A reactive search optimization algorithm for scientific workflow scheduling using clustering techniques

Karpagam, M.; Geetha, K.; Rajan, C.

doi:10.1007/s12652-020-02480-3

Cited by 15 publications

(2 citation statements)

References 18 publications

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“…Generally, cloud computing involves multiple technologies that create a new way of handling information technology (IT). Cloud computing offers highly available and elastically scalable resources as subscription-based services like utility computing to execute scientific workflows [4][5]. The primary aim of the task scheduling methods is to increase the acceleration of the execution, where it allocates the resources to the workloads that have different execution times [6].…”

Section: Introductionmentioning

confidence: 99%

Effective Workflow Scheduling in Cloud Platform Using Data Aware based Adaptive Gravitational Search Algorithm

2023

IJIES

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In recent periods, cloud-computing environments are widely utilizing scientific workflows for executing large-scale applications. The workflow scheduling with the scientific standard for optimizing quality of service (QoS) parameters is a hard task. In the existing research studies, several metaheuristics optimization algorithms are employed for satisfying the QoS parameters such as resource utilization, cost, and makespan. Still, the existing metaheuristics optimization algorithms are insignificant for maintaining the balance between exploitation and exploration in a search space, because the algorithms are easily trapped in local optima. For addressing the above-stated issues, a data aware based adaptive gravitational search algorithm (DA-AGSA) technique is implemented to minimize the cost and makespan, and to schedule workflows in the cloud-computing platform. In the conventional GSA technique, a random coefficient is replaced by an adaptive weight function for improving convergence rate, and further, the weight function is multiplied with an acceleration term for facilitating quicker convergence. In this article, the performance of the DA-AGSA technique is validated by utilizing workflow sim for scheduling multiple workflows. An extensive experimental investigation showed that the DA-AGSA technique almost reduced 20% of the cost and 15% of the makespan compared to the conventional optimization algorithms on the Montage, CyberShake, and Epigenomics workflows with 1000 tasks. In addition, the DA-AGSA technique achieved a reliability of 0.99, 0.98, and 0.98 on the Montage, CyberShake and Epigenomics workflows with 1000 tasks.

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Section: Introductionmentioning

confidence: 99%

Effective Workflow Scheduling in Cloud Platform Using Data Aware based Adaptive Gravitational Search Algorithm

2023

IJIES

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“…The clustering scheduling algorithm [43,[47][48][49] also has two phases. The first phase is mainly to analyze the characteristics of tasks or processors and cluster tasks or processors according to different clustering conditions.…”

Section: Introductionmentioning

confidence: 99%

List Scheduling Algorithm Based on Virtual Scheduling Length Table in Heterogeneous Computing System

Zhou

Liao

et al. 2021

Wireless Communications and Mobile Computing

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Edge computing needs the close cooperation of cloud computing to better meet various needs. Therefore, ensuring the efficient implementation of applications in cloud computing is not only related to the development of cloud computing itself but also affects the promotion of edge computing. However, resource management and task scheduling strategy are important factors affecting the efficient implementation of applications. Therefore, aiming at the task scheduling problem in cloud computing environment, this paper proposes a new list scheduling algorithm, namely, based on a virtual scheduling length (BVSL) table algorithm. The algorithm first constructs the predicted remaining length table based on the prescheduling results, then constructs a virtual scheduling length table based on the predicted remaining length table, the current task execution cost, and the actual start time of the task, and calculates the task priority based on the virtual scheduling length table to make the overall path the longest task is scheduled first, thus effectively shorten the scheduling length. Finally, the processor is selected for the task based on the predicted remaining length table. The selected processor may not be the earliest for the current task, but it can shorten the finish time of the task in the next phase and reduce the scheduling length. To verify the effectiveness of the scheduling method, experiments were carried out from two aspects: randomly generated graphs and real-world application graphs. Experimental results show that the BVSL algorithm outperforms the latest Improved Predict Priority Task Scheduling (IPPTS) and RE-18 scheduling methods in terms of makespan, scheduling length ratio, speedup, and the number of occurrences of better quality of schedules while maintaining the same time complexity.

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A Novel Optimized Recurrent Network-Based Automatic System for Speech Emotion Identification

Koppula¹,

Rao²,

Nabi³

et al. 2022

Wireless Pers Commun

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A reactive search optimization algorithm for scientific workflow scheduling using clustering techniques

Cited by 15 publications

References 18 publications

Effective Workflow Scheduling in Cloud Platform Using Data Aware based Adaptive Gravitational Search Algorithm

Effective Workflow Scheduling in Cloud Platform Using Data Aware based Adaptive Gravitational Search Algorithm

List Scheduling Algorithm Based on Virtual Scheduling Length Table in Heterogeneous Computing System

A Novel Optimized Recurrent Network-Based Automatic System for Speech Emotion Identification

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