A Community-based Cloud Computing Caching Service

Caching has become an important technology in the development of cloud computing-based high-performance web services. Caches reduce the request to response latency experienced by users, and reduce workload on backend databases. They need a high cache-hit rate to be fit for purpose, and this rate is dependent on the cache management policy used. Existing cache management policies are not designed to prevent cache pollution or cache monopoly problems, which impacts negatively on the cache-hit rate. This paper proposes a community-based caching approach (CC) to address these two problems. CC was evaluated for performance against thirteen commercially available cache management policies, and results demonstrate that the cache-hit rate achieved by CC was between 0.7% and 55% better than the alternate cache management policies

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

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

Quality Quantity and Repellent Scent Aware Artificial Bee Colony Algorithm for Clustering

A modification of Artificial Bee Colony algorithm for clustering to improve on its quality of cluster identification, energy efficiency and execution speed