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.

A Novel Task Scheduling Algorithm in Heterogeneous Cloud Environment Using Equi-Depth Binning Method

2019 ◽  
pp. 115-127
Author(s):  
Roshni Pradhan ◽  
Amiya Kumar Dash
Keyword(s):  
Task Scheduling ◽  
Large Scale ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Np Complete ◽  
Heterogeneous Cloud ◽  
Heterogeneous Computing Systems ◽  
Modern Tool

Cloud computing is modern tool for large-scale distributed computing and parallel processing. It has become a growing technology to deliver highly scalable service to the user. Task scheduling is one of the essential strategies to expeditiously utilize the potential of heterogeneous computing systems. In heterogeneous framework mapping, a task to a machine is a NP complete problem. This issue can be comprehended just utilizing heuristic approach. There are various heuristic approaches that were proposed to deal with scheduling of independent tasks. Different scheduling measures can be utilized for measuring the potency of scheduling algorithms. The most essential of them are makespan, flow-time, and overall resource utilization. Cloud generally is a single machine or combination of machines. Applications in the form of set of tasks are processed by the cloud.

A Novel Task Scheduling Algorithm in Heterogeneous Cloud Environment Using Equi-Depth Binning Method

2021 ◽  
pp. 1303-1316
Author(s):  
Roshni Pradhan ◽  
Amiya Kumar Dash
Keyword(s):  
Task Scheduling ◽  
Large Scale ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Np Complete ◽  
Heterogeneous Cloud ◽  
Heterogeneous Computing Systems ◽  
Modern Tool

Cloud computing is modern tool for large-scale distributed computing and parallel processing. It has become a growing technology to deliver highly scalable service to the user. Task scheduling is one of the essential strategies to expeditiously utilize the potential of heterogeneous computing systems. In heterogeneous framework mapping, a task to a machine is a NP complete problem. This issue can be comprehended just utilizing heuristic approach. There are various heuristic approaches that were proposed to deal with scheduling of independent tasks. Different scheduling measures can be utilized for measuring the potency of scheduling algorithms. The most essential of them are makespan, flow-time, and overall resource utilization. Cloud generally is a single machine or combination of machines. Applications in the form of set of tasks are processed by the cloud.

Prediction-Based Independent Task Scheduling for Heterogeneous Distributed Computing Systems

2012 ◽  
Vol 457-458 ◽  
pp. 1039-1046 ◽  
Author(s):  
You Wei Lu ◽  
Zhen Zhen Xu ◽  
Feng Xia
Keyword(s):  
Distributed Computing ◽  
Task Scheduling ◽  
Execution Time ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Distributed Computing Systems ◽  
Computing Systems ◽  
Independent Task ◽  
Balance Task ◽  
Heterogeneous Computing Systems

Independent task scheduling algorithms in distributed computing systems deal with three main conflicting factors including load balance, task execution time and scheduling cost. In this paper, the problem of scheduling tasks arriving at a low rate and with long execution time in heterogeneous computing systems is studied, and a new scheduling algorithm based on prediction is proposed. This algorithm evaluates the utility of task scheduling based on statistics and prediction to solve the influence of heterogeneous computing systems. The experimental results reveal that the proposed algorithm adequately balances the conflicting factors, and thus performs better than some classical algorithms such as MCT and MET when the parameters are well selected.

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