Automatic Middleware Deployment Planning on Heterogeneous Platforms

The use of many distributed, heterogeneous resources as a large collective platform offers great potential. A key issue for these grid platforms is middleware scalability and how middleware services can be mapped on the available resources. Optimizing deployment is a difficult problem with no existing general solutions. In this paper, we address the following problem: how to perform out an adapted deployment for a hierarchy of servers and resource brokers on a heterogeneous system? Our objective is to generate a best platform from the available nodes so as to fulfill the clients demands. However, finding the best deployment among heterogeneous resources is a hard problem since it is close to find the best broadcast tree in a general graph, which is known to be NP-complete. Thus, in this paper, we present a heuristic for middleware deployment on heterogeneous resources. We apply our heuristic to automatically deploy a distributed problem solving environment on a large scale grid. We present experiments comparing the automatically generated deployment against a number of other reasonable deployments.