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Deep-Q learning-based heterogeneous earliest finish time scheduling algorithm for scientific workflows in cloud

journal contribution
posted on 2020-01-01, 00:00 authored by A Kaur, P Singh, R Singh Batth, C Peng Lim
The complex and large-scale scientific workflow applications are effectively executes on the cloud. The performance of cloud computing highly depends on the task scheduling. Optimal workflow scheduling is still a challenge that needs to be addressed due to the conflicting objectives and increasing demand for quality of service. Task scheduling is an NP-hard problem due to its complexity. The newly introduced methods for resolving the problem of task scheduling are facing challenges to take the benefits of all aspects of cloud computing. In this article, we study the joint optimization of cost and makespan of scheduling workflows in infrastructure as a service clouds and propose a new workflow scheduling scheme using deep learning. In this scheme, a deep-Q learning-based heterogeneous earliest-finish-time (DQ-HEFT) algorithm is developed, which closely integrates the deep learning mechanism with the task scheduling heuristic HEFT. The workflowsim simulator is used for the experiment of the real-world and synthetic workflows. The experiment results demonstrate the efficiency of our proposed approach compared with existing algorithms. This technique can achieve significantly better makespan and speed metrics with a remarkably higher volume of data and can run faster compared with the existing workflow scheduling algorithms in cloud computing environment.

History

Journal

Software - Practice and Experience

Volume

52

Pagination

689-709

Location

Chichester, Eng.

ISSN

0038-0644

eISSN

1097-024X

Language

English

Notes

Early View Article

Publication classification

C1 Refereed article in a scholarly journal

Issue

3

Publisher

WILEY