Optimized multi‐objective Q‐learning with enhanced beetle swarm optimization based scientific workflows scheduling on cloud computing environment-Reference-Cited by-同舟云学术

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Optimized multi‐objective Q‐learning with enhanced beetle swarm optimization based scientific workflows scheduling on cloud computing environment

Published:2022-10-23 Issue:1 Volume:35 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency and Computation

Author:

S Nivethithai¹,B Hariharan²^ORCID

Affiliation:

1. Department of Computational Intelligence School of Computing, SRM Institute of Science and Technology Kattankulathur India

2. Department of Computational Intelligence, School of Computing SRM Institute of Science and Technology Kattankulathur India

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.7409

Reference28 articles.

1. Cost Effective Genetic Algorithm for Workflow Scheduling in Cloud Under Deadline Constraint

2. Multi‐objective cloud workflow scheduling: a multiple populations ant colony system approach;Chen ZG;IEEE Trans Cybernet,2018

3. An energy-efficient, QoS-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing DVFS and approximate computations

4. Energy efficient workflow scheduling with virtual machine consolidation for green cloud computing

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