Intelligent Scheduling in Fog Environment based on Improved Hybrid Heuristics

• Published in: 2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA) pp. 1 - 7
• Main Authors: Leena, Sri. R, Divya, V., Lilian, J Felicia
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2020
• Subjects: Crow search; Decision making; Delays; Fog computing; Job Scheduling; Mathematical model; Particle Swarm Optimization; Processor scheduling; Resource management; Scheduling; Task analysis
• ISSN: 2161-5330
• DOI: 10.1109/AICCSA50499.2020.9316493

The need for real-time decisions and the use of smart devices have increased to a great extent thereby increasing the use of the Fog computing paradigm. The major concern in task scheduling among the edge/fog nodes lie in the efficient choice of nodes with the least possible latency. This involves the design of an efficient scheduling algorithm with the least possible latency and increased energy efficiency. Our proposed work deals with an efficient algorithm of task scheduling amidst the fog nodes with an improvement in the energy utility. The paper provides an improved hybrid heuristics approach that helps in the proper utilization of the fog nodes that are limited in terms of computational resources and energy. The paper also compares the proposed work with the existing heuristic algorithms and is proved to be superior and a mathematical analysis of the objectives has also been presented. The proposed algorithm shows a reduction in service time by 18% as compared to neural networks and improvement in energy efficiency of about 29% as compared to the existing heuristics with a significant 40% reduction in the average delay of the system as compared to the use of Deep neural networks.