An overview of web caching replacement algorithms

Balamash, Abdullah Saeed; Krunz, Marwan

doi:10.1109/comst.2004.5342239

Cited by 135 publications

(82 citation statements)

References 24 publications

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“…When an eviction is required, Size removes the largest object currently in the cache with the idea that users are less likely to rerequest larger objects. Smaller objects, then, are more likely to remain in the cache long-term [3]. Additionally, this allows for a larger number of objects to remain in the cache, potentially improving hit rates for some traffic patterns.…”

Section: Size Algorithmmentioning

confidence: 99%

“…The basic premise of the algorithm is that those objects that are most likely to be accessed will have been accessed more recently than those that are not as likely. While simple to implement and requiring less computational power than most other algorithms, LRU has been outclassed by several other replacement algorithms: Balamash and Krunz's experiments showed that for large cache sizes, LUV, GDS, and Hyper-G produced better results for both HR and LR [3], while Bahn et al found that for large cache sizes, LUV, Hybrid, Size, Mix, and sw-LFU performed better for HR and LUV was better for LR [5].…”

Section: A Least Recently Used (Lru) Algorithmmentioning

confidence: 99%

“…Other effectiveness measures include reduced packets, or the ratio of network packets avoided due to caching to the total packets that would have otherwise been seen by the server; and reduced hops, a similar measure that focuses on network hops between client and server. Balamash and Krunz define the most common measures as follows [3]: …”

Section: Cache Replacement Policiesmentioning

confidence: 99%

“…While some algorithms utilize a single traffic trait, others employ a functional approach to compute a derived ‗cost' of an object cache miss based on multiple parameters, thus removing the lowest ‗cost' object when a replacement is required. Not surprisingly, the research has shown that functional approaches are generally more computationally complex than those based on a single attribute [3]. Due to the variety of approaches, metrics, and parameters utilized (sometimes singly and sometimes in combination), each algorithm has distinct performance characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Since the Web has evolved, several caching algorithms have been suggested in literature; Balamash and Krunz's survey of replacement algorithms described no fewer than twenty different replacement schemes [3]. Each strategy utilizes different parameters to determine objects to replaceBalamash classified caching policies based on whether they utilized frequency, recency, or size information.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Adaptive Cache Replacement:A Novel Approach

Elfayoumy¹,

Warden²

2014

ijacsa

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Abstract-Cache replacement policies are developed to help insure optimal use of limited resources. Varieties of such algorithms exist with relatively few that dynamically adapt to traffic patterns. Algorithms that are tunable typically utilize offline training mechanisms or trial-and-error to determine optimal characteristics.Utilizing multiple algorithms to establish an efficient replacement policy that dynamically adapts to changes in traffic load and access patterns is a novel option that is introduced in this article. A simulation of this approach utilizing two existing, simple, and effective policies; namely, LRU and LFU was studied to assess the potential of the adaptive policy. This policy is compared and contrasted to other cache replacement policies utilizing public traffic samples mentioned in the literature as well as a synthetic model created from existing samples. Simulation results suggest that the adaptive cache replacement policy is beneficial, primarily in smaller cache sizes.

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Section: Size Algorithmmentioning

confidence: 99%

Section: A Least Recently Used (Lru) Algorithmmentioning

confidence: 99%

Section: Cache Replacement Policiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive Cache Replacement:A Novel Approach

Elfayoumy¹,

Warden²

2014

ijacsa

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Data Dissemination in Opportunistic Networks

Boldrini

Passarella

2013

Mobile Ad Hoc Networking

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An analytical model for the performance evaluation of stack‐based Web cache replacement algorithms

Messaoud

Youssef

2009

Int J Communication

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SUMMARYWeb caching has been the solution of choice to web latency problems. The efficiency of a Web cache is strongly affected by the replacement algorithm used to decide which objects to evict once the cache is saturated. Numerous web cache replacement algorithms have appeared in the literature. Despite their diversity, a large number of them belong to a class known as stack-based algorithms. These algorithms are evaluated mainly via trace-driven simulation. The very few analytical models reported in the literature were targeted at one particular replacement algorithm, namely least recently used (LRU) or least frequently used (LFU). Further they provide a formula for the evaluation of the Hit Ratio only. The main contribution of this paper is an analytical model for the performance evaluation of any stack-based web cache replacement algorithm. The model provides formulae for the prediction of the object Hit Ratio, the byte Hit Ratio, and the delay saving ratio. The model is validated against extensive discrete event trace-driven simulations of the three popular stack-based algorithms, LRU, LFU, and SIZE, using NLANR and DEC traces. Results show that the analytical model achieves very good accuracy. The mean error deviation between analytical and simulation results is at most 6% for LRU, 6% for the LFU, and 10% for the SIZE stack-based algorithms.

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An overview of web caching replacement algorithms

Cited by 135 publications

References 24 publications

Adaptive Cache Replacement:A Novel Approach

Adaptive Cache Replacement:A Novel Approach

Data Dissemination in Opportunistic Networks

An analytical model for the performance evaluation of stack‐based Web cache replacement algorithms

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