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Enhancing Job Scheduling Efficiency through Multi-Agent Systems in Distributed Computing Environments

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Bibliographic Details
Title: Enhancing Job Scheduling Efficiency through Multi-Agent Systems in Distributed Computing Environments
Authors: Olayinka Akinbolajo Olayinka Akinbolajo
Source: International Journal of Advances in Engineering and Management. 7:706-711
Publisher Information: Quest Journals, 2025.
Publication Year: 2025
Description: The rapid growth of distributed computing environments has necessitated the development of efficient job scheduling mechanisms to optimize resource utilization and minimize latency. MultiAgent Systems (MAS) have emerged as a promising approach to address the complexities of job scheduling in such environments. This paper explores the integration of MAS into distributed computing systems to enhance job scheduling efficiency. We propose a novel framework that leverages the autonomous, collaborative, and adaptive capabilities of agents to improve scheduling decisions. Through extensive simulations and comparative analysis, we demonstrate that our approach significantly reduces job completion times and enhances resource allocation. The findings of this study contribute to the growing body of knowledge on intelligent scheduling systems and provide practical insights for implementing MAS in real-world distributed computing environments.
Document Type: Article
ISSN: 2395-5252
DOI: 10.35629/5252-0703706711
Accession Number: edsair.doi...........2edd993e1eba51664eee52b86b30eef5
Database: OpenAIRE
Description
Abstract:The rapid growth of distributed computing environments has necessitated the development of efficient job scheduling mechanisms to optimize resource utilization and minimize latency. MultiAgent Systems (MAS) have emerged as a promising approach to address the complexities of job scheduling in such environments. This paper explores the integration of MAS into distributed computing systems to enhance job scheduling efficiency. We propose a novel framework that leverages the autonomous, collaborative, and adaptive capabilities of agents to improve scheduling decisions. Through extensive simulations and comparative analysis, we demonstrate that our approach significantly reduces job completion times and enhances resource allocation. The findings of this study contribute to the growing body of knowledge on intelligent scheduling systems and provide practical insights for implementing MAS in real-world distributed computing environments.
ISSN:23955252
DOI:10.35629/5252-0703706711