
ESJFM: An Enhanced SJF Scheduling Model to Improve Resource Utilization In Heterogeneous Cloud Environments | IJCT Volume 12 – Issue 6 | IJCT-V12I6P63

International Journal of Computer Techniques
ISSN 2394-2231
Volume 12, Issue 6 | Published: November – December 2025
Table of Contents
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Ali Mothana, Hikmat Al-Quhfa, Jie Song
Abstract
Cloud computing has become essential in today’s IT industry by offering flexible, on-demand services like data storage and computing platforms. However, managing limited resources to meet growing user demand remains a challenge for cloud providers, particularly in maintaining Quality of Service (QoS). Task scheduling plays a crucial role in resource management, but traditional algorithms like Shortest Job First (SJF) can lead to resource imbalances and task starvation. This study introduces the Enhanced Shortest Job First Model (ESJFM), which organizes tasks by size and assigns them to Virtual Machines (VMs) with appropriate processing power—short tasks are directed to low-power VMs, while longer tasks are assigned to high-power VMs. Experiments conducted using CloudSim with GoCJ and random datasets show that ESJFM improves resource utilization by over 90%, reducing makespan by approximately 40% compared to SJF. The proposed model effectively eliminates starvation, balances the load, and enhances resource utilization in heterogeneous cloud environments.
Keywords
Cloud Computing; Task Scheduling; Shortest Job First (SJF); Resource Utilization; Load Balancing; CloudSim.
Conclusion
In conclusion, ESJFM represents a significant advancement in cloud task scheduling. By dynamically adapting task alloca- tion based on task length and VM processing power, ESJFM improves makespan, reduces response time, and maximizes resource utilization. Our simulation experiments show that ESJFM outperforms traditional algorithms like SJF and MSJF, offering a more scalable and efficient solution for modern heterogeneous cloud environments. This work contributes to the field by providing a robust, adaptive scheduling model that addresses critical issues such as load balancing, resource underutilization, and task starvation in cloud systems.
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How to Cite This Paper
Ali Mothana, Hikmat Al-Quhfa, Jie Song (2025). ESJFM: An Enhanced SJF Scheduling Model to Improve Resource Utilization In Heterogeneous Cloud Environments. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
ESJFM An Enhanced SJF Scheduling Model to Improve Resource Utilization In Heterogeneous Cloud EnvironmentsDownload
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