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|Title:||Design and performance evaluation of load distribution strategies for multiple divisible loads on heterogeneous linear daisy chain networks|
|Citation:||Min, W.H., Veeravalli, B., Barlas, G. (2005-12). Design and performance evaluation of load distribution strategies for multiple divisible loads on heterogeneous linear daisy chain networks. Journal of Parallel and Distributed Computing 65 (12) : 1558-1577. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jpdc.2005.05.016|
|Abstract:||In this paper, we consider the problem of scheduling multiple divisible loads on heterogeneous linear daisy chain networks. Our objective is to design a load distribution strategy such that the total processing time of a set of loads is minimized. We assume that the set of loads are resident in one of the farthest end processors, which has a scheduler that will distribute the load to the other processors in the network. When distributing a load from the set, the distribution pattern of the previous load has to be taken into consideration to ensure that no processors are left idle and there are no collisions in the communication links. We design single and multi-installments strategies to achieve the above objective. We derive certain important conditions to determine whether an optimum solution exists. We propose two heuristic strategies when an optimum solution is unattainable. Using all the above strategies, we conduct four different simulation experiments to track the performance of strategies under several real-life situations. We conducted four different simulation experiments based on the two heuristic strategies to identify the best combination suitable for our multiple-loads distribution strategy. We also run simulations for a homogeneous system to quantify the performance under 3 different policies, that is, when the loads are (a) unsorted, (b) sorted with smallest load first (SLF) and (c) sorted with largest load first (LLF). A detailed analysis of the simulation results is presented and based on these, recommendations are made for the choice of strategies. Finally, we compare the performance of a single-load distribution strategy against the multiple-loads distribution strategy designed in this paper to quantify the exact performance gain that can be achieved. Illustrative examples are also provided for ease of understanding. © 2005 Elsevier Inc. All rights reserved.|
|Source Title:||Journal of Parallel and Distributed Computing|
|Appears in Collections:||Staff Publications|
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