The Map-Reduce Parallelism Framework for Task Scheduling in Grid Computing

2011 ◽  
Vol 216 ◽  
pp. 111-115 ◽  
Author(s):  
Yun Xia Pei ◽  
Yue Zhang

As a rapid developing infrastructure, the grid can share widely distributed computing, storage, data and human resources. In order to improve the usability and QoS of the grid, the job management in the grid is very important, and becomes one of the key research issues in grid computing. Map-Reduce provide an efficient and easy-to-use framework for parallelizing the global optimization procedure. The simulation results show the usefulness and effectiveness of our task scheduling algorithm.

Author(s):  
Ge Weiqing ◽  
Cui Yanru

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.


2011 ◽  
Vol 50-51 ◽  
pp. 526-530 ◽  
Author(s):  
Xiao Bo Gao

From the perspective of resource sharing, grid computing is a system ranging from small kind of network system for home using to large-scale network computing systems even to the Internet. The management of resources in the grid environment becomes very complex as these resources are distributed geographically, heterogeneous in nature, and each having their own resource management policies and different access as well as cost models. In this paper, we bring forward an efficient resources management model and task scheduling algorithm in grid computing. The simulation results show that the proposed algorithm achieves resource load balancing, and can be applied to the optimization of task scheduling successfully.


2014 ◽  
Vol 577 ◽  
pp. 935-938
Author(s):  
Cheng Yu Cai ◽  
Yuan Sheng Lou

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.


Sign in / Sign up

Export Citation Format

Share Document