TY - GEN
T1 - A Dynamic Load Scheduling Using Binary Self-adaptive JAYA (BSAJAYA) Algorithm in Cloud-Based Computing
AU - Mishra, Kaushik
AU - Majhi, Santosh Kumar
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - The load scheduling is a paramount concern in the cloud-based computing due to the involvement of conflicting parameters and fluctuating demands of users. Though the problem of load scheduling comes in the category of NP-hard problem, it is utmost essential to design a load scheduling approach to tackle the challenges in cloud computing. These challenges could be elevated by using metaheuristic approaches that offer an optimal solution to an NP-hard problem. In this research, authors propose a binary self-adaptive JAYA-based load scheduling algorithm to solve the dynamically independent load scheduling problem in cloud-based computing. The proposed algorithm is compared with the lately invented metaheuristic-based task scheduling algorithms such as bird swarm optimization (BSO), modified particle swarm optimization (MPSO), and the standard JAYA. The proposed algorithm is evaluated using a real-world dataset for the various QoS scheduling parameters to advocate the effectiveness of the algorithm. The simulated results show notable improvements over other compared algorithms for makespan, average resource utilization, and load-balancing.
AB - The load scheduling is a paramount concern in the cloud-based computing due to the involvement of conflicting parameters and fluctuating demands of users. Though the problem of load scheduling comes in the category of NP-hard problem, it is utmost essential to design a load scheduling approach to tackle the challenges in cloud computing. These challenges could be elevated by using metaheuristic approaches that offer an optimal solution to an NP-hard problem. In this research, authors propose a binary self-adaptive JAYA-based load scheduling algorithm to solve the dynamically independent load scheduling problem in cloud-based computing. The proposed algorithm is compared with the lately invented metaheuristic-based task scheduling algorithms such as bird swarm optimization (BSO), modified particle swarm optimization (MPSO), and the standard JAYA. The proposed algorithm is evaluated using a real-world dataset for the various QoS scheduling parameters to advocate the effectiveness of the algorithm. The simulated results show notable improvements over other compared algorithms for makespan, average resource utilization, and load-balancing.
UR - https://www.scopus.com/pages/publications/85111245669
UR - https://www.scopus.com/inward/citedby.url?scp=85111245669&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-0695-3_12
DO - 10.1007/978-981-16-0695-3_12
M3 - Conference contribution
AN - SCOPUS:85111245669
SN - 9789811606946
T3 - Lecture Notes in Networks and Systems
SP - 111
EP - 121
BT - Advances in Intelligent Computing and Communication - Proceedings of ICAC 2020
A2 - Das, Swagatam
A2 - Mohanty, Mihir Narayan
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Intelligent Computing and Advances in Communication, ICAC 2020
Y2 - 25 November 2020 through 26 November 2020
ER -