scholarly journals Task Scheduling Algorithm in Cloud Computing Based on Fairness Load Balance and Minimum Completion Time

2016 ◽  
Author(s):  
Yizhen Wang ◽  
Yongqiang Sun ◽  
Yi Sun
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Load Balance ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Task Scheduling Algorithm ◽  
Minimum Completion Time

scholarly journals Load Balancing Technique with an Efficient Method

2019 ◽  
Vol 8 (6) ◽  
pp. 1257-1262
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Efficient Method ◽  
Load Balance ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Quality Of Services ◽  
Huge Number ◽  
Task Scheduling Algorithm

There are a huge number of nodes connected to web computing to offer various types of web services to provide cloud clients. Limited numbers of nodes connected to cloud computing have to execute more than a thousand or a million tasks at the same time. So it is not so simple to execute all tasks at the same particular time. Some nodes execute all tasks, so there is a need to balance all the tasks or loads at a time. Load balance minimizes the completion time and executes all the tasks in a particular way.There is no possibility to keep an equal number of servers in cloud computing to execute an equal number of tasks. Tasks that are to be performed in cloud computing would be more than the connected servers. Limited servers have to perform a great number of tasks.We propose a task scheduling algorithm where few nodes perform the jobs, where jobs are more than the nodes and balance all loads to the available nodes to make the best use of the quality of services with load balancing.

Task Scheduling Algorithm Based on Improved Min-Min Algorithm in Cloud Computing Environment

2013 ◽  
Vol 303-306 ◽  
pp. 2429-2432 ◽  
Author(s):  
Guan Wang ◽  
Hai Cun Yu
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Total Completion Time ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Task Schedule ◽  
Task Scheduling Algorithm

Task schedule algorithms directly related to the speed and quality of schedule. Min-Min algorithm always completes the shortest total completion time task first, and has the characteristic of simple and shortest completion time. This paper research scheduling algorithm based on Min—Min algorithm. The result shows that the proposed algorithm is efficient in the cloud computing environment.

scholarly journals A Task Scheduling Algorithm Based on Classification Mining in Fog Computing Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Lindong Liu ◽  
Deyu Qi ◽  
Naqin Zhou ◽  
Yilin Wu
Keyword(s):  
Task Scheduling ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Apriori Algorithm ◽  
Distributed Environment ◽  
Data Mining Technique ◽  
Task Scheduling Algorithm ◽  
Increase Productivity ◽  
Minimum Completion Time

Fog computing (FC) is an emerging paradigm that extends computation, communication, and storage facilities towards the edge of a network. In this heterogeneous and distributed environment, resource allocation is very important. Hence, scheduling will be a challenge to increase productivity and allocate resources appropriately to the tasks. We schedule tasks in fog computing devices based on classification data mining technique. A key contribution is that a novel classification mining algorithm I-Apriori is proposed based on the Apriori algorithm. Another contribution is that we propose a novel task scheduling model and a TSFC (Task Scheduling in Fog Computing) algorithm based on the I-Apriori algorithm. Association rules generated by the I-Apriori algorithm are combined with the minimum completion time of every task in the task set. Furthermore, the task with the minimum completion time is selected to be executed at the fog node with the minimum completion time. We finally evaluate the performance of I-Apriori and TSFC algorithm through experimental simulations. The experimental results show that TSFC algorithm has better performance on reducing the total execution time of tasks and average waiting time.

Research of Cloud Computing Task Scheduling Algorithm Based on Improved Genetic Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 2426-2429 ◽  
Author(s):  
Jun Wei Ge ◽  
Yong Sheng Yuan
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Task Completion Time ◽  
Task Scheduling Algorithm ◽  
Reasonable Use

Use genetic algorithm for task allocation and scheduling has get more and more scholars' attention. How to reasonable use of computing resources make the total and average time of complete the task shorter and cost smaller is an important issue. The paper presents a genetic algorithm consider total task completion time, average task completion time and cost constraint. Compared with algorithm that only consider cost constraint (CGA) and adaptive algorithm that only consider total task completion time by the simulation experiment. Experimental results show that this algorithm is a more effective task scheduling algorithm in the cloud computing environment.

scholarly journals An Improved Coral Reef Optimization-Based Scheduling Algorithm for Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Shuzhen Wan ◽  
Lixin Qi
Keyword(s):  
Cloud Computing ◽  
Coral Reef ◽  
Task Scheduling ◽  
Load Balance ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Difficult Problem ◽  
Task Scheduling Algorithm ◽  
The Cost

An important problem in cloud computing faces the challenge of scheduling tasks to virtual machines to meet the cost and time demands, while maintaining the Quality of Service (QoS). Allocating tasks into cloud resources is a difficult problem due to the uncertainty of consumers’ future requirements and the diversity of providers’ resources. Previous studies, either on modeling or scheduling approaches, can no longer offer a satisfactory solution. In this paper, we establish a resource allocation framework and propose a novel task scheduling algorithm. An improved coral reef optimization (ICRO) is proposed to deal with this task scheduling problem. In ICRO, the better-offspring and multicrossover strategies increase the convergent speed and improve the quality of solutions. In addition, a novel load balance-aware mutation enhances the load balance among virtual machines and adjusts the number of resources provided to users. Experimental results show that compared with other algorithms, ICRO can significantly reduce the makespan and cost of the scheduling, while maintaining a better load balance in the system.

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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