Verizon Patent and Licensing Inc.

Worldcom, Inc.

Verizon Business Global LLC

Nandagopal Venugopal

Thomas P Newcomer

Kristen L Watkins

Agustin Pecorari

Aruna Apte

A method and system for providing a computing environment that permits simulations directed towards the consolidation and decommissioning of network elements associated with a network. The computing environment permits a user to designate a network element for decommission and exclude a network element from decommission and/or consolidation. The computing environment includes consolidation and decommission analytics that minimize a total operational cost of the network including leased network elements. The consolidation and decommission analytics includes accounting for penalties associated with early termination of leases and break-even values.

Network resource consolidation and decommissioning analysis

A device receives network configuration information from a network, determines a new capacity model of the network based on the network configuration information, and constructs a mixed integer programming (MIP) model based on the new capacity model. The device also calculates an optimal baseline solution, that minimizes network costs, using the mixed integer programming (MIP) model, performs a collocation optimization procedure on the optimal baseline solution to produce an optimal collocation solutions for the network, and provides the optimal network collocation solutions to the network for implementation.

Determining collocations with an access transport management system (ATMS)

A device receives network configuration information from a network, determines a logical network based on the network configuration information, and constructs a mixed integer programming (MIP) model based on the logical network. The device also calculates an optimal solution, which minimizes network costs, using the mixed integer programming (MIP) model, performs a post-optimization mapping procedure on the optimal solution to produce an optimal network configuration mapping, and outputs the optimal network configuration mapping for implementation.

Mixed integer programming model for minimizing leased access network costs

A system and method are provided for designing ring cover candidates for a network. A ring cover candidate is generated based on configuration information and traffic demand information associated with the network. The ring cover candidate includes a group of rings, where each ring includes a group of network spans. In some implementations consistent with the principles of the invention, a report including characteristics of a ring cover candidate may be generated.

Apparatus and method for designing ring covers

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. The recursive optimization process takes place for each of the demand sets provided in the demand input structure in accordance with their time points. Preferably, Priority 1 demands are optimized first. Thereafter, Priority 2 demands are optimized by employing a capacitated shortest path algorithm with respect to each Priority 2 demand presented in its order.

System and method for staggering time points for deployment of rings in a fiber optic network simulation plan

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.

System and method for time slot assignment in a fiber optic network simulation plan

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. The recursive optimization process takes place for each of the demand sets provided in the demand input structure in accordance with their time points. Preferably, Priority demands are optimized first. Thereafter, Priority demands are optimized by employing a capacitated shortest path algorithm with respect to each Priority demand presented in its order.

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. When the next MUX level of the demand is taken up for optimization, the filtered network model and associated cost function are recursively updated by using the solution set obtained for the previous MUX level. The recursive optimization process takes place for each time point, covering all MUX levels at that time point, as provided in the demand input structure. Preferably, Priority demands are optimized first. Thereafter, Priority demands are optimized by employing a capacitated shortest path algorithm with respect to each Priority demand presented in its order.

Growing community of inventors

Plano, TX, United States of America

Nandagopal Venugopal