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

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.

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 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.

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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