Research on Scheduling Strategy in Parallel Applications Based on a Hybrid Genetic Algorithm

Efficient scheduling of parallel applications in a dynamic environment reveals several challenges due to its high heterogeneity, dynamic behavior, and space shared utilization. In this paper, first we compared some typical scheduling strategies and pointed out their shortcomings, and then we proposed a new scheduling strategy based on an advanced genetic algorithm, finally we simulated the strategy with the aid of SimGrid toolkit and it was proved reasonable and efficient. It is an effective approach for tasks scheduling in parallel applications.

[1]  Sonia Sharama,et al.  Grid Computing , 2004, Lecture Notes in Computer Science.

[2]  Lingyun Yang,et al.  Conservative Scheduling: Using Predicted Variance to Improve Scheduling Decisions in Dynamic Environments , 2003, ACM/IEEE SC 2003 Conference (SC'03).

[3]  Xia Chen,et al.  A spatial path scheduling algorithm for EDGE architectures , 2006, ASPLOS XII.

[4]  Anthony T. Chronopoulos,et al.  A class of loop self-scheduling for heterogeneous clusters , 2001, Proceedings 42nd IEEE Symposium on Foundations of Computer Science.

[5]  Bharadwaj Veeravalli,et al.  Design and analysis of a dynamic scheduling strategy with resource estimation for large-scale grid systems , 2004, Fifth IEEE/ACM International Workshop on Grid Computing.

[6]  Binoy Ravindran,et al.  A utility accrual scheduling algorithm for real-time activities with mutual exclusion resource constraints , 2006, IEEE Transactions on Computers.

[7]  Henri Casanova,et al.  Simgrid: a toolkit for the simulation of application scheduling , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.