On Load Balancing Approaches for Distributed Object Computing Systems

Distributed object computing systems are widely envisioned to be the desired distributed software development paradigm in the near future due to the higher modularity and the capability of handling machine and operating system heterogeneity. Indeed, enabled by the tremendous advancements in processor and networking technologies, complex operations such as object serialization and data marshalling become very efficient, and thus, distributed object systems are being built for many different applications. As the system scales up (e.g., with larger number of server and client objects, and more machines), a judicious load balancing system is required to efficiently distribute the workload (e.g., the queries, messages/objects passing) among different servers in the system. Several such load balancing schemes are proposed recently in the literature. However, while the rationales and mechanisms employed are dramatically different, the relative strengths and weaknesses of these approaches are unknown, making it difficult for a practitioner to choose an appropriate approach for the problem at hand. In this paper, we describe in detail three representative approaches, which are all practicable, and present a quantitative comparison using a real experimental distributed object computing platform. Among these three approaches, namely, JavaSpaces based, request redirection based, and fuzzy decision based, we find that the fuzzy decision-based algorithm outperforms the other two considerably under a wide range of different practical scenarios.