Grid computing collects geographically dispersed resources ranging from laptops to supercomputers to compute tasks requested by clients. Grid scheduling, i.e., assigning tasks to resources, is an NPhard problem, and thus, metaheuristic methods are employed to find the optimal solutions. In this paper, we propose a Chemical Reaction Optimization (CRO) algorithm for the grid scheduling problem. CRO is a population-based metaheuristics mimicking the interactions between molecules in a chemical reaction. We compare the CRO approach with four generally acknowledged metaheuristics, and show that CRO performs the best.
Transmission scheduling is a key design problem in wireless multi-hop networks. Many transmission scheduling algorithms have been proposed to maximize the spatial reuse and minimize the time division multiple access (TDMA) frame length. Most of the scheduling algorithms are topology-dependent. They are generally graph-based and depend on the exact network topology information. Thus, they cannot adapt well to the dynamic wireless environment. In contrast, topology-transparent TDMA scheduling algorithms do not need detailed topology information. However, these algorithms offer very low minimum throughput. The objective of this work is to propose an adaptive topology-transparent scheduling algorithm to offer better throughput performance. With our algorithm, each node finds a transmission schedule so as to reduce the transmission conflicts and adapt better to the changing network environment. The simulation results show that the performance of our algorithm is better than the existing topology-transparent algorithms.
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