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Preemptive Min Max Optimal Cost Based Scheduling for Improving the Load Balancing in Virtualized Cloud Environment

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Abstract

Load balancing and scheduling are essential components of cloud computing that aim to optimize resource allocation and utilization. In a cloud environment, multiple virtual machines and applications compete for shared resources, and efficient load balancing and scheduling mechanisms are crucial for ensuring optimal performance and resource utilization. The prior methodologies attains higher utilization to allocate task due to makspan problem leads more deadlock occurrence and request failures with higher energy consideration. Furthermore, the paper examines the challenges and considerations in load balancing and scheduling, such as dynamic workload variations, heterogeneity of resources, and QoS requirements. To handle this issues, to propose an Efficient Load Balancing Based Predictive Priority-based Preemptive Min max priority Load balancer (PMin-Max LB) to improve the service optimality in Decentralized cloud server. Initially the user request accessibility and task progress is evaluated through Service level Workload Impact score (SLWIS). Then introducing Workload Scaling Job vector estimation is carried by Particle Swarm Optimization (PSO). By evaluating the Makespan Instance Scaling Changeover Virtualization (MISCV) task weights are assigned to virtual machine by applying Preemptive Min max priority Load balancer (PMin-Max LB) to reduce the workload to improve the performance. The proposed system improves the impact of load balancing and scheduling on cloud performance, scalability, and cost-effectiveness. Finally proposed system attains the potential advancements in load balancing and scheduling techniques improves the computing approach in virtualization process to reduce the workload burned to optimize the heterogeneity and Quality of service.

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Data Availability

The dataset utilized and examined in this study can be obtained from the corresponding author upon reasonable request.

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Acknowledgements

The authors acknowledged the RRASE College of Engineering, Chennai, Tamilnadu, India; Sri Eshwar College of Engineering, Coimbatore, Tamilnadu, India; Ponjesly College of Engineering, Nagercoil, Tamilnadu, India for supporting the research work by providing the facilities.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, RRASE College of Engineering, Chennai, Tamilnadu, India

    K. Ravikumar

  2. Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamilnadu, India

    K. Saravanakumar

  3. Department of Information Technology, Ponjesly College of Engineering, Nagercoil, Tamilnadu, India

    Anand Viswanathan

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  1. K. Ravikumar
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  2. K. Saravanakumar
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  3. Anand Viswanathan
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The success of this research is attributed to the collaborative efforts and invaluable contributions made by all participating authors.

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Correspondence to K. Ravikumar.

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Ravikumar, K., Saravanakumar, K. & Viswanathan, A. Preemptive Min Max Optimal Cost Based Scheduling for Improving the Load Balancing in Virtualized Cloud Environment. SN COMPUT. SCI. 5, 754 (2024). https://doi.org/10.1007/s42979-024-03085-9

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