What question did this study set out to answer?

This research aims to improve cloud resource management through an efficient task scheduling strategy using a novel approach combining SBA and DRL.

June 27, 2026Open Access

Multi-objective task scheduling using SBA-based deep reinforcement learning in cloud computing

EKEmir KalikUniversity of Wasit HIHabib IzadkhahUniversity of Tabriz JKJaber KarimpourUniversity of Tabriz

Puntos clave

This research aims to improve cloud resource management through an efficient task scheduling strategy using a novel approach combining SBA and DRL.
Developed a scheduling method called SBA-DRL that learns workload patterns.
Evaluated performance using synthetic dataset and real-world Google Cloud Jobs dataset, ranging from 200 to 1,000 tasks.
Compared SBA-DRL to existing scheduling methods including heuristics and machine learning-based solutions.
On the synthetic dataset, SBA-DRL reduced costs by 20.21% and energy consumption by 25.31%.
Achieved up to 28.94% lower costs and a 14.04% increase in resource utilization on the GoCJ dataset.
Demonstrated superior performance in resource allocation and management of high availability compared to traditional methods.

Resumen

Cloud computing is a key enabler of modern computing services, offering scalability and flexibility. However, efficient management of cloud resources remains challenging due to limited capacity and the increasing number of tasks requiring timely execution. An effective task scheduling strategy is therefore essential to improve resource allocation and utilization, reduce operational costs and energy consumption, and support high availability-especially for long-term jobs. In this paper, we propose a new scheduling approach that combines a Social-Based Algorithm (SBA) with Deep Reinforcement Learning (DRL), referred to as SBA-DRL. This method allocates tasks to resources by learning from workload patterns and adapting to workload characteristics in a batch scheduling context. We evaluate SBA-DRL using both a synthetic dataset and the real-world Google Cloud Jobs (GoCJ) under workloads ranging from 200 to 1,000 tasks. On the synthetic dataset, our method reduces cost by 20.21% and energy consumption by 25.31%, while improving resource utilization by 9.36%. On the GoCJ dataset, it achieves up to 28.94% lower cost, 8.16% less energy use, and a 14.04% increase in resource utilization. In both cases, SBA-DRL also demonstrates better performance in resource allocation and high-availability management compared to existing heuristics, meta-heuristics, hybrid, and machine learning-based schedulers. These results indicate that the proposed SBA-DRL approach effectively addresses key challenges in cloud task scheduling, offering a practical solution to enhance the efficiency and sustainability of cloud systems.

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