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Aiming at QoS: A Modified DE Algorithm for Task Allocation in Cloud Computing - 2020

Research Area:  Cloud Computing

Abstract:

The Cloud computing system is characterized by large scale servers being utilized by an even larger number of users. It is a system where there is the need to frequently and efficiently schedule and manage different application tasks, with varied service requirements. One of the challenges of Cloud computing is managing the quality of service (QoS) rendered to users, specifically scheduling tasks between users and Cloud resources in a timely manner. Cloud users usually have widely diverse QoS requirements and meeting these simultaneously is also a challenge. In this paper, in order to improve on Cloud resource allocation and specifically to tailor it towards meeting varied QoS requirements of users, we proposed a new algorithm which combines Differential Evolution with the Shapley Value economic mode. This combination allows us measure the contribution of each virtual machine (VM), so as to improve the probability of obtaining a better tasks-to-resource allocation thereby improving user satisfaction. From results of conducted experiments, when compared with the traditional DE (Differential Evolution) algorithm and the conventional task-VM binding policy in CloudSim, both for allocations where special QoS requirements are required and in instances of multiple QoS requirements; the modified Shapley value based DE algorithm (SVBDA) shows significant improvement.

Author(s) Name:  Kun Ma; Antoine Bagula; Olasupo Ajayi; Clement Nyirenda

Journal name:  

Conferrence name:  ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

Publisher name:  IEEE

DOI:  10.1109/ICC40277.2020.9148980

Volume Information: