A novel approach for Credit-Based Resource Aware Load Balancing algorithm (CB-RALB-SA) for scheduling jobs in cloud computing

In recent years, cloud computing has gained popularity, mainly because of its utility and relevance to current technological trends. It is an arrangement that is highly customizable and encapsulated for providing better computational services to its clients worldwide. In the cloud, computing schedul...

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Bibliographic Details
Published in:Data & knowledge engineering Vol. 145; p. 102138
Main Authors: Narwal, Abhikriti, Dhingra, Sunita
Format: Journal Article
Language:English
Published: Elsevier B.V 01.05.2023
Subjects:
Cloud computing
Credit based scheduling algorithm
Enhanced multiobjective scheduling algorithm
Load balancing
Resource aware load balancing
Task scheduling
Virtual machines
Cloud computing
Enhanced multiobjective scheduling algorithm
Resource aware load balancing
Credit based scheduling algorithm
Task scheduling
Virtual machines
Load balancing
ISSN:0169-023X
Online Access:Get full text
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Summary:In recent years, cloud computing has gained popularity, mainly because of its utility and relevance to current technological trends. It is an arrangement that is highly customizable and encapsulated for providing better computational services to its clients worldwide. In the cloud, computing scheduling plays a pivotal role in optimizing resources. A better scheduling algorithm should be efficient and impartial, reducing the makespan time with proper resource utilization. However, most scheduling algorithms customarily lead to less resource utilization, termed load imbalance. The analysis of the existing papers exhibits better Makespan time but cannot guarantee the load-balanced mapping of jobs with proper resource utilization. Therefore, to eliminate the shortcomings of the prevalent/existing algorithms and enhance the performance, CB-RALB-SA, Credit-based Resource Aware Load Balancing scheduling algorithm has been rendered. The proposed work ensures a balanced distribution of tasks based on the capabilities of the resources, which eventually proves sustainable improvement against the existing scheduling algorithms. Therefore, a novel Credit Based Resource Aware Load Balancing Scheduling algorithm (CB-RALB-SA) is proposed. The tasks weighted by the credit-based scheduling algorithm are then mapped to the resources considering each resource’s load and computing capability using FILL and SPILL functions of Resource Aware and Load using Honey bee optimization heuristic algorithm. With the experimental evaluations and results, it has been proved that the proposed approach provides 48.5% better in Processing Time and 16.90 % better results in makespan time than the Existing CBSA-LB algorithm. Thus, it improves the processor’s efficiency while uplifting the whole system’s performance and has saved memory allocated to tasks and RAM.
ISSN:0169-023X
DOI:10.1016/j.datak.2022.102138