Control and management of large and dynamic networks
Control and management of large and dynamic networks
Internetworking with TCP/IP: principles, protocols, and architecture
Internetworking with TCP/IP: principles, protocols, and architecture
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
A Fast Distributed Shortest Path Algorithm for a Class of Hierarchically Clustered Data Networks
IEEE Transactions on Computers
Routing algorithms in communication networks
Routing algorithms in communication networks
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Exact Convergence of a Parallel Textured Algorithm for Data Network Optimal Routing Problems
IEEE Transactions on Parallel and Distributed Systems
A fast distributed optimal routing algorithm for multicommodity large data networks
IPPS '95 Proceedings of the 9th International Symposium on Parallel Processing
Dynamic Programming
A New Parallel and Distributed Shortest Path Algorithm for Hierarchically Clustered Data Networks
IEEE Transactions on Parallel and Distributed Systems
Reverse shortest path for dataflow optimization of hierarchically structure data networks
WSEAS TRANSACTIONS on COMMUNICATIONS
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In this paper, a new algorithm based on hierarchical aggregation/disaggregation and decomposition/composition (HAD) scheme is proposed to solve the optimal routing problems (ORP) for hierarchically structured networks of multi-layer backbones. Our algorithm has two major differences with the existing HAD algorithms for hierarchically clustered networks [1], [2]: 1) our algorithm works with more general networks than the networks with the clustered structure; 2) our algorithm parallelizes the computations for different commodities (message flows defined by a pair of origin node and destination node) so that it speeds up with a parallel time complexity of O(mlog2(n)), which is much less than O(Mlog2(n)) needed for the existing HAD algorithms. Here, n is the number of nodes in the network; M is the number of commodities and m is a positive number usually much smaller than M and is a function of the patterns of all the commodities including the locations of all origin nodes and destination nodes, and the flow demand of each commodity. Furthermore, our algorithm can make a trade-off between the run time and the optimality, i.e., by allowing the solution to be sub-optimal, our algorithm can save great amount of computation time. The implementation of the algorithm for a 200-node network is simulated using OPNET simulation package (OPNET or Optimized Network Engineering Tools is developed by MIL3, Inc.), and the test results are consistent with our analysis.