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Balanced load distribution is especially important to attain optimal use of existing computational resources in distributed and parallel applications. In dynamic load balancing (DLB), surplus workload in nodes overwhelmed with work is transferred to relatively free nodes during run time. While in iterative DLB methods, the load reaches to its final execution node through several iteration steps, the execution node is selected directly in one step in direct methods. However, direct methods require immense system state information to perform selection. In this paper, we present two new hybrid dynamic load balancing (HLB) methods that take advantage of both direct and iterative methods. HLB aims to consume minimum possible system resources to balance common workload distributions by using an iterative method, the hydrodynamic approach (HA). Besides, HLB intends to solve the problems derived from exceptional instantaneous load rises by using a direct method which requires only little system knowledge. The excess workload is shared directly with some non-neighboring nodes which are selected randomly or from a fixed distribution list. The experimental results designate that the hybrid methods outrun other iterative methods in terms of performance and whole system utilization. |
