetch, and ``pushes'' them into the client-side proxy's cache. The proxy prefetches only items in its cache. The predictor at the modem-side proxy remembers the client's last few requests but does not know the state of its cache, resulting in possible duplication.

Cohen and Kaplan [6] investigate three other types of prefetching: opening an HTTP connection to a server in advance of its (possible) use (pre-connecting); resolving a server's name to an IP address in advance of opening a connection to the server (pre-resolving); and sending a dummy request (such as a HEAD) to the server in advance of the first real request (pre-warming). Pre-connecting is motivated by the substantial overhead of TCP connection establishment. Pre-resolving is a subset of pre-connecting: the only part of a GET request done in advance is to translate the server's name into an IP address. Pre-warming is a superset of pre-connecting: its purpose is to force the server to perform one-time access control checking in advance of demand requests. In a trace-driven simulation, the three techniques reduced the number of ``session starting'' HTTP interactions whose latency exceeded 4 seconds from 7% to 4.5%, 2%, and 1%, respectively. This work represents a more conservative approach to prefetching than our own: much less complex, more likely to work without unintended consequences, and less capable of reducing latency.

Cunha's work [11] presents a very simple browser prefetch mechanism plus two mathematical models taken from prior work on other topics, that are used to indicate whether the mechanism should be invoked. His dissertation [12] provides additional detail not supplied in the paper, including recognition of the complications of file size and the need to age the usage information.

The prefetch model is that only the client is active in gathering usage information and making prefetch decisions. Hence, a user prefetches only pages that he has accessed before. The earlier references resulted in Markov chains with three types of links indicating whether objects that were accessed within a time window are unrelated, related by one being embedded in the other, or related simply by being likely to be accessed at about the same time.

The mathematical models -- based on DRAM caches and linear predictive coding for speech processing -- attempt to classify a user's behavior as ``surfing'' or ``conservative.'' Surfing behavior references many different URLs whereas conservative behavior frequently re-references the same URLs. Very high hit rates (e.g., over 80%) are possible when the user's behavior fits the model.

2.3 Bounds

One of the results of the Coolist papers [27, 28] is a mathematical analysis of how accurate prefetch predictions must be (or, alternatively, how lightly loaded the network must be) in order for prefetches not to interfere with demand fetches and thereby increase the average latency of all fetches. The formula is R < E/(1+E), where R is network utilization without prefetching and E is the ratio of hit rate with prefetching to traffic increase caused by prefetching. The papers also present a taxonomy of prefetching approaches that range from conservative to aggressive in their use of bandwidth, and ``Coolist,'' a prefetcher that allows the user to choose the level of aggressiveness for prefetching user-specified groups of pages.

The surprising conclusion of Crovella and Barford's trace-driven simulations [9, 10] is that prefetching makes traffic burstier and thereby worsens queueing. This is surprising because, generally speaking, a side effect of prefetching is to smooth short ``spikes'' of demand fetching into longer ``trickles'' of prefetching. The explanation lies in the definition of prefetching: at the start of a session all the files to be accessed in that session are prefetched immediately, creating an initial burst of demand. Crovella and Barford provide a solution, ``rate-controlled prefetching,'' that smoothes out traffic. Rate-controlled prefetching approximates realistic prefetching, where the lookahead is limited. The analysis of rate-controlled prefetching also uses a better definition of prefetching -- pages are prefetched one at a time and not with perfect accuracy. Such rate-controlled prefetching significantly reduces queue length over a wide range of prefetch accuracies.

An important study by Kroeger et al. [19] establishes bounds on the latency reduction achievable through caching and prefetching, under idealized conditions. Their most widely noted conclusion is that, even employing an unlimited cache and a prefetch algorithm that knows the future, at most 60% latency reduction can be achieved.

However, this study assumes (1) that ``query events and events with cgi-bin in their URL cannot be either prefetched or cached'' and (2) prefetching will always miss on the first access to a particular server. Neither is true in our work. The first assumption does not apply to our work because it is appropriate for a cache shared by several users, but our algorithm prefetches into a user-specific cache. Because many URLs represent queries or dynamic content (16.3% as shown in Section 3.2), removing this assumption in particular could yield an upper bound significantly above 60%.

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