(EERO) Energy-Efficient Fog Resource Optimization Model for Scientific Workflow Applications
(EERO) Energy-Efficient Fog Resource Optimization Model for Scientific Workflow Applications |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-5 |
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| Year of Publication : 2024 | ||
| Author : Satyakam Rahul, Vinay Bhardwaj |
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| DOI : 10.14445/22315381/IJETT-V72I5P116 | ||
How to Cite?
Satyakam Rahul, Vinay Bhardwaj, "(EERO) Energy-Efficient Fog Resource Optimization Model for Scientific Workflow Applications," International Journal of Engineering Trends and Technology, vol. 72, no. 5, pp. 149-164, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I5P116
Abstract
Network congestion and increased latency may result from the speedy development of intelligent services and Internet of Things devices contacting cloud data centres. Fog computing meets the latency and privacy needs of operations running at the network edge by focusing on widely linked heterogeneous devices. The intricate and stringent Quality of Service limitations make allocating resources in this paradigm challenging. We look into workflow scheduling in fog-cloud systems to give an energy-efficient task plan within tolerable application completion times. The Energy Efficient optimization mode is presented. This paper delves into the outcomes of algorithms created by the researchers to address issues with energy management. The objective is to provide energy-efficient algorithms for a particular problem that minimize service compromise while reducing energy usage. The algorithms must attain a provably good performance, a crucial requirement. The goal is to find an efficient Pareto front by employing a Bayesian method with a maximum likelihood procedure for processing the fog node tasks while improving task scheduling by integrating heuristic methodologies such as Predict Earliest Finish Time (PEFT) and the Multi-objective genetic algorithm.
Keywords
Fog computing, Resource utilization, Energy consumption, Workflows, Energy-Efficient, Scientific.
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