Multi-objective Task Scheduling Using Chaotic Quantum-Behaved Chicken Swarm Optimization (CQCSO) in Cloud Computing Environment

Gopalakrishnan, K.; Vennila, S.

doi:10.1007/978-981-15-5258-8_74

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…The second is chemical reaction optimization, which is inspired by chemical reactions. Cuckoo Search Algorithm (CSA) [34][35][36][37], Chicken Swarm Optimization (GSO) [38][39][40][41][42], Genetic Algorithm (GA) [44][45][46][47][48], BA [50][51][52][53], Whale Optimization Algorithm (WOA) [55][56][57][58][59][60][61], and Gray Wolf Optimization (GWO) [63][64][65][66][67][68][69][70] are examples of nature-inspired algorithms. A brief summary of the mentioned methods is given in Tables 5 and 6.…”

Section: Related Work Of Meta-heuristic Scheduling Algorithmmentioning

confidence: 99%

“…. Multi objective [35][36][37] Single objective [39,40] Multi objective [41,42] Multi objective [44][45][46][47][48] Single objective [50,52,53] Multi objective [51] Single objective [56,58] Multi objective [55,57,[59][60][61] Single objective [65,68] Multi objective [63,64,66,67,69,70] Chemistry-based Physics-based Nature-based…”

Section: Related Work Of Meta-heuristic Scheduling Algorithmmentioning

confidence: 99%

“…The multi-objective Chaotic Quantum Behaved Chicken Swarm Optimization (CQCSO) method for task scheduling is presented by Kiruthiga and Mary Vennila [41]. CQCSO is produced by applying the chaotic and quantum theories to the standard CSO in order to solve local optima and premature convergence problems.…”

Section: Icuckoo Search Based Task Schedulingmentioning

confidence: 99%

See 2 more Smart Citations

Task Scheduling in a Cloud Environment Based on Meta-Heuristic Approaches: A Survey

Abdulrazzaq,

Shati,

Hoomod

2024

Iraqi Journal of Science

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Cloud computing is one of the emerging technologies that expands the boundaries of the internet by using centralized servers to maintain data and resources. It allows users and consumers to use various applications provided by the cloud provider, but one of the major issues is task scheduling. Task scheduling is employed for the purpose of mapping the requests of users to the appropriate resources available. This paper provides a detailed survey of the available scheduling techniques for cloud environments based on six common metaheuristic algorithms. Those algorithms are the Cuckoo Search Algorithm (CSA), Chicken Swarm Optimization (CSO), Genetic Algorithm (GA), Bat Algorithm (BA), Whale Optimization Algorithm (WOA), and Grey Wolf Optimization (GWO). The literature is analyzed from three perspectives: task type, objectives to be optimized, simulation environment, and quality of service performance metrics. In addition, the research gaps and future directions for future investigation are presented.

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Section: Related Work Of Meta-heuristic Scheduling Algorithmmentioning

confidence: 99%

Section: Related Work Of Meta-heuristic Scheduling Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Task Scheduling in a Cloud Environment Based on Meta-Heuristic Approaches: A Survey

Abdulrazzaq,

Shati,

Hoomod

2024

Iraqi Journal of Science

View full text Add to dashboard Cite

show abstract

“…Approach achieves deadline at minimum time and budget. Kiruthiga G., Mary Vennila S. (2021) [23], presented a Chaotic Quantum-behaved Chicken Swarm Optimization (CQCSO) algorithm. The algorithm followed a chaotic and quantum theory to solve the issue like premature convergence and local optima.…”

Section: Related Workmentioning

confidence: 99%

“…Devipriya and Ramesh (2013) [32],presented improved Max-Min technique. The technique matches larger tasks to lower computing e ciency virtual machine and vice-versa.MajidHabibi, NimaJafariNavimipour (2016) [23], presented a meta-heuristic optimization scheduling heuristic Imperialist Competitive Algorithm (ICA). ICA achieves 0.7 percentage of improvement in execution time than Genetic Algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Pair-Task Heuristic for Scheduling Tasks in Heterogeneous Multi-cloud Environment

Krishnasamy¹,

Periasamy

et al. 2023

Wireless Pers Commun

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Heterogeneous multi-cloud environments make use of a collection of varied performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Mapping problem provides an optimal solution in scheduling tasks to distributed heterogeneous clouds is termed NP-complete, which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of 'n' tasks in two groups among a set of 'm' clouds, we propose three heuristics PTL (Pair-Task Threshold Limit), PTMax-Min, and PTMin-Max. Firstly to determine the tasks scheduling order, proposed heuristics based on the tasks attributes calculate tasks threshold value. Tasks sorted in descending value of threshold. Group G1 comprises tasks ordered in descending value of threshold. Group G2 comprises remaining tasks ordered in ascending value of threshold. Secondly, tasks form Group 1 are scheduled rst based on minimum completion time, and then tasks in Group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. Heuristics PTL, PTMax-Min, and PTMin-Max bring out reduced makespan compared to MCT, MET, and Min-min.

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A Pair-Task Heuristic for Scheduling Tasks in Heterogeneous Multi-Cloud Environment

Krishnasamy

Periasamy

et al. 2022

Preprint

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Heterogeneous multi-cloud environments make use of a collection of varied performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Mapping problem provides an optimal solution in scheduling tasks to distributed heterogeneous clouds is termed NP-complete, which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of ‘n’ tasks in two groups among a set of 'm' clouds, we propose three heuristics PTL (Pair-Task Threshold Limit), PTMax-Min, and PTMin-Max. Firstly to determine the tasks scheduling order, proposed heuristics based on the tasks attributes calculate tasks threshold value. Tasks sorted in descending value of threshold. Group G1 comprises tasks ordered in descending value of threshold. Group G2 comprises remaining tasks ordered in ascending value of threshold. Secondly, tasks form Group 1 are scheduled first based on minimum completion time, and then tasks in Group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. Heuristics PTL, PTMax-Min, and PTMin-Max bring out reduced makespan compared to MCT, MET, and Min-min.

show abstract

Multi-objective Task Scheduling Using Chaotic Quantum-Behaved Chicken Swarm Optimization (CQCSO) in Cloud Computing Environment

Cited by 3 publications

References 17 publications

Task Scheduling in a Cloud Environment Based on Meta-Heuristic Approaches: A Survey

Task Scheduling in a Cloud Environment Based on Meta-Heuristic Approaches: A Survey

A Pair-Task Heuristic for Scheduling Tasks in Heterogeneous Multi-cloud Environment

A Pair-Task Heuristic for Scheduling Tasks in Heterogeneous Multi-Cloud Environment

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