  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 location which is a calculated function of network
characteristics, processing cycles and data location and size. The job
scheduler provides a global ranking of the computing resources and then selects
an optimal one on the basis of this overall access and execution cost. The
DIANA approach considers the Grid as a combination of active network elements
and takes network characteristics as a first class criterion in the scheduling
decision matrix along with computation and data. The scheduler can then make
informed decisions by taking into account the changing state of the network,
locality and size of the data and the pool of available processing cycles.
