The AppLeS parameter sweep template: user-level middleware for the grid
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Heuristics for Scheduling Parameter Sweep Applications in Grid Environments
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Matchmaking: Distributed Resource Management for High Throughput Computing
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
Decoupling Computation and Data Scheduling in Distributed Data-Intensive Applications
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
SRDS '98 Proceedings of the The 17th IEEE Symposium on Reliable Distributed Systems
QoS guided min-min heuristic for grid task scheduling
Journal of Computer Science and Technology - Grid computing
An efficient implementation of the Min-Min heuristic
Computers and Operations Research
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Task Scheduling is a critical design issue of distributed computing. The emerging Grid computing infrastructure consists of heterogeneous resources in widely distributed autonomous domains and makes task scheduling even more challenging. Grid considers both static, unmovable hardware and moveable, replicable data as computing resources. While intensive research has been done on task scheduling on hardware computing resources and on data replication protocols, how to incorporate data movement into task scheduling seamlessly is unrevealed. We consider data movement as a dimension of task scheduling. A dynamic data structure, Data Distance Table (DDT), is proposed to provide real-time data distribution and communication information. Based on DDT, a data-conscious task scheduling heuristics is introduced to minimize the data access delay. A simulated Grid environment is set up to test the efficiency of the newly proposed algorithm. Experimental results show that for data intensive tasks, the dynamic data-conscious scheduling outperforms the conventional Min-Min significantly.