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.

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.

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 Customer-Oriented Task Scheduling for Heterogeneous Multi-Cloud Environment

2016 ◽  
Vol 6 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Sohan Kumar Pande ◽  
Sanjaya Kumar Panda ◽  
Satyabrata Das
Keyword(s):  
Customer Satisfaction ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Computing Systems ◽  
Customer Expectation ◽  
Task Scheduling Algorithm ◽  
Np Complete ◽  
Simulation Results ◽  
Multi Cloud

Task scheduling is widely studied in various environments such as cluster, grid and cloud computing systems. Moreover, it is NP-Complete as the optimization criteria is to minimize the overall processing time of all the tasks (i.e., makespan). However, minimization of makespan does not equate to customer satisfaction. In this paper, the authors propose a customer-oriented task scheduling algorithm for heterogeneous multi-cloud environment. The basic idea of this algorithm is to assign a suitable task for each cloud which takes minimum execution time. Then it balances the makespan by inserting as much as tasks into the idle slots of each cloud. As a result, the customers will get better services in minimum time. They simulate the proposed algorithm in a virtualized environment and compare the simulation results with a well-known algorithm, called cloud min-min scheduling. The results show the superiority of the proposed algorithm in terms of customer satisfaction and surplus customer expectation. The authors validate the results using two statistical techniques, namely T-test and ANOVA.

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