Node cooperation for workload offloading in a fog computing network via multi-objective optimization

The number of devices connected to the internet is increasing at a tremendous rate. Connecting intelligent devices has created a new paradigm called the Internet of Things (IoT). Due to the significant volume of data generated in the IoT, cloud infrastructure alone cannot process this data. Thus, fo...

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Bibliographic Details
Published in:Journal of network and computer applications Vol. 205; p. 103428
Main Authors: Vakilian, Shakoor, Fanian, Ali, Falsafain, Hossein, Gulliver, T. Aaron
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.09.2022
Subjects:
Distributed ADMM Algorithm
Fog computing
Fog node cooperation
Multi-objective optimization
Workload offloading
Fog computing
Multi-objective optimization
Fog node cooperation
Workload offloading
Distributed ADMM Algorithm
ISSN:1084-8045, 1095-8592
Online Access:Get full text
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Summary:The number of devices connected to the internet is increasing at a tremendous rate. Connecting intelligent devices has created a new paradigm called the Internet of Things (IoT). Due to the significant volume of data generated in the IoT, cloud infrastructure alone cannot process this data. Thus, fog computing has been proposed to supplement the cloud. One of the critical issues in fog computing is deciding how much of the workload should be offloaded to the cloud. In this paper, a multi-objective optimization problem that considers cooperation between fog nodes is presented. The goal is to minimize the response time, energy cost, and rental cost of cloud resources. This multi-objective optimization problem is transformed into a single-objective optimization problem which is solved using the distributed Alternating Direction Method of Multipliers (ADMM) algorithm. Results are presented which show that this distributed algorithm converges to an optimal solution which provides a tradeoff between the total response time, total energy cost in the fog nodes, and total rental cost of cloud resources. It is shown to provide better performance compared to the well-known adversary algorithm.
ISSN:1084-8045
1095-8592
DOI:10.1016/j.jnca.2022.103428