Abstract: A system and method for optimizing placement of network equipment and information load in a network over a period of time. A demand input structure having a plurality of demands organized by their time points is provided as an input to a model generator and an optimization processor associated therewith. Starting with the earliest demand set to be serviced by the network, a directed graph network model is obtained by using appropriate transformation techniques. A cost function associated with the network model is constructed using a flow cost term and an equipment cost term. Appropriate constraints are imposed on the cost function for optimization. A solution set comprising network placement information and demand routing information is obtained for a current time point. When the next demand set is taken up for optimization, the network model and associated cost function are recursively updated by using the solution set obtained for the demand set at a prior time point.

Abstract: A system and method for providing Time Slot Assignment (TSA)-compatible routes that optimize demand transport in a network with optimal placement of network equipment. A demand input structure having a plurality of demands organized by their time points and MUX levels is provided as an input to a model generator and an optimization processor associated therewith. After recursively optimizing the network for each MUX level/time point combination, demand routes are analyzed to verify whether they are TSA-compatible. Where demands with TSA-blocked routes are found, blocking spans are identified and a cost associated therewith is increased during an iterative re-routing process with respect to each of such blocked demands. Accordingly, alternate spans are discovered that may allow TSA transport for the blocked demands. The iterative re-routing process is effectuated by using a capacitated shortest path algorithm, and may be bounded by a limit on the number of iterations or a timeout period.

Abstract: A system and method for optimizing placement of network equipment and information load in a network over a period of time. A demand input structure having a plurality of demands organized by their time points is provided as an input to a model generator and an optimization processor associated therewith. Starting with the earliest demand set to be serviced by the network, a directed graph network model is obtained by using appropriate transformation techniques. A cost function associated with the network model is constructed using a flow cost term and an equipment cost term. Appropriate constraints are imposed on the cost function for optimization. A solution set comprising network placement information and demand routing information is obtained for a current time point. When the next demand set is taken up for optimization, the network model and associated cost function are recursively updated by using the solution set obtained for the demand set at a prior time point.

Abstract: A system and method for optimizing placement of network equipment and information load in a network over a period of time. A demand input structure having a plurality of demands organized by their time points is provided as an input to a model generator and an optimization processor associated therewith. Starting with the earliest demand set to be serviced by the network, a directed graph network model is obtained by using appropriate transformation techniques. A cost function associated with the network model is constructed using a flow cost term and an equipment cost term. Appropriate constraints are imposed on the cost function for optimization. A solution set comprising network placement information and demand routing information is obtained for a current time point. When the next demand set is taken up for optimization, the network model and associated cost function are recursively updated by using the solution set obtained for the demand set at a prior time point.

Abstract: A system and method for optimizing placement of network equipment and information load in a network over a period of time. A demand input structure having a plurality of demands organized by their time points and MUX levels is provided as an input to a model generator and an optimization processor associated therewith. Starting with the earliest demand with the highest MUX level to be serviced by the network, a directed graph network model is obtained by using appropriate transformation techniques. A MUX modularity constraint is imposed in order to obtain a filtered network model that can support a MUX level of a selected demand at a particular time point. A cost function associated with the filtered network model is constructed using a flow cost term and an equipment cost term. Appropriate constraints are imposed on the cost function for optimization. A solution set comprising network placement information and demand routing information is obtained for a MUX level at a current time point.