Bobby Ninan - Raleigh NC, US Gordon M. Bolt - Apex NC, US Edward Sykes - Cary NC, US Scott Glasser - Chapel Hill NC, US Alain Cohen - Washington DC, US Yevgeny Gurevich - Washington DC, US
Assignee:
OPNET Technologies, Inc. - Bethesda MD
International Classification:
G06F 15/173
US Classification:
709224, 709223, 709225, 709226
Abstract:
Traffic flow between each pair of nodes in a network are determined based on loads measured at each link and based on gravity measures associated with each node. The gravity measures correspond to a relative likelihood of the node being a source or a sink of traffic, and may be assigned based on ‘soft’ characteristics associated with each node, such as the demographics of the region in which the node is located, prior sinking and sourcing statistics, and so on. Because the assigned gravities are relatively subjective, the gravity measures are used to generate an objective function for solving a system of linear equations, rather than as criteria that must be satisfied in the solution. The measured link loads are allocated among the traffic flows between nodes to at least a given allocation efficiency criteria by solving a system of linear equations with an objective of minimizing a difference between the assigned gravities and the resultant gravities corresponding to the determined flows.
Traffic Flow Inference Based On Link Loads And Gravity Measures
Bobby Ninan - Raleigh NC, US Gordon M. Bolt - Apex NC, US Edward A. Sykes - Cary NC, US Scott Glasser - Chapel Hill NC, US Alain J. Cohen - McLean VA, US Yevgeny Gurevich - Washington DC, US
Assignee:
OPNET Technologies, Inc. - Bethesda MD
International Classification:
G06F 15/173
US Classification:
709224, 709223, 709225, 709226
Abstract:
Traffic flow between each pair of nodes in a network are determined based on loads measured at each link and based on gravity measures. The gravity measures correspond to a likelihood of the node being a source or a sink of traffic and may be assigned based on characteristics associated with each node, such as the demographics of the region in which the node is located, prior sinking and sourcing statistics, and so on. The gravity measures are used to generate an objective function for solving a system of linear equations, rather than as criteria that must be satisfied in the solution. The measured link loads are allocated among the traffic flows between nodes to at least a given allocation efficiency criteria by solving a system of linear equations with an objective of minimizing a difference between the assigned gravities and the resultant gravities corresponding to the determined flows.
Alain Cohen - Washington DC, US Jerome Plun - Alexandria VA, US Yevgeny Gurevich - Washington DC, US
International Classification:
H04Q007/20
US Classification:
455423000
Abstract:
A simulation system includes a cache structure that stores determined characteristics related to the propagation of an event. If a similar event occurs, and the factors affecting the determination of these characteristics have not changed, the characteristics associated with the new event are retrieved from the cache, rather than being re-determined from the underlying factors. In the example of mobile transceivers, if multiple transceivers share a common set of underlying factors, the determined characteristics of one transceiver can be shared among all of the other transceivers. If the underlying factors are dependent upon geographic area, mobile transceivers that enter a geographic area having associated cached characteristics can use the cached characteristics of other transceivers that are, or had been, in the area. Because the cache process is structured to intercept a call to the routine that determines the characteristics, the modifications to a simulation system to incorporate this caching feature should be minimal.
Visualizing A Complex Network Based On A Set Of Objects Of Interest
Robert Mollitor - Rockville MD, US Yevgeny Gurevich - Washington DC, US Vivek Narayanan - Colorado Springs CO, US Patrick Malloy - Washington DC, US
International Classification:
G06F 9/00
US Classification:
715764000, 715835000, 715810000, 715800000
Abstract:
A user interface of a device for displaying a representation of a network of objects wherein a plurality of selectable objects are rendered, and each of the plurality of selectable objects are rendered having a given positioning relative to each other of the plurality of selectable objects. One or more edges are rendered interconnecting the plurality of selectable objects within a first region of the user interface. A second region is rendered in response to one or more of the selectable objects of the first region being selected. Objects corresponding to the selected objects are depicted in the second region while maintaining the given relative positioning. A number of additional objects may be retrieved from a database that indicates objects that are adjoining the corresponding objects. The adjoining objects are positioned in the second region and are interconnected to the corresponding objects by one or more additional edges.
