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Reverse Shortest Path for Dataflow Optimization of Hierarchically Structure Data Networks

By Prawat Chaiprapa, Kajornasak Kantapanit, Prawat Chaiprapa and Kajornsak Kantapanit


Abstract: In the routing process to select the data paths for Hierarchically Aggregation/Disaggregation and Composition/Decomposition,(HAD) networks, a fast algorithm for finding optimum paths for dataflow is needed. In this research we propose an algorithm called the Reverse Shortest Path algorithm to improve the speed in the calculating procedure for finding the shortest paths. This algorithm performs the reversed calculation in stead of the forward calculation used in conventional algorithms. The demand in each original destination pair (OD pair) has been distributed to the sub OD pairs in each relevant subnetwork r (u,v) = r (u,l) =... = r (l,k) = r (k,v) with l and k, the gateways and ancestors in the active path. For each different commodities, the parallel processing is carried out with the shared shortest path processing time of O(log(n)) which less than O(m log(n)) of HAD algorithm[1] where, n is the number of nodes in the networks, M is the number of commodities in each cluster and m is a positive integer which is less than M. The proposed algorithms have been developed and tested on a simulated network of 200 nodes clustered into 20 groups. Each group uses a personal computer as the processor for the group. Ten data Monte Carlo simulation patterns were generated for the test. The first five patterns represent typical normal dataflows which largely consist of short distance communications. The other five patterns represent the worst case data communication scenario. Test results on the proposed Reverse Shortest Path algorithm show that, for the tested network, the algorithm has improves the speed in finding the shortest paths by 20 % as compared to the conventional shortest path algorithm. Key–Words: Optimal routing,distributed computation, gradient projection method, hierarchically structure network

Year: 2012
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