scholarly journals Edge computing task scheduling strategy based on load balancing

2020 ◽  
Vol 309 ◽  
pp. 03025
Author(s):  
Lintan Sun ◽  
Zigan Li ◽  
Jingxian Lv ◽  
Chenfei Wang ◽  
Yajuan Wang ◽  
...  
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Rapid Development ◽  
Mobile Terminal ◽  
Heavy Load ◽  
Dynamic Selection ◽  
Scheduling Strategy ◽  
Light Load ◽  
Task Scheduling Algorithm

With the rapid development and wide application of the Internet of Everything, in order to cope with the increasing amount of data and computational scale of mobile terminal processing, and the imbalance of existing scheduling algorithms and low resource utilization, this paper proposes a task scheduling algorithm based on business priority. The algorithm firstly divides the service according to the priority of the service. Secondly, the standard deviation of the computing task group is used to determine the proportion of long and short services, and the dynamic selection model is established. Finally, according to the idea of secondary allocation, the task of heavy load is assigned to the scheduling strategy of light load resources to execute, and the service redistribution model is established. The simulation results show that compared with the typical algorithm, the proposed algorithm achieves the result of comprehensive consideration of Makespan and load balancing to improve system efficiency.

scholarly journals Research on Task Scheduling Strategy based on the Trustworthiness of MapReduce

2021 ◽  
Author(s):  
QIN Jun ◽  
SONG Yanyan ◽  
ZONG Ping
Keyword(s):  
Task Scheduling ◽  
Programming Model ◽  
Fault Tolerant ◽  
Scheduling Algorithm ◽  
Rapid Development ◽  
Computing Technology ◽  
Scheduling Strategy ◽  
Task Scheduling Algorithm ◽  
Good Environment ◽  
Massive Data Processing

With the rapid development and popularization of information technology, cloud computing technology provides a good environment for solving massive data processing. Hadoop is an open-source implementation of MapReduce and has the ability to process large amounts of data. Aiming at the shortcomings of the fault-tolerant technology in the MapReduce programming model, this paper proposes a reliability task scheduling strategy that introduces a failure recovery mechanism, evaluates the trustworthiness of resource nodes in the cloud environment, establishes a trustworthiness model, and avoids task allocation to low reliability node, causing the task to be re-executed, wasting time and resources. Finally, the simulation platform CloudSim verifies the validity and stability of the task scheduling algorithm and scheduling model proposed in this paper.

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.

Design and Construction of Distributed JavaScript Parsing System

2014 ◽  
Vol 6 (4) ◽  
pp. 1-14
Author(s):  
Bo Shen ◽  
Wei Huang ◽  
Xiaodi Li
Keyword(s):  
Search Engine ◽  
Distributed System ◽  
Task Scheduling ◽  
Information Acquisition ◽  
Scheduling Algorithm ◽  
Rapid Development ◽  
Internet Technology ◽  
The Internet ◽  
Computing Technology ◽  
Task Scheduling Algorithm

With the rapid development of the Internet technology, JS (short for JavaScript), as one of the representative of script languages, which is very powerful, is becoming more and more popular to the developers and users. But JS programming is more complex than usual static technology. In the field of search engine and information acquisition, it's very difficult to get the information hidden in script code. In this paper, the authors design a distributed system for parsing the JS code embedded in HTML file and retrieving the underling information. the authors describe how to extract JS codes from HTML file and parse them. Also, they introduce a task scheduling algorithm for the JS parsing system by employing Hadoop distributed computing technology. The experimental results indicate that the proposed algorithm and system can achieve a reasonable task scheduling efficiency and parse JS codes rapidly.

Research of Load Balancing Scheduling under Grid Computing

2014 ◽  
Vol 577 ◽  
pp. 935-938
Author(s):  
Cheng Yu Cai ◽  
Yuan Sheng Lou
Keyword(s):  
Load Balancing ◽  
Grid Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
The Other ◽  
Task Scheduling Algorithm ◽  
Better Than

In order to make up for the shortage of Min-Min in load balancing, a new task scheduling algorithm T-Max-Int Under the grid computing has been proposed in this paper. In T-Max-Int, the Loss Degree of Max-Int has been brought into Min-Min. T was in the form of percentage, which represents the proportion of selected tasks that have loss degree in the total tasks. Then, experiments of T have been taken to make Makespan the minimum. Finally, T-Max-Int, Max-Min, Min-Min were compared, which proved that T-Max-Min is better than the other two algorithms in aspects of Makespan and load balancing.

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