Randomized Algorithms for Mapping Clustered Object-Oriented Software onto Distributed Architectures

Abstract

Distributed Object Oriented (DOO) applications have been developed for solving complex problems in various scientific fields. One of the most important aspects of the DOO systems is the efficient distribution of software classes among different nodes in order to solve the mismatch problem that may appear when the software structure does not match up the available hardware organization. We have proposed a multistep approach for restructuring DOO software. According to this approach, the OO system is partitioned into clusters that are then merged into larger groups forming what we call Merged Cluster Graph. The last step in this approach is concerned by mapping these merged clusters onto the target distributed architecture. Generally, the mapping problem is intractable thus allowing only for efficient heuristics. This paper presents two algorithms to solve the mapping problem using a randomized approach. The proposed algorithms has proved to be efficient, Simple and easy to understand and implement. Furthermore, the performance of the proposed algorithms was tested against some existing deterministic techniques. The experimental results showed an outstanding performance of the proposed algorithms in minimizing the overall mapping cost of the produced assignments.