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Telecommunication Systems
Published in: Telecommunication Systems 3/2023

27-08-2023

A reinforcement learning-based load balancing algorithm for fog computing

Authors: Niloofar Tahmasebi-Pouya, Mehdi Agha Sarram, Seyedakbar Mostafavi

Published in: Telecommunication Systems | Issue 3/2023

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Abstract

Fog computing is a developing paradigm for bringing cloud computing capabilities closer to end-users. Fog computing plays an important role in improving resource utilization and decreasing delay for internet of things (IoT) applications. At the same time, it faces many challenges, including challenges related to energy consumption, scheduling and resource overload. Load balancing helps to reduce delay, increase user satisfaction, and also increase system efficiency by efficiently and fairly allocation of tasks among computing resources. Fair load distribution among fog nodes is a difficult challenge due to the increasing number of IoT devices. In this research, we suggested a new approach for fair load distribution in fog environment. The Q-learning algorithm-based load balancing method is executed as the proposed approach in the fog layer. The objective of this method is to simultaneously improve the load balancing and delay. In this technique, the fog node uses reinforcement learning to choose whether to handle a task it receives via IoT devices directly, or whether to send it to a nearby fog node or the cloud. The simulation findings demonstrate that our approach results a suitable technique for fair load distribution among fog nodes, which improves the delay, run time, network utilization, and standard deviation of load on nodes than other compared techniques. In this way, in the case where the number of fog nodes is considered to be 4, the delay in the proposed method is reduced by around 8.44% in comparison to the load balancing and optimization strategy (LBOS) method, 26.65% in comparison to the secure authentication and load balancing (SALB) method, 29.15% in comparison to the proportional method, 7.75% in comparison to the fog cluster-based load-balancing (FCBLB) method, and 36.22% in comparison to the random method. In the case where the number of fog nodes is considered to be 10, the delay in the proposed method is reduced by around 13.80% in comparison to the LBOS method, 29.84% in comparison to the SALB method, 32.23% in comparison to the proportional method, 13.34% in comparison to the FCBLB method, and 39.1% in comparison to the Random method.
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Footnotes
1
Quality of Experience.
 
2
Quality of Service.
 
3
Electro Encephalo Gram.
 
4
Load Balancing and Optimization Strategy.
 
5
Secure Authentication and Load Balancing.
 
6
Fog Cluster-Based Load-Balancing.
 
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Metadata
Title
A reinforcement learning-based load balancing algorithm for fog computing
Authors
Niloofar Tahmasebi-Pouya
Mehdi Agha Sarram
Seyedakbar Mostafavi
Publication date
27-08-2023
Publisher
Springer US
Published in
Telecommunication Systems / Issue 3/2023
Print ISSN: 1018-4864
Electronic ISSN: 1572-9451
DOI
https://doi.org/10.1007/s11235-023-01049-7

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