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

Load and Cost Aware Min-Min Workflow Scheduling Algorithm for Heterogeneous Resources in Fog, Cloud and Edge Scenarios

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Load Balancing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Workflow Scheduling ◽  
Heterogeneous Environment ◽  
Cloud Servers ◽  
Heterogeneous Resources

Fog computing and Edge computing are few of the latest technologies which are offered as solution to challenges faced in Cloud Computing. Instead of offloading of all the tasks to centralized cloud servers, some of the tasks can be scheduled at intermediate Fog servers or Edge devices. Though this solves most of the problems faced in cloud but also encounter other traditional problems due to resource-related constraints like load balancing, scheduling, etc. In order to address task scheduling and load balancing in Cloud-fog-edge collaboration among servers, we have proposed an improved version of min-min algorithm for workflow scheduling which considers cost, makespan, energy and load balancing in heterogeneous environment. This algorithm is implemented and tested in different offloading scenarios- Cloud only, Fog only, Cloud-fog and Cloud-Fog-Edge collaboration. This approach performed better and the result gives minimum makespan, less energy consumption along with load balancing and marginally less cost when compared to min-min and ELBMM algorithms

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%.

scholarly journals Deadline-Constrained Task Scheduling with Load Balancing in Cloud Computing Environment

2019 ◽  
Vol 8 (9) ◽  
pp. 1790-1795
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Free Time ◽  
Cloud Provider ◽  
User Requirements ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Effective Utilization

Cloud computing environment is a conglomeration of computing resources that are maintained to provide a plethora of services to users as utility on a pay-per-use basis. When users request for services, a cloud provider has to allocate relevant computing resources not only to complete a task but also to satisfy the user requirements such as deadlines. This necessitates the design of suitable scheduling algorithm for effective utilization of available resources at the provider’s end. While doing so one of the concerns of the provider is to balance the load uniformly across resources. In this paper, the Max-Min algorithm has been extended by incorporating the concept of free time in order to effectively utilize the cloud resources through load balancing and at the same time meet the deadlines of individual tasks.

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