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Edge user allocation by FOA in edge computing environment
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DOI：10.1016/j.jocs.2021.101390.png)
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Abstract
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In recent years, edge computing (EC) has been widely studied as a new computing paradigm which extends cloud computing. It paves the way to further reduce the network latency between IoT/mobile devices (referred to as edge users hereafter) and service providers by pushing services and corresponding data from clouds to nearby edge servers located nearby edge users. The edge user allocation (EUA) problem is a new issue in EC environment. It aims at optimizing strategies to allocate edge users to those edge servers while fulfilling specific constraints, e.g., budget constraint, coverage constraint, etc. As the EUA problem is NP-hard, effectively and efficiently solving it is still intractable. In this paper, we take allocating maximum edge users and employing minimum edge servers as objectives, then take both the proximity constraint and capacity constraint into account, and propose EUA-FOA, an Fruit fly Optimization Algorithm (FOA)-based approach, to solve the EUA problem. To extensively evaluate EUA-FOA's performance, we employ a widely used real-world dataset to conduct two sets of experiments, including small-scale EUA scenarios and large-scale EUA scenarios. We compare EUA-FOA against four representative approaches and the experimental results demonstrate that EUA-FOA is highly effective as it outperforms the state-of-the-art approaches significantly.
Keywords:
Edge user allocation
Fruit fly optimization Algorithm (FOA)
Edge computing
Swarm intelligence
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