An efficient population-based multi-objective task scheduling approach in fog computing systems
Abstract 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 Io...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-10-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-021-00264-4 |
| _version_ | 1831581343141593088 |
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| author | Zahra Movahedi Bruno Defude Amir mohammad Hosseininia |
| author_facet | Zahra Movahedi Bruno Defude Amir mohammad Hosseininia |
| author_sort | Zahra Movahedi |
| collection | DOAJ |
| description | Abstract 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). |
| first_indexed | 2024-12-17T20:02:16Z |
| format | Article |
| id | doaj.art-81d0318021d843f2a6cf7dee655bfa14 |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-12-17T20:02:16Z |
| publishDate | 2021-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-81d0318021d843f2a6cf7dee655bfa142022-12-21T21:34:26ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2021-10-0110113110.1186/s13677-021-00264-4An efficient population-based multi-objective task scheduling approach in fog computing systemsZahra Movahedi0Bruno Defude1Amir mohammad Hosseininia2Department of Engineering, College of Farabi, University of TehranSAMOVAR, Télécom SudParis, Institut Polytechnique de ParisDepartment of Engineering, College of Farabi, University of TehranAbstract 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).https://doi.org/10.1186/s13677-021-00264-4Fog computingTask schedulingInternet of thingsMeta-heuristicWhale optimization algorithmOpposition-based learning |
| spellingShingle | Zahra Movahedi Bruno Defude Amir mohammad Hosseininia An efficient population-based multi-objective task scheduling approach in fog computing systems Journal of Cloud Computing: Advances, Systems and Applications Fog computing Task scheduling Internet of things Meta-heuristic Whale optimization algorithm Opposition-based learning |
| title | An efficient population-based multi-objective task scheduling approach in fog computing systems |
| title_full | An efficient population-based multi-objective task scheduling approach in fog computing systems |
| title_fullStr | An efficient population-based multi-objective task scheduling approach in fog computing systems |
| title_full_unstemmed | An efficient population-based multi-objective task scheduling approach in fog computing systems |
| title_short | An efficient population-based multi-objective task scheduling approach in fog computing systems |
| title_sort | efficient population based multi objective task scheduling approach in fog computing systems |
| topic | Fog computing Task scheduling Internet of things Meta-heuristic Whale optimization algorithm Opposition-based learning |
| url | https://doi.org/10.1186/s13677-021-00264-4 |
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