Providing high performance distributed computing through scalable computation servers

Distributed applications spanning multiple nodes should he capable of providing fast response. Computation servers equipped with powerful processors and large memories can improve performance by better utilising system resources such as offloading overloaded nodes. A computation service may comprise all the nodes of the system (in case all are equipped with the required resources) or a small subset such as provided by a pool of computation servers (as in Amoeba). In both cases there can be a large number of service providers (compute servers). A server selection service is required to choose the most suitable server to provide the service. This paper is concerned with the design of adaptive computation server selection with scale recognised as a primary design and implementation factor. Adaptive system partitioning into domains is advocated as a key design principle for scalability. The model is demonstrated with compute-server selection using adaptive partitioning compared to the same service using random selection and probing