Two new biasing load balancing algorithms in distributed systems

In this paper two new methods for load balancing in distributed systems are proposed. Both methods are based on hierarchical structure. These methods have two major advantages of static and dynamic methods: the simplicity of static methods and adaptiveness of dynamic methods. They use current state information of nodes for the decision making in workloads allocations on the excising nodes in distributed systems. The first method determines specific weights, called biases, on groups and nodes, based on the state information of the system. The second method called Minimum Load State Round Robin (MLSRR), uses state information of the system to improve common round robin method. Comparative study of these methods shows better performance than existing conventional algorithms.