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Improving Quality-of-Service in Cloud/Fog Computing through Efficient Resource Allocation

ISAT Laboratory, Department of Computer Science, University of the Western Cape, Bellville 7535, South Africa
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This paper is an extension version of the conference paper: Akintoye, S.B.; Bagula, A. Optimization of virtual resources allocation in cloud computing environment. In Proceedings of the IEEE AFRICON, Cape Town, South Africa, 18–20 September 2017.
Sensors 2019, 19(6), 1267; https://doi.org/10.3390/s19061267
Received: 11 January 2019 / Revised: 30 January 2019 / Accepted: 9 February 2019 / Published: 13 March 2019
(This article belongs to the Special Issue Recent Advances in Fog/Edge Computing in Internet of Things)
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Abstract

Recently, a massive migration of enterprise applications to the cloud has been recorded in the IT world. One of the challenges of cloud computing is Quality-of-Service management, which includes the adoption of appropriate methods for allocating cloud-user applications to virtual resources, and virtual resources to the physical resources. The effective allocation of resources in cloud data centers is also one of the vital optimization problems in cloud computing, particularly when the cloud service infrastructures are built by lightweight computing devices. In this paper, we formulate and present the task allocation and virtual machine placement problems in a single cloud/fog computing environment, and propose a task allocation algorithmic solution and a Genetic Algorithm Based Virtual Machine Placement as solutions for the task allocation and virtual machine placement problem models. Finally, the experiments are carried out and the results show that the proposed solutions improve Quality-of-Service in the cloud/fog computing environment in terms of the allocation cost. View Full-Text
Keywords: CloudSim; virtual machine; greedy heuristics; cloud computing; fog computing; genetic algorithm; data center; CloudSim and hungarian algorithm CloudSim; virtual machine; greedy heuristics; cloud computing; fog computing; genetic algorithm; data center; CloudSim and hungarian algorithm
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Akintoye, S.B.; Bagula, A. Improving Quality-of-Service in Cloud/Fog Computing through Efficient Resource Allocation. Sensors 2019, 19, 1267.

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