Partitioning and allocation of objects in heterogeneous distributed environments using the niched Pareto genetic-algorithm

As the importance of middleware-based distributed object computing environments (e.g. CORBA and DCOM) increases, there is considerable interest in incorporation of object-orientation (OO) and distributed systems. One important aspect of distributed object systems is effective distribution of software components, to achieve some performance goals, such as balancing the workloads, maximizing the degree of concurrency and minimizing the entire communication casts. Although there have been a lot of works on partitioning and allocation for distributed system, they are not directly applicable to OO system. We developed a partitioning and allocation model for mapping OO applications to heterogeneous distributed environments, and evaluated it using genetic algorithm (GA). Our model applies the graph-theoretic approach, dealing with a lot of characteristics of OO paradigm. The Niched Pareto GA is adopted to experiment our model because a partitioning and allocation problem is multiobjective problem with non-commensurable objectives