An energy-efficient task-scheduling algorithm based on a multi-criteria decision-making method in cloud computing

2020 ◽  
Vol 33 (9) ◽  
pp. e4379 ◽  
Author(s):  
Reihaneh Khorsand ◽  
Mohammadreza Ramezanpour
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Energy Efficient ◽  
Scheduling Algorithm ◽  
Multi Criteria Decision Making ◽  
Task Scheduling Algorithm

scholarly journals Task scheduling algorithm based on multi criteria decision making method for cloud computing environment: TSABMCDMCCE

2019 ◽  
Vol 9 (1) ◽  
pp. 279-291 ◽  
Author(s):  
Proshikshya Mukherjee ◽  
Prasant Kumar Pattnaik ◽  
Tanmaya Swain ◽  
Amlan Datta
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Multi Criteria Decision Making ◽  
Cloud Environment ◽  
Computing Environment ◽  
Queuing Model ◽  
Cloud Computing Environment ◽  
Task Scheduling Algorithm

AbstractThis Paper focuses on multi-criteria decision making techniques (MCDMs), especially analytical networking process (ANP) algorithm to design a model in order to minimize the task scheduling cost during implementation using a queuing model in a cloud environment and also deals with minimization of the waiting time of the task. The simulated results of the algorithm give better outcomes as compared to other existing algorithms by 15 percent.

An Improved Task Scheduling Mechanism Using Multi-Criteria Decision Making in Cloud Computing

2021 ◽  
pp. 767-795
Author(s):  
Suvendu Chandan Nayak ◽  
Chitaranjan Tripathy
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Synthetic Data ◽  
Task Completion ◽  
Data Sets ◽  
Multi Criteria Decision Making ◽  
Order Preference ◽  
First Come First Serve

In this work, the authors propose Multi-criteria Decision-making to schedule deadline based tasks in cloud computing. The existing backfilling task scheduling algorithm could not handle similar tasks for scheduling. In backfilling algorithm, tasks are backfilled to provide ideal resources to schedule other deadline sensitive tasks. However, the task to be backfilled is selected on first come, first serve (FCFS) basis from scheduling queue. The scheduling performances require to be improved when, there are similar tasks. In this proposed work, the authors propose to implement MCDM technique, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to improve the performance of the backfilling algorithm through scheduling deadline sensitive tasks in cloud computing. It resolves the conflicts among the similar tasks that is used as a decision support system. The work is simulated with synthetic data sets based on slack values of the tasks. The performance results affirm the task completion and reduction in task rejection compared to the existing backfilling algorithm.

An Improved Task Scheduling Mechanism Using Multi-Criteria Decision Making in Cloud Computing

2019 ◽  
Vol 14 (2) ◽  
pp. 92-117 ◽  
Author(s):  
Suvendu Chandan Nayak ◽  
Chitaranjan Tripathy
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Synthetic Data ◽  
Task Completion ◽  
Data Sets ◽  
Multi Criteria Decision Making ◽  
First Come First Serve ◽  
Topsis Technique

In this work, the authors propose Multi-criteria Decision-making to schedule deadline based tasks in cloud computing. The existing backfilling task scheduling algorithm could not handle similar tasks for scheduling. In backfilling algorithm, tasks are backfilled to provide ideal resources to schedule other deadline sensitive tasks. However, the task to be backfilled is selected on first come, first serve (FCFS) basis from scheduling queue. The scheduling performances require to be improved when, there are similar tasks. In this proposed work, the authors propose to implement MCDM technique, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to improve the performance of the backfilling algorithm through scheduling deadline sensitive tasks in cloud computing. It resolves the conflicts among the similar tasks that is used as a decision support system. The work is simulated with synthetic data sets based on slack values of the tasks. The performance results affirm the task completion and reduction in task rejection compared to the existing backfilling algorithm.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

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