Adaptive Cache Replacement:A Novel Approach

Abstract

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.

Accelerating the Internet in the presence of Big Data: Reducing user delays by leveraging historical user request patterns for web caching

“…Proxy caching is beneficial for both the specific network where it is used as well as for all Internet users in general. Given the test results of Kumar & Norris (2008), as well as the merits of other caching approaches surveyed in Cui et al (2018), Floratou et al (2015), Irani & Lam (2015), Elfayoumy & Warden (2014), Zhang et al (2013), and Zeng et al (2004), we offer that effective proxy caching mechanisms exploiting historical user request patterns can significantly reduce delays for web users if they were to be adopted at large scale networks in this Big Data era.…”

“…We observe in early 2019 that Google, Facebook, Amazon, Reddit, Wikipedia, Yahoo, Twitter, and Netflix, among others, continue to be among the most popular websites in a 30 day period from January to February. Consequently, the principle of exploiting patterns remains the same as proposed in Kumar (2010), Kumar & Norris (2008), and other studies (Floratou et al, 2015;Irani & Lam, 2015;Elfayoumy & Warden, 2014;Ali et al, 2012). We may predict the likely most requested sites in advance by mining historical request patterns.…”

“…Comparing popular caching mechanisms, such as FIFO, the proposed caching mechanism of Kumar & Norris (2008) is shown to perform more favorably. Ali et al (2012), Elfayoumy & Warden (2014), and Irani & Lam (2015) review and study cache replacement strategies using historical replacement strategies. Employing the use of historical user request patterns in a cache allow content providers and network administrators to drastically reduce web user delays at proxy servers (Kumar, 2016;Kumar, 2010).…”

“…It has been shown in prior studies that users at a proxy level tend to access the same web content on a routine basis while creating a distinguishable patterns in demand for the web content, for example accessing the content every half hour (Kumar, 2016;Irani & Lam, 2015;Elfayoumy & Warden, 2014;Rizzo & Vicisano, 2000;Cao & Irani, 1997). This is illustrated by a sport fan who goes to espn.com on a regular basis throughout the day to check sports news.…”

“…The use of historical patterns in proxy caching have been extensively reviewed by Floratou et al (2015), Irani & Lam (2015), Ali etl al. (2012), Elfayoumy & Warden (2014), and Zeng et al (2004), which survey techniques that utilize some aspects of historical user request patterns. One interesting approach that uses historical request patters is Kumar & Norris (2008), they offer a model that aggregates patterns based on user object requests to use as part of the caching mechanism in a proxy server.…”