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A Bio-Inspired Cache Management Policy for Cloud Computing Environments using the Artificial Bee Colony Algorithm

Idachaba, Unekwu Solomon (2015) A Bio-Inspired Cache Management Policy for Cloud Computing Environments using the Artificial Bee Colony Algorithm. Doctor of Philosophy (PhD) thesis, University of Kent,. (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

Caching has become an important technology in the development of cloud computing-based high-performance web services. Caches reduce the request-response latency experienced by users and reduce workload on backend databases. Caches need a high cache-hit rate to be fit for purpose, and this is dependent on the cache management policy used. Existing cache management policies do not prevent cache pollution and cache monopoly. This lack of prevention impacts negatively on cache hit rates. This work presents a Bio-inspired Community-based Caching (BCC) approach to address these two problems, by drawing intelligence from users' access behaviour using the Quantity and Quality Aware Artificial Bee Colony (Q2-ABC) clustering algorithm to achieve high cache-hit rates. Q2-ABC is a redesigned Artificial Bee Colony (ABC) algorithm which is also presented in this work. It optimizes the quality of clusters produced by addressing the repetition in metric space searches, probability-based effort distribution, and limit of abandonment problems inherent in ABC. To evaluate the performance of BCC, two sets of experiments were performed. In the first set of experiments, the quality of clusters identified by Q2-ABC was between 15% and 63% better than ABC. The performance of Q2-ABC comes with a cost: additional storage (a maximum of 300 bytes in this experiment) to store indexes of searched metric space. In the second set of experiments, the cache-hit rate achieved by BCC was between 0.7% and 55% better than the others across most of the test data used. The cost associated with BCC performance includes additional memory requirement-a total of 1.7Mb in this experiment-for storing generated intelligence and processor cycle overhead for generating intelligence. The implication of these results are that better quality clusters are produced by avoiding repeated searches within a metric space, and that high cache-hit rate can be achieved by managing caches intelligently, an alternative to expanding them as is conventional for Cloud Computing based services.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Wang, Frank
Uncontrolled keywords: Cache, Cloud Computing, Clustering and Artificial Bee Colony
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Faculties > Sciences > School of Computing
Depositing User: Users 1 not found.
Date Deposited: 11 Oct 2016 17:00 UTC
Last Modified: 29 May 2019 17:59 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/57856 (The current URI for this page, for reference purposes)
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