Cooperative Web Caching Using Dynamic Interest-Tagged Filtered Bloom Filters

Abstract

Although cooperative Web caching has been widely researched, comparatively little has been done to reduce inter-proxy network overhead whilst allowing for a high percentage of requested documents to be retrieved from the cache. Alleviating these issues can substantially reduce web traffic, increase scalability and enhance a user's browsing experience. This paper introduces a novel cache sharing system employing data structures called Dynamic Interest-Tagged Filtered Bloom Filters (DITFBFs). DITFBFs are capable of representing the cache content of a proxy in a compact form, which is then shared with other proxies in the cooperative Web caching system. What distinguishes the proposed system from others is that DITFBFs only represent the portion of a proxy's cache content that will be of interest to another proxy. This then results in a reduction of inter-proxy overhead. Experimental simulations indicate that, when compared with existing protocols, the proposed system is capable of multiple improvements. Namely, lowering the number of remote cache search messages by at least 60%, decreasing user-perceived latency by at least 65% and appreciably reducing the overall inter-proxy network overhead. The proposed system accomplishes this whilst maintaining a cache hit ratio as high as the other protocols.

Bloom filter empowered smart storage/access in IoMT [edge‐fog‐cloud] hierarchy for health‐care data ingestion

“…Thus, for this purpose, the small size of Alexander et al uses BF and also deletes some data from BF to accommodate new data. 40 Stable BFs, 41 double buffering, and A 2 buffering 42 are only a few examples of well-liked staling-supporting BF variations. These BFs have double memory, which allows for more data storage time.…”

“…It has happened because streaming applications (like membership queries, detect duplicates, and approximate caching) require one‐pass processing of data and also time‐bound results. Thus, for this purpose, the small size of Alexander et al uses BF and also deletes some data from BF to accommodate new data 40 . Stable BFs, 41 double buffering, and $$$ {A}^2 $$$ buffering 42 are only a few examples of well‐liked staling‐supporting BF variations.…”

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

“…The BF for the bus system further reduces system-wide data broadcasts. As for web caching, BF is redesigned to represent cache content of each Internet proxy in a compact form [10]. Thereafter, the BFs are shared with other proxies in the web caching system.…”

Web service QoS prediction using improved software source code metrics

“…However, QoS information is influenced by network and geological factors. As Internet is dynamic and vulnerable, it is not possible to get the same QoS values for different users from diverse locations for a particular service [17,18].…”