Fog computing job scheduling optimization based on bees swarm. Issue 4 (21st April 2018)

• Record Type: Journal Article
• Authors: Bitam, Salim; Zeadally, Sherali; Mellouk, Abdelhamid
• Is Part Of: Enterprise information systems. Volume 12:Issue 4(2018)
• Page Start: 373
• Page End: 397
• Publication Date: 2018-04-21
• Subjects: Fog computing -- edge computing -- job scheduling -- bees life algorithm -- CPU execution time -- allocated memory; Information storage and retrieval systems -- Periodicals; Management information systems -- Periodicals; Electronic commerce -- Periodicals; 658.4038011
• DOI: 10.1080/17517575.2017.1304579
• Languages: English
• ISSNs: 1751-7575

Abstract

ABSTRACT: Fog computing is a new computing architecture, composed of a set of near-user edge devices called fog nodes, which collaborate together in order to perform computational services such as running applications, storing an important amount of data, and transmitting messages. Fog computing extends cloud computing by deploying digital resources at the premise of mobile users. In this new paradigm, management and operating functions, such as job scheduling aim at providing high-performance, cost-effective services requested by mobile users and executed by fog nodes. We propose a new bio-inspired optimization approach called Bees Life Algorithm (BLA) aimed at addressing the job scheduling problem in the fog computing environment. Our proposed approach is based on the optimized distribution of a set of tasks among all the fog computing nodes. The objective is to find an optimal tradeoff between CPU execution time and allocated memory required by fog computing services established by mobile users. Our empirical performance evaluation results demonstrate that the proposal outperforms the traditional particle swarm optimization and genetic algorithm in terms of CPU execution time and allocated memory.