No abstract

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.