Opnet Technologies, Inc.

Riverbed Technology, Inc.

Bobby Ninan

Gordon Bolt

Alain J Cohen

Yevgeny Gurevich

Edward A Sykes

Scott Glasser

Olivier Goldschmidt

Traffic flow between each pair of nodes in a network may be modeled based on loads measured at each link and based on gravity measures associated with each node. Gravity measures correspond to a relative likelihood of the node being a source or a sink of traffic. Gravity objectives are assigned to nodes to serve as an objective for a node's performance. These gravity objectives may be based on qualitative characteristics associated with each node. Because the assigned gravity objectives may be subjective, the gravity measures are used to generate a quantitative function for determining whether a network can achieve these gravity objectives. In one embodiment, link loads are allocated to traffic flows between nodes and current gravity measures are determined. Changes to link loads and traffic flows may then be modeled to minimize a difference between the assigned gravity measures and the gravity measures.

Traffic flow inference based on link loads and gravity measures

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.

A metric tuning technique optimizes the maximum link utilization of a set of links incrementally. Changes to the metric are constrained to be metric increases to divert routes from select links, thereby minimizing the number of changes required to achieve the optimization by avoiding the potential cascade of changes caused by attracting routes to a link. An interactive user interface is provided to allow a user to specify limits and constraints, and to select the sets of links to be addressed, including, for example, only the links that exceed a given link utilization threshold, the links having the highest link utilizations, the links having the highest failure effect, and so on. This incremental optimization technique is also used to optimize network resiliency by minimizing the network degradation caused by the failure of one or more links.

Tuning routing metrics to reduce maximum link utilization or provide failure resiliency

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.

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Raleigh, NC, United States of America

Bobby Ninan