Adaptive Multi-Objective Task Scheduling in Cloud Computing Using Deep Reinforcement Learning and Hybrid Metaheuristic Optimization

Saquib Ali; Raza Abbas Haidri; Nafees Akhter Farooqui

doi:10.65890/race.v2i1.194

Authors

Saquib Ali Department of Computer Science and Information Technology Khwaja Moinuddin Chishti Language University Lucknow, India Author
Raza Abbas Haidri Department of Computer Science and Information Technology Khwaja Moinuddin Chishti Language University Lucknow, India Author
Nafees Akhter Farooqui Department of Computer Application Integral University Lucknow, India Author

DOI:

https://doi.org/10.65890/race.v2i1.194

Keywords:

CloudSim, Deep Reinforcement Learning, Deep Q-Network, Genetic Algorithm, Kubernetes, OpenStack, Particle Swarm Optimization, Service Level Agreement, Clod Computing.

Abstract

The rapid development of cloud computing has posed a significant challenge for task scheduling, given the highly dynamic, diverse, and large-scale workloads. Most heuristic and metaheuristic scheduling algorithms fail to adapt to rapidly changing run-time conditions, and lead to sub-optimal resource utilization, longer makespan, higher energy usage and violation of Service Level Agreements (SLAs). This study proposes an adaptive multi-objective task scheduling system based on Deep Reinforcement Learning (DRL) combined with hybrid metaheuristic optimisation methods to overcome these restrictions. To enable independent decision-making in an unpredictable, fluctuating workload, the proposed approach dynamically learns optimal scheduling rules from real-time conditions in the cloud system using a Deep Q-Network (DQN). In addition, the integration of Genetic Algorithm and Particle Swarm Optimization (PSO) is performed to encourage convergence towards the optimal scheduling solution without the occurrence of local minima. The hybrid approach has been shown to find a good balance between exploration and exploitation in complex cloud environments. We further evaluate the practicality of the proposed framework through large-scale simulations of real-world cloud systems running Kubernetes and OpenStack with CloudSim. The performance is assessed based on key metrics such as makespan, resource utilisation, energy efficiency, throughput and SLA violation rate. Through experiments, it has been shown that the proposed method consistently outperforms the state-of-the-art scheduling strategies in execution time, energy efficiency, resource utilization, and fewer SLA violations. The study presents a scalable, intelligent, and self-adaptive scheduling paradigm suitable for effective, persistent resource management in extremely dynamic distributed computing environments for next-generation cloud infrastructures.

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