An efficient population-based multi-objective task scheduling approach in fog computing systems

With the rapid development of Internet of Things (IoT) technologies, fog computing has emerged as an extension to the cloud computing that relies on fog nodes with distributed resources at the edge of network. Fog nodes offer computing and storage resources opportunities to resource-less IoT devices...

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Bibliographic Details
Published in:Journal of cloud computing : advances, systems and applications Vol. 10; no. 1; pp. 53:1 - 53:31
Main Authors: Movahedi, Zahra, Defude, Bruno, Hosseininia, Amir mohammad
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 07.10.2021
Springer Nature B.V
Hindawi Limited
SpringerOpen
Subjects:
Cloud computing
Computation offloading
Computer Communication Networks
Computer Science
Computer System Implementation
Computer Systems Organization and Communication Networks
Edge-cloud computing cooperation for task offloading in internet-of-things
Energy consumption
Fog computing
Genetic algorithms
Heuristic methods
Information Systems Applications (incl.Internet)
Integer programming
Internet of Things
Linear programming
Meta-heuristic
Multiple objective analysis
Network latency
Nodes
Opposition-based learning
Optimization
Particle swarm optimization
Quality of service architectures
Scheduling
Software Engineering/Programming and Operating Systems
Special Purpose and Application-Based Systems
Task scheduling
Whale optimization algorithm
Fog computing
Internet of things
Chaos theory
Task scheduling
Meta-heuristic
Whale optimization algorithm
Opposition-based learning
ISSN:2192-113X, 2192-113X
Online Access:Get full text
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Summary:With the rapid development of Internet of Things (IoT) technologies, fog computing has emerged as an extension to the cloud computing that relies on fog nodes with distributed resources at the edge of network. Fog nodes offer computing and storage resources opportunities to resource-less IoT devices which are not capable to support IoT applications with computation-intensive requirements. Furthermore, the closeness of fog nodes to IoT devices satisfies the low-latency requirements of IoT applications. However, due to the high IoT task offloading requests and fog resource limitations, providing an optimal task scheduling solution that considers a number of quality metrics is essential. In this paper, we address the task scheduling problem with the aim of optimizing the time and energy consumption as two QoS parameters in the fog context. First, we present a fog-based architecture for handling the task scheduling requests to provide the optimal solutions. Second, we formulate the task scheduling problem as an Integer Linear Programming (ILP) optimization model considering both time and fog energy consumption. Finally, we propose an advanced approach called Opposition-based Chaotic Whale Optimization Algorithm (OppoCWOA) to enhance the performance of the original WOA for solving the modelled task scheduling problem in a timely manner. The efficiency of the proposed OppoCWOA is shown by providing extensive simulations and comparisons with the original WOA and some existing meta-heuristic algorithms such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).
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ISSN:2192-113X
2192-113X
DOI:10.1186/s13677-021-00264-4