Abstract: Problem statement: To examine the strategies for scheduling of independent file-sharing tasks in a heterogeneous environment and the concept of load balancing. Approach: We propose hypergraph partitioning based strategy for the scheduling of non-critical jobs. This is done by scheduling the tasks that share tasks among them to the same processor. The tasks thus scheduled are employed to a load balancing scheme for balancing the load on the processors by considering the average load on all processors. Results: This strategy reduces the input output overheads among the tasks thus reducing the end-point contention. Conclusion: Thus the batch execution time on the processors is reduced
This paper addresses the problem of load balancing data-parallel computations on heterogeneous and t...
The scheduling of independent but file-sharing tasks on heterogeneous master-slave platforms has rec...
Fine-grained parallel applications require all their processes to run simultaneously on distinct pro...
(eng) This paper is devoted to scheduling a large collection of independent tasks onto heterogeneous...
This paper is devoted to scheduling a large collection of independent tasks onto heterogeneous clust...
This paper is devoted to scheduling a large collection of independent tasks onto heterogeneous clust...
AbstractExecution of a logic program can be sped up by load sharing among a group of interconnected ...
This paper proposes a novel, hypergraph partitioning based strategy to schedule multiple data analys...
We consider the problem of scheduling an application on a computing system consisting of heterogeneo...
(eng) This paper is devoted to scheduling a large collection of independent tasks onto a large distr...
(eng) This paper is devoted to scheduling a large collection of independent tasks onto a large distr...
Abstract. This paper is devoted to scheduling a large collection of independent tasks onto a distrib...
Abstract-Heterogeneous systems become popular in both client and cloud. A parallel program can incur...
(eng) Scheduling computational tasks on processors is a key issue for high-performance computing. Al...
Importance of distributed systems for distributing the workload on the processors is globally accept...
This paper addresses the problem of load balancing data-parallel computations on heterogeneous and t...
The scheduling of independent but file-sharing tasks on heterogeneous master-slave platforms has rec...
Fine-grained parallel applications require all their processes to run simultaneously on distinct pro...
(eng) This paper is devoted to scheduling a large collection of independent tasks onto heterogeneous...
This paper is devoted to scheduling a large collection of independent tasks onto heterogeneous clust...
This paper is devoted to scheduling a large collection of independent tasks onto heterogeneous clust...
AbstractExecution of a logic program can be sped up by load sharing among a group of interconnected ...
This paper proposes a novel, hypergraph partitioning based strategy to schedule multiple data analys...
We consider the problem of scheduling an application on a computing system consisting of heterogeneo...
(eng) This paper is devoted to scheduling a large collection of independent tasks onto a large distr...
(eng) This paper is devoted to scheduling a large collection of independent tasks onto a large distr...
Abstract. This paper is devoted to scheduling a large collection of independent tasks onto a distrib...
Abstract-Heterogeneous systems become popular in both client and cloud. A parallel program can incur...
(eng) Scheduling computational tasks on processors is a key issue for high-performance computing. Al...
Importance of distributed systems for distributing the workload on the processors is globally accept...
This paper addresses the problem of load balancing data-parallel computations on heterogeneous and t...
The scheduling of independent but file-sharing tasks on heterogeneous master-slave platforms has rec...
Fine-grained parallel applications require all their processes to run simultaneously on distinct pro...