Spawn: A Distributed Computational Economy
IEEE Transactions on Software Engineering
Issues in the Design of Adaptive Middleware Load Balancing
OM '01 Proceedings of the 2001 ACM SIGPLAN workshop on Optimization of middleware and distributed systems
Adaptive Location Policies for Global Scheduling
IEEE Transactions on Software Engineering
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A modular QoS-enabled load management framework for component-based middleware
OOPSLA '03 Companion of the 18th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
A new fuzzy-decision based load balancing system for distributed object computing
Journal of Parallel and Distributed Computing
Resource Allocation in the Grid Using Reinforcement Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Dynamic Task Scheduling using Genetic Algorithms for Heterogeneous Distributed Computing
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
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Distributed object computing is widely envisioned to be the desired distributed software development paradigm due to the higher modularity and the capability of handling machine and operating system heterogeneity. In this paper, we address the issue of judicious load balancing in distributed object computing systems. In order to decrease response time and to utilize services effectively, we have proposed and implemented a new technique based on machine learning for adaptive and flexible load balancing mechanism within the framework of distributed middleware. We have chosen Jini 2.0 to build our experimental middleware platform, on which our proposed approach as well as other related techniques are implemented and compared. Extensive experiments are conducted to investigate the effectiveness of the proposed technique, which is found to be consistently better in comparison with existing techniques.