In this paper, we explore the tradeoffs and opportunities in porting a high-throughput Grid computing middleware to a high-performance service oriented environment. We present the limitations of the Grid computing middleware when operating in such a performance sensitive environment and suggest ways of overcoming these limitations. We focus on exploiting the computation and communication heterogeneity of the Grid resources to meet the performance requirements of services, and present several approaches of work distribution that deal with this heterogeneity. We also present a heuristic for finding the best decomposition of work and present algorithms for each of the approaches which we evaluate on a PlanetLab testbed. The results validate the heuristic and indicate that a significant improvement in performance can be achieved by making the Grid computing middleware aware of the heterogeneity in the underlying infrastructure. The results also provide some useful insights into selecting a work distribution policy based on the dynamic status of the Grid computing environment.