Pre-emptive Approach for Resource Scheduling in Cloud Computing

Kumar, Senthil; Senthil, P.; Sabitha, E; Kumar, Prince; Balasundaram, A.; Ashokkumar, S.

doi:10.1109/icosec49089.2020.9215358

Cited by 2 publications

(1 citation statement)

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“…This process is important for optimizing resource utilization and ensuring that all tasks are completed efficiently and within their desired timelines [1]. There are different approaches to workload scheduling in the cloud, including rulebased scheduling [2], resource-based scheduling [3], and priority-based scheduling [4]. Cloud workload scheduling also involves the use of automation and orchestration tools to automate the scheduling and management of resources.…”

Section: Introductionmentioning

confidence: 99%

Quality of Service Aware Task Replanning Algorithm for Web-Service Provisioning in Cloud Environment

2024

IJIES

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In web service composition, the combination of multiple individual services to create complex workflows introduces additional challenges, and task scheduling is a crucial aspect that significantly impacts the overall system performance. The main challenge in web service composition task scheduling arises from the varying nature of services, which may have diverse processing requirements, data dependencies, and execution times. Hence, to address these challenges, the existing work have proposed many methods. But, as cloud environments often host these web services, the dynamic nature of cloud resources and the unpredictability of workloads add another layer of complexity to the task scheduling process. Due to this the existing works have attempted very less work on providing an efficient replanning task based on real-time changes in resource availability. Hence, this paper proposes a novel approach to cloud workload task scheduling in web service composition that incorporates efficient replanning and time complexity analysis called as Web service composition-efficient re-planning (WSC-ERP). The proposed approach takes into account various factors, such as task dependencies, and resource availability, to generate an optimized schedule that minimizes execution time and maximizes resource utilization. The WSC-ERP has been evaluated using the Montage scientific workload. The results show that the WSC-ERP provides better performance in terms of executional time, power sum, power average, energy consumption and reliability when compared with the Energy-Minimized Scheduling (EMS), and Evolutionary Computing based Web Service Composition (EC-WSC). The results show that the WSC-ERP has showed an improvement of 80.27% and 78.45% for average execution time, 89.98% and 87.49% for average power sum, 63.44% and 41.93% for average power average, 92.58% and 87.87% for average energy consumption, 4.97% and 4.07% for average reliability when compared with the existing EMS and EC-WSC models respectively.

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