A Survey Paper on Task Scheduling Methods in Cluster Computing Environment for High Performance

2015 ◽  
Author(s):  
Harvinder Singh ◽  
Gurdev Singh
Keyword(s):  
Task Scheduling ◽  
High Performance ◽  
Cluster Computing ◽  
Computing Environment ◽  
Survey Paper

On Construction of a Diskless Cluster Computing Environment in a Computer Classroom

2012 ◽  
Vol 4 (4) ◽  
pp. 68-88
Author(s):  
Chao-Tung Yang ◽  
Wen-Feng Hsieh
Keyword(s):  
High Performance ◽  
Cluster Computing ◽  
Relevant Information ◽  
Computing Environment ◽  
Cluster Architecture ◽  
Computer Classroom ◽  
Computation Node ◽  
Cluster Environment ◽  
Computing Performance ◽  
Performance Computing

This paper’s objective is to implement and evaluate a high-performance computing environment by clustering idle PCs (personal computers) with diskless slave nodes on campuses to obtain the effectiveness of the largest computer potency. Two sets of Cluster platforms, BCCD and DRBL, are used to compare computing performance. It’s to prove that DRBL has better performance than BCCD in this experiment. Originally, DRBL was created to facilitate instructions for a Free Software Teaching platform. In order to achieve the purpose, DRBL is applied to the computer classroom with 32 PCs so to enable PCs to be switched manually or automatically among different OS (operating systems). The bioinformatics program, mpiBLAST, is executed smoothly in the Cluster architecture as well. From management’s view, the state of each Computation Node in Clusters is monitored by “Ganglia”, an existing Open Source. The authors gather the relevant information of CPU, Memory, and Network Load for each Computation Node in every network section. Through comparing aspects of performance, including performance of Swap and different network environment, they attempted to find out the best Cluster environment in a computer classroom at the school. Finally, HPL of HPCC is used to demonstrate cluster performance.

CQPSO scheduling algorithm for heterogeneous multi-core DAG task model

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740050 ◽  
Author(s):  
Wenzheng Zhai ◽  
Yue-Li Hu ◽  
Feng Ran
Keyword(s):  
Task Scheduling ◽  
High Performance ◽  
Directed Acyclic Graph ◽  
Scheduling Algorithm ◽  
Task Assignment ◽  
Distributed Environment ◽  
Computing Environment ◽  
Task Model ◽  
Scheduling Method ◽  
Earliest Finish Time

Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment.

A novel task scheduling scheme in a cloud computing environment using hybrid biogeography-based optimization

2018 ◽  
Vol 23 (21) ◽  
pp. 11035-11054 ◽  
Author(s):  
Zhao Tong ◽  
Hongjian Chen ◽  
Xiaomei Deng ◽  
Kenli Li ◽  
Keqin Li
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Scheduling Scheme
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