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With the rapid development of computer simulation technology, the Radar simulation applications scale up increasingly. More and more Radar simulation applications adopt distributed structure to improve system performance and availability. Hence, how to enhance the robustness and efficiency of these complex distributed simulation systems is a hot point. We should balance the load for the applications to enhance the resource' s utility and increase the throughput. To overcome the problem, one effective way is to make use of load balancing. At the same time, load balancing middleware provides better scalability, response time and throughput. However, we must pay attention to the fact that the computing of the load should be adaptive and predicative to avoid the affection of the peak load. To the complex simulation applications, the peak means the system may suffer extremely high load for a short period while keeping stable load for a long time and some hosts of the system may be overloaded and the response time may be decreased for this kind of fluctuate. Therefore, to utilize the services effectively especially when the workloads fluctuate frequently, we should make the system react to the load fluctuate gradually and predictably. So we have proposed and implemented machine learning based load prediction and fuzzy logic based replica management for adaptive and flexible load balancing mechanism within the framework of distributed middleware.