A Greedy Constraints-based Fog Computing Model to Optimize Task Scheduling
A Greedy Constraints-based Fog Computing Model to Optimize Task Scheduling |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-5 |
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| Year of Publication : 2025 | ||
| Author : Monika, Harkesh Sehrawat, Vikas Siwach | ||
| DOI : 10.14445/22315381/IJETT-V73I5P129 | ||
How to Cite?
Monika, Harkesh Sehrawat, Vikas Siwach, "A Greedy Constraints-based Fog Computing Model to Optimize Task Scheduling," International Journal of Engineering Trends and Technology, vol. 73, no. 5, pp.359-368, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I5P129
Abstract
Fog computing ensures effective request processing and service distribution in real-time IoT applications. Instant request processing, reliable execution, and effective QoS are the key requirements of such applications. However, the increasing requests and broad coverage of this network can increase the network load. An effective resource allocation and scheduling method is required to utilize the available resources and to reduce execution failure. Higher delay and makespan are the key challenges of scheduling algorithms and fog computing. In this paper, a multiple-constraint adaptive greedy task scheduler is designed to optimize the functioning of task scheduling. The functioning of the proposed scheduling model is divided into two stages. In the first stage, a multiple constraint-based resource allocation is done. In this stage, task criticality and resource priority-based mapping methods are defined to optimize the resource scheduling. In the final stage, the deadline and delay adaptive greedy method is defined to schedule the requests. The comparative evaluation is done against the FCFS, SJF, GTS, and DPTS methods. The algorithm reduced the average delay and makespan in comparison with state-of-the-art methods.
Keywords
Fog computing, Task scheduling, Greedy, Resource allocation, IoT.
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