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 Task Scheduling Algorithm Based on Parallel Computing

2011 ◽  
Vol 267 ◽  
pp. 693-698
Author(s):  
Yi Jun Liu ◽  
Xiao Man He ◽  
Dan Feng ◽  
Yu Fang
Keyword(s):  
Parallel Computing ◽  
Service Quality ◽  
Task Scheduling ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
User Requirements ◽  
Task Scheduling Algorithm ◽  
The Status ◽  
Functional Features

Through the research on the existing parallel computing technologies, this paper bas an in-depth research and analysis on the status, issues to be addressed and functional features of parallel computing task scheduling, and for the current problems existed, presents a solution. The program can better reflect the heterogeneity and dynamicity of the parallel resources, as far as possible ensure the reliability and stability of the selected resources, while reducing task completion time and meeting user requirements for service quality.

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 Fog Task Scheduling using Clustering based Randomized Round Robin

2021 ◽  
Vol 22 (3) ◽  
pp. 295-302
Author(s):  
Shahid Sultan Hajam ◽  
Shabir Ahmad Sofi
Keyword(s):  
Internet Of Things ◽  
Task Scheduling ◽  
Clustering Algorithm ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Round Robin ◽  
The Internet ◽  
Delay Sensitive ◽  
Task Scheduling Algorithm ◽  
The Internet Of Things

Fog computing serves the delay-sensitive applications of the Internet of Things (IoT) in more efficient means than the cloud. The heterogeneity of the tasks and the limited fog resources make task scheduling a complicated job. This paper proposes a clustering based task scheduling algorithm. Specifically, the K-Means++ clustering algorithm is used for clustering the fog nodes. Randomized round robin, a task scheduling algorithm is applied to each cluster. The results show that the proposed algorithm reduces the system's average waiting time.

scholarly journals Task Scheduling Algorithm Based on Improved Firework Algorithm in Fog Computing

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 32385-32394 ◽  
Author(s):  
Shudong Wang ◽  
Tianyu Zhao ◽  
Shanchen Pang
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Task Scheduling Algorithm

Optimization of makespan and resource utilization in the fog computing environment through task scheduling algorithm

2020 ◽  
Vol 18 (01) ◽  
pp. 1941025
Author(s):  
R. Vijayalakshmi ◽  
V. Vasudevan ◽  
Seifedine Kadry ◽  
R. Lakshmana Kumar
Keyword(s):  
Resource Utilization ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Utilization Rate ◽  
Test Results ◽  
Computing Environment ◽  
Computing Model ◽  
Task Scheduling Algorithm ◽  
The Mean

The Fog computing is rising as a dominant and modern computing model to deliver Internet of Things (IoT) computations, which is an addition to the cloud computing standard to get it probable to perform the IoT requests in the network of edge. In those above independent and dispersed environment, resource allocation is vital. Therefore, scheduling will be a test to enhance potency and allot resources properly to the tasks. This paper offers a distinct task scheduling algorithm in the fog computing environment that tries to depreciate the makespan and maximize resource utilization. This algorithm catalogues the task based on the mean Suffrage value. The suggested algorithm gives much resource utilization and diminishes makespan. Our offered algorithm is compared with different alive scheduling for performance investigation, and test results confirm that our algorithm has a more significant resource utilization rate and low makespan than other familiar algorithms.

scholarly journals An Improved Min-Min Task Scheduling Algorithm with Grid Utilization and Minimized Makespan

2015 ◽  
Vol 14 (8) ◽  
pp. 5960-5966 ◽  
Author(s):  
Lalla Singh ◽  
Neha Agarwal
Keyword(s):  
Distributed Systems ◽  
Load Balancing ◽  
Grid Computing ◽  
Task Scheduling ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Basic Algorithm ◽  
Software Infrastructure ◽  
Task Scheduling Algorithm

Grid computing is hardware and software infrastructure which offers a economical, distributable, coordinated and credible access to strong computational abilities [1]. For optimal use of the abilities of large distributed systems, necessitate for successful and proficient scheduling algorithms is enforced. For diminution of total completion time and improvement of load balancing, many algorithms have been executed. In this paper, our goal is to propose new scheduling algorithm based on well known task scheduling algorithm i.e. Min-Min[1]. The proposed algorithm tries to use the advantages of this basic algorithm and excludes its drawbacks with better grid utilization and minimized makespan. In comparison to existing algorithms like Min-Min and improved Min-Min algorithm[1], our proposed algorithm is achieving better results for considered parameters.

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.

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