Yigong Liu - Herndon VA, US Yevgeny Gurevich - Washington DC, US Pradeep Singh - Arlington VA, US Alain Cohen - Washington DC, US Vivek Narayanan - Rohnert Park CA, US Russell Elsner - Bethesda MD, US Clinton Hart - Frederick MD, US
International Classification:
H04L 12/28
US Classification:
370254000
Abstract:
A new network topology visualization is produced from an existing network topology using grouping algorithms that are selectively applied to selectable hierarchies of the existing network topological hierarchy. The new network topology is rendered for review. Interaction may be provided through a graphical user interface (GUI). The new network topology is provided as a previewing visualization. The previewing visualization is provided in a tree-view. The previewing visualization provides a visual differentiation between portions of the network topology that are changed by the grouping algorithm and portions that are not changed. The grouping algorithms may be provided as plug-in algorithms. A further new network topology may be rendered in response to receipt of a changed network topology.
Organizing, Displaying, And/Or Manipulating Network Traffic Data
Alain J. Cohen - McLean VA, US David Manowitz - Washington DC, US Yevgeny Gurevich - Washington DC, US Edward A. Sykes - Cary NC, US Shobana Narayanaswamy - Kensington MD, US
International Classification:
H04L 12/26 G06F 3/048
US Classification:
370241, 715808
Abstract:
A system for analyzing network activity by displaying in a first portion of a display, network objects according to an activity characteristic, receiving a selection of one or more of the network objects, and displaying in a second portion of the display, a further activity characteristic of the selected network objects according to at least a first criteria. The display of the network objects may be altered according to second criteria that may include filtering criteria that removes network objects displayed in the first portion. The results of the altered display may be displayed in a third portion of the display providing further details of the selected network objects. The selection of the network object may be a selection of a plurality of objects and the displayed activity characteristic may be a sum of the selected network objects activity characteristics.
Edward Sykes - Cary NC, US Shobana Narayanaswamy - Kensington MD, US Alain Cohen - Washington DC, US Pradeep Singh - Arlington VA, US Vinod Jeyachandran - Rockville MD, US Vivek Narayanan - Rohnert Park CA, US Yevgeny Gurevich - Washington DC, US Michael Brauwerman - Seattle WA, US
International Classification:
H04L 12/56
US Classification:
370389000
Abstract:
Devices and methods for modeling and analysis of services provided over a common network include a processor configured to track services connected to the common network through nodes and links; run service models associated with the services under selected conditions, the selected conditions including failure and repair of one of the nodes or links; and propose corrective action and/or change of network resources of the common network to minimize impact of the failure. The processor may also run Network model(s). The models may be executed successively or simultaneously, and outputs of one model may be used as input to other models, including any necessary conversions for compatibility.
Edward A. Sykes - Cary NC, US Shobana Narayanaswamy - Kensington MD, US Alain J. Cohen - Washington DC, US Pradeep K. Singh - Arlington VA, US Vinod Jeyachandran - Rockville MD, US Vivek Narayanan - Rohnert Park CA, US Yevgeny Gurevich - Washington DC, US Michael Brauwerman - Seattle WA, US
International Classification:
G06F 11/07 G06F 11/00 G06F 15/173
US Classification:
714 4, 709224, 714E11024
Abstract:
Devices and methods for modeling and analysis of services provided over a common network include a processor configured to track services connected to the common network through nodes and links; run service models associated with the services under selected conditions, the selected conditions including failure and repair of one of the nodes or links; and propose corrective action and/or change of network resources of the common network to minimize impact of the failure. The processor may also run Network models. The models may be executed successively or simultaneously, and outputs of one model may be used as input to other models, including any necessary conversions for compatibility.
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