Energy-Efficient Task Scheduling in Fog Computing Based on Particle Swarm Optimization

• Published in: SN computer science Vol. 4; no. 4; p. 391
• Main Authors: Vispute, Shilpa Dinesh, Vashisht, Priyanka
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.07.2023; Springer Nature B.V
• Subjects: Algorithms; Cloud computing; Computer centers; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Edge computing; Enabling Innovative Computational Intelligence Technologies for IOT; Energy consumption; Information Systems and Communication Service; Internet of Things; Literature reviews; Markov analysis; Network latency; Nodes; Optimization; Original Research; Particle swarm optimization; Pattern Recognition and Graphics; Performance evaluation; Reputations; Scheduling; Simulation; Software Engineering/Programming and Operating Systems; Task scheduling; Teaching methods; Vision; Fog computing; Task scheduling; Cloud computing; Internet of Things (IoT); Particle swarm optimization
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-022-01639-3

Recently, continuous growth in use of Internet of Things (IoT) produces huge amount of data during processing, which increases load on Cloud Computing network. Cloud computing does not support low latency for real-time applications. To overcome such drawbacks like low latency, lower response time, IoT-based application moves towards Fog Computing. Fog Computing along with Cloud Computing provides best solution for such IoT applications. Fog Computing enables user to process data near to end user. It supports low latency and low response time than Cloud environment. Fog Computing allows to schedule application tasks on various Fog nodes which are available in the network. Fog nodes have limited resources than Cloud Computing, and hence, scheduling tasks among Fog nodes becomes essential. In this paper, a scheduling algorithm, namely, Energy efficient task scheduling in Fog Computing based on Particle Swarm Optimization (EETSPSO), has been proposed. The proposed EETSPSO algorithm simulated in MATLAB. Proposed EETSPSO evaluated using evaluation parameters makespan, energy, and execution time. The simulated result of EETSPSO shows better result by minimizing the makespan time, execution time, and reducing the energy consumption as compared to Bee Life Algorithm (BLA) and Modified Particle Swarm Optimization (MPSO). The simulated experiment shows better performance of EETSPSO for makespan 6.39% and 4.71% compared to MPSO and BLA respectively. Simulation result of EETSPSO also shows 9.12% and 11.47% energy reduction when compared with MPSO and BLA. EETSPSO performed better than MPSO and BLA by 9.83% and 6.32%, respectively, for execution time comparison.