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.

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.

scholarly journals EHEFT-R: multi-objective task scheduling scheme in cloud computing

2021 ◽  
Author(s):  
Honglin Zhang ◽  
Yaohua Wu ◽  
Zaixing Sun
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Solution Space ◽  
Finish Time ◽  
Task Scheduling Algorithm ◽  
Matching Efficiency ◽  
Earliest Finish Time ◽  
Core Issues

AbstractIn cloud computing, task scheduling and resource allocation are the two core issues of the IaaS layer. Efficient task scheduling algorithm can improve the matching efficiency between tasks and resources. In this paper, an enhanced heterogeneous earliest finish time based on rule (EHEFT-R) task scheduling algorithm is proposed to optimize task execution efficiency, quality of service (QoS) and energy consumption. In EHEFT-R, ordering rules based on priority constraints are used to optimize the quality of the initial solution, and the enhanced heterogeneous earliest finish time (HEFT) algorithm is used to ensure the global performance of the solution space. Simulation experiments verify the effectiveness and superiority of EHEFT-R.

An Improved Task Scheduling Algorithm Based on Multi-QoS in Cloud Computing

2014 ◽  
Vol 538 ◽  
pp. 512-515
Author(s):  
Feng Song Li ◽  
Yuan Sheng Lou
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Priority Scheduling ◽  
Task Scheduling Algorithm ◽  
Priority Function

For the issues of quality of service (QoS) in the cloud computing raised by users, this paper proposes a strategy for QoS classification and builds tasks' priority function modeling and through priority scheduling tasks, assigning tasks to the reasonable resources, and finally to complete the task efficiently, improve the utilization of resources.

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.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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