A Trace-Driven Simulation Study of Dynamic Load Balancing
IEEE Transactions on Software Engineering
Automated learning of load-balancing strategies for a distributed computer system
Automated learning of load-balancing strategies for a distributed computer system
Parallel image processing applications on a network of workstations
Parallel Computing
Rendering large scenes using parallel ray tracing
Parallel Computing - Special issue on applications: parallel graphics and visualisation
Information Processing Letters
Enhanced GridSim architecture with load balancing
The Journal of Supercomputing
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One of the main obstacles in obtaining high performance from heterogeneous distributed computing (HDC) system is the inevitable communication overhead. This occurs when tasks executing on different computing nodes exchange data or the assigned sub-task size is very small. In this paper, we present adaptive pre-task assignment (APA) strategy for heterogeneous distributed raytracing system. In this strategy, the master assigns pre-task to the each node. The size of sub-task for each node is proportional to the node's performance. One of the main features of this strategy is that it reduces the inter-processes communication, the cost overhead of the node's idle time and load imbalance, which normally occurs in traditional runtime task scheduling (RTS) strategies. Performances of the RTS and APA strategies are evaluated on manager/master and workers model of HDC system. The experimental results of our proposed (APA) strategy have shown a significant improvement in the performance over RTS strategy.