| Advances in network and middleware technologies have brought computing with many widely-distributed and heterogeneous resources to the forefront. The large distributed platforms of Grid Computing and Internet Computing allow scientists to solve problems at unprecedented scales and/or at greatly reduced cost. Efficiently managing the computation is a difficult and challenging problem, given the heterogeneous attributes of the underlying components. An added complexity is that resources in these environments exhibit dynamically changing performance.; In this dissertation we consider independent task applications , which decompose into a large number of uniform independent tasks, where all application data initially resides in a single repository, and the time required to transfer that data is a significant factor. We support the premise that dynamic, autonomous scheduling of independent task applications can achieve maximum steady-state throughput and low overhead in networks of non-dedicated heterogeneous computers. We present two autonomous bandwidth-centric scheduling protocols that answer some practical challenges of attaining the optimal steady-state execution rate.; We also consider the potential decrease in computation rate of a host due to concurrent data transfers to and from that host. Our experiments show that the computation rate can be reduced by over 50%, and that the reduction is roughly proportional to the communication transfer rate. We present the Communication-Interference processor model that incorporates the interference rate of communication on computation. We show how to determine an optimal steady-state allocation for a fork graph (tree of height 1) in this new model.; Finally, we compare the performance of various autonomous scheduling algorithms on an example testbed of near and distant heterogeneous processors. Our aggressive Communication-Interference-Centric algorithm performed the best overall. |
