An evolutionary fuzzy scheduler for multi-objective resource allocation in fog computing

With rapid development of the Internet of Things (IoT), a vast amount of raw data produced by IoT devices needs to be processed promptly. Compared to cloud computing, fog computing nodes are closer to data resource for decreasing the end-to-end transmission latency. Considering the limited resource...

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Bibliographic Details
Published in:Future generation computer systems Vol. 117; pp. 498 - 509
Main Authors: Wu, Chu-ge, Li, Wei, Wang, Ling, Zomaya, Albert Y.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.04.2021
Subjects:
Agreement index
Edge computing
Estimation of distribution algorithm
Evolutionary computation
Fuzzy scheduling
Internet of Things
Robustness
Evolutionary computation
Agreement index
Robustness
Internet of Things
Fuzzy scheduling
Estimation of distribution algorithm
Edge computing
ISSN:0167-739X, 1872-7115
Online Access:Get full text
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Summary:With rapid development of the Internet of Things (IoT), a vast amount of raw data produced by IoT devices needs to be processed promptly. Compared to cloud computing, fog computing nodes are closer to data resource for decreasing the end-to-end transmission latency. Considering the limited resource of IoT devices, offloading computationally-intensive tasks to the servers with high computing capability is essential in the IoT–fog–cloud system to complete those tasks on time. In this work, we propose a fuzzy logical offloading strategy for IoT applications characterized by uncertain parameters to optimize both agreement index and robustness. A multi-objective Estimation of Distribution Algorithm (EDA) is designed to learn and optimize the fuzzy offloading strategy from a diversity of the applications. The algorithm partitions applications into independent clusters, so that each cluster can be allocated to the corresponding tier for further processing. Thus, system resources are saved by making scheduling decisions in a reduced search space. Simulation studies on benchmark problems and real-world cases are carried out to verify the efficiency of our proposed algorithm. Pareto sets produced by our algorithm outperformed classic heuristic solutions for 88.3% benchmark cases and dominated Pareto sets of two state-of-art multi-objective algorithms for 92.7% and 94.4% cases correspondingly. •Use fuzzy number to model processing time and inter-task data size.•Agreement index of customers and robustness of schedule as objectives.•Fuzzy logic method for pre-partition and EDA for rule allocation.•Verify efficiency by extensive numerical tests.
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2020.12.019