Scheduling in Data Intensive and Network Aware (DIANA) Grid Environments

In Grids scheduling decisions are often made on the basis of jobs being either data or computation intensive: in data intensive situations jobs may be pushed to the data and in computation intensive situations data may be pulled to the jobs. This kind of scheduling, in which there is no consideration of network characteristics, can lead to performance degradation in a Grid environment and may result in large processing queues and job execution delays due to site overloads. In this paper we describe a Data Intensive and Network Aware (DIANA) meta-scheduling approach, which takes into account data, processing power and network characteristics when making scheduling decisions across multiple sites. Through a practical implementation on a Grid testbed, we demonstrate that queue and execution times of data-intensive jobs can be significantly improved when we introduce our proposed DIANA scheduler. The basic scheduling decisions are dictated by a weighting factor for each potential target l...

• Type: Journal
• Year: 2007
• Where: CORR
• Authors: Richard McClatchey, Ashiq Anjum, Heinz Stockinger, Arshad Ali, Ian Willers, Michael Thomas
• Keywords: CORR 2007, Education, Intensive, Network Characteristics, Scheduling Decision