A Multi-Objective Service Placement and Load Distribution in Edge Computing

Edge Computing emerges as a solution that overcomes some obstacles of traditional central data centers to support the performance-critical Internet of Things applications. However, a challenge therein is the resource allocation for heterogeneous applications at a network edge composed of distributed...

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Bibliographic Details
Published in2019 IEEE Global Communications Conference (GLOBECOM) pp. 1 - 7
Main Authors Maia, Adyson M., Ghamri-Doudane, Yacine, Vieira, Dario, de Castro, Miguel F.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
Delays
Edge computing
Genetic algorithms
Internet of Things
Optimization
Resource management
Time factors
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Summary:Edge Computing emerges as a solution that overcomes some obstacles of traditional central data centers to support the performance-critical Internet of Things applications. However, a challenge therein is the resource allocation for heterogeneous applications at a network edge composed of distributed and resource-restricted nodes. In this paper, we investigate how to place replicas of applications, and distribute requests among these replicas to optimize multiple objectives. We propose a genetic algorithm based on Pareto fronts as a problem-solving meta-heuristic to prioritize latency-sensitive applications and optimize conflicted objectives. Evaluation results show that our proposal outperforms other benchmark algorithms in terms of response deadline violation, as well as terms of other important and sometimes conflicting objectives, such as cost and availability.
ISSN:2576-6813
DOI:10.1109/GLOBECOM38437.2019.9014303