scholarly journals Improved FIFO Scheduling Algorithm Based on Fuzzy Clustering in Cloud Computing

Information ◽  
2017 ◽  
Vol 8 (1) ◽  
pp. 25 ◽  
Author(s):  
Jian Li ◽  
Tinghuai Ma ◽  
Meili Tang ◽  
Wenhai Shen ◽  
Yuanfeng Jin
Keyword(s):  
Cloud Computing ◽  
Fuzzy Clustering ◽  
Scheduling Algorithm

scholarly journals Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2122 ◽  
Author(s):  
Guangshun Li ◽  
Yuncui Liu ◽  
Junhua Wu ◽  
Dandan Lin ◽  
Shuaishuai Zhao
Keyword(s):  
Cloud Computing ◽  
Fuzzy Clustering ◽  
User Satisfaction ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Resource Scheduling ◽  
Computing Technology ◽  
Swarm Optimization ◽  
Resource Search ◽  
Scheduling Method

Cloud computing technology is widely used at present. However, cloud computing servers are far from terminal users, which may lead to high service request delays and low user satisfaction. As a new computing architecture, fog computing is an extension of cloud computing that can effectively solve the aforementioned problems. Resource scheduling is one of the key technologies in fog computing. We propose a resource scheduling method for fog computing in this paper. First, we standardize and normalize the resource attributes. Second, we combine the methods of fuzzy clustering with particle swarm optimization to divide the resources, and the scale of the resource search is reduced. Finally, we propose a new resource scheduling algorithm based on optimized fuzzy clustering. The experimental results show that our method can improve user satisfaction and the efficiency of resource scheduling.

Research on Multi-QoS and Trusted Task Scheduling in Cloud Computing Environment

2012 ◽  
Vol 263-266 ◽  
pp. 1892-1896
Author(s):  
Fu Fang Li ◽  
Dong Qing Xie ◽  
Ying Gao ◽  
Guo Wen Xie ◽  
Qiu Ye Guo
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Task Scheduling ◽  
Fuzzy Clustering ◽  
Scheduling Algorithm ◽  
Management Model ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Task Scheduling Algorithm ◽  
And Task

Multi-QoS and Trusted resource management and Task Scheduling are key problem in cloud computing. This paper presented an effective Multi-QoS and Trusted resource management model and Task Scheduling algorithm in Cloud Computing Environment. By using the idea of fuzzy clustering for reference, the proposed approach can subtly schedule the Cloud tasks to appropriate resources that exactly meets its’ multi-QoS needs of resources. Considering the credibility of cloud resources, the credibility and trusty of task scheduling has been dramatically improved.

AN EXTENDED MIN-MIN SCHEDULING ALGORITHM IN CLOUD COMPUTING

2018 ◽  
Vol 5 (2) ◽  
pp. 20
Author(s):  
MAIPAN-UKU J. Y. ◽  
MISHRA A. ◽  
A. ABDULGANIYU ◽  
ABDULKADIR A. ◽  
◽  
...  
Keyword(s):  
Cloud Computing ◽  
Scheduling 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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