scholarly journals A Load Balancing Task Scheduling Algorithm based on Feedback Mechanism for Cloud Computing

2016 ◽  
Vol 9 (4) ◽  
pp. 41-52 ◽  
Author(s):  
Zhang Qian ◽  
Ge Yufei ◽  
Liang Hong ◽  
Shi Jin
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Feedback Mechanism ◽  
Task Scheduling Algorithm

scholarly journals Capacity Preserve Assignment Scheduling Algorithm in context of Determination System for Cloud Computing

2019 ◽  
Vol 8 (2S8) ◽  
pp. 647-650
Keyword(s):  
Cloud Computing ◽  
Information Systems ◽  
Load Balancing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Processing Condition ◽  
Feedback Mechanism ◽  
Determination System

Booking figuring is reliably a fervently issue in appropriated processing condition. Remembering the true objective to take out system bottleneck and modify stack logically. A stack changing endeavor booking count in light of weighted self-assertive and input frameworks was proposed in this paperFrom the outset the picked cloud masterminding host picked assets by necessities and made static estimation, and some time later coordinated them; other than the tally picked assets from which composed by weight self-confidently; by then it got standing out powerful data from effect burden to channel and sort the left. Finally it accomplished oneself adaptively to structure stack through information systems. The examination demonstrates that the calculation has stayed away from the framework bottleneck adequately and has accomplished adjusted burden and furthermore self-flexibility to it.keywords: Task Scheduling; Feedback Mechanism; Cloud Computing; Load Balancing

The Cloud Computing Tasks Scheduling Algorithm Based on Improved K-Means

2014 ◽  
Vol 513-517 ◽  
pp. 1830-1834
Author(s):  
Xue Ying Sun ◽  
Xue Liang Fu ◽  
Hua Hu ◽  
Tao Gui
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Theoretical Research ◽  
Experimental Result ◽  
Task Completion ◽  
Time Efficiency ◽  
Task Scheduling Algorithm ◽  
Resource Requirements

Cloud task scheduling is a hot technology today, how to effectively improve the utilization of resources, time efficiency, load balancing, is the focus and difficult of the study. The time efficiency, load balancing of K-Min algorithm still need to be improved, so this paper proposes cloud computing task scheduling algorithm based on modified K-Means (Improved K-Min), firstly, This paper improves the k-means algorithm using the BFA and PSO,then according to the length attribute of the task, resource requirements, the algorithm uses the improved K-means for packet processing tasks, then performs Min-Min scheduling algorithm within the group. Through theoretical research and simulation of Cloud-sim platform, when the number of tasks is 300, experimental result is best, comparing with Min-Min algorithm, the total task completion time improved 17.13%.

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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