Enhanced Leveled DAG Prioritized Task Scheduling Algorithm in Distributed Computing System

2016 ◽  
Vol 10 (10) ◽  
pp. 1-13
Author(s):  
Amal EL-NATTAT ◽  
Nirmeen El-Bahnasawy ◽  
Ayman EL-SAYED
Keyword(s):  
Distributed Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Task Scheduling Algorithm

Task Scheduling in Heterogeneous Computing Systems Based on Machine Learning Approach

2020 ◽  
Vol 34 (12) ◽  
pp. 2051012
Author(s):  
Hui Xie ◽  
Li Wei ◽  
Dong Liu ◽  
Luda Wang
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Scheduling Problem ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Machine Learning Approach ◽  
Heterogeneous Computing Systems ◽  
Regression Theory

Task scheduling problem of heterogeneous computing system (HCS), which with increasing popularity, nowadays has become a research hotspot in this domain. The task scheduling problem of HCS, which can be described essentially as assigning tasks to the proper processor for executing, has been shown to be NP-complete. However, the existing scheduling algorithm suffers from an inherent limitation of lacking global view. Here, we reported a novel task scheduling algorithm based on Multi-Logistic Regression theory (called MLRS) in heterogeneous computing environment. First, we collected the best scheduling plans as the historical training set, and then a scheduling model was established by which we could predict the following schedule action. Through the analysis of experimental results, it is interpreted that the proposed algorithm has better optimization effect and robustness.

scholarly journals RETRACTED: Minimum makespan task scheduling algorithm in cloud computing

2016 ◽  
Vol 2 (3) ◽  
pp. 123
Author(s):  
N Sasikaladevi
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Minimum Makespan ◽  
Task Scheduling Algorithm

RETRACTEDFollowing a rigorous, carefully concerns and considered review of the article published in International Journal of Advances in Intelligent Informatics to article entitled “Minimum makespan task scheduling algorithm in cloud computing” Vol 2, No 3, pp. 123-130, November 2016, DOI: http://dx.doi.org/10.26555/ijain.v2i3.59.This paper has been found to be in violation of the International Journal of Advances in Intelligent Informatics Publication principles and has been retracted.The article contained redundant material, the editor investigated and found that the paper published in International Journal of Grid and Distributed Computing, Vol. 9, No. 11, pp. 61-70, 2016, DOI: http://dx.doi.org/10.14257/ijgdc.2016.9.11.05.The document and its content has been removed from International Journal of Advances in Intelligent Informatics, and reasonable effort should be made to remove all references to this article.

scholarly journals Dynamic Task Scheduling Algorithm with Deadline Constraint in Heterogeneous Volunteer Computing Platforms

2019 ◽  
Vol 11 (6) ◽  
pp. 121 ◽  
Author(s):  
Ling Xu ◽  
Jianzhong Qiao ◽  
Shukuan Lin ◽  
Wanting Zhang
Keyword(s):  
Distributed Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Volunteer Computing ◽  
Scheduling Problem ◽  
Dynamic Task ◽  
Use Of Resources ◽  
Task Scheduling Algorithm ◽  
Deadline Constraint ◽  
Dynamic Task Scheduling

Volunteer computing (VC) is a distributed computing paradigm, which provides unlimited computing resources in the form of donated idle resources for many large-scale scientific computing applications. Task scheduling is one of the most challenging problems in VC. Although, dynamic scheduling problem with deadline constraint has been extensively studied in prior studies in the heterogeneous system, such as cloud computing and clusters, these algorithms can’t be fully applied to VC. This is because volunteer nodes can get offline whenever they want without taking any responsibility, which is different from other distributed computing. For this situation, this paper proposes a dynamic task scheduling algorithm for heterogeneous VC with deadline constraint, called deadline preference dispatch scheduling (DPDS). The DPDS algorithm selects tasks with the nearest deadline each time and assigns them to volunteer nodes (VN), which solves the dynamic task scheduling problem with deadline constraint. To make full use of resources and maximize the number of completed tasks before the deadline constraint, on the basis of the DPDS algorithm, improved dispatch constraint scheduling (IDCS) is further proposed. To verify our algorithms, we conducted experiments, and the results show that the proposed algorithms can effectively solve the dynamic task assignment problem with deadline constraint in VC.

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