Abstract: An example method for discovering and grouping application endpoints in a network environment is provided and includes discovering endpoints communicating in a network environment, calculating affinity between the discovered endpoints, and grouping the endpoints into separate endpoint groups (EPGs) according to the calculated affinity, each EPG comprising a logical grouping of similar endpoints for applying common forwarding and policy logic according to logical application boundaries. In specific embodiments, the affinity includes a weighted average of network affinity, compute affinity and user specified affinity.

Abstract: An example method for discovering and grouping application endpoints in a network environment is provided and includes discovering endpoints communicating in a network environment, calculating affinity between the discovered endpoints, and grouping the endpoints into separate endpoint groups (EPGs) according to the calculated affinity, each EPG comprising a logical grouping of similar endpoints for applying common forwarding and policy logic according to logical application boundaries. In specific embodiments, the affinity includes a weighted average of network affinity, compute affinity and user specified affinity.

Abstract: An example method for discovering and grouping application endpoints in a network environment is provided and includes discovering endpoints communicating in a network environment, calculating affinity between the discovered endpoints, and grouping the endpoints into separate endpoint groups (EPGs) according to the calculated affinity, each EPG comprising a logical grouping of similar endpoints for applying common forwarding and policy logic according to logical application boundaries. In specific embodiments, the affinity includes a weighted average of network affinity, compute affinity and user specified affinity.

Abstract: An example method for discovering and grouping application endpoints in a network environment is provided and includes discovering endpoints communicating in a network environment, calculating affinity between the discovered endpoints, and grouping the endpoints into separate endpoint groups (EPGs) according to the calculated affinity, each EPG comprising a logical grouping of similar endpoints for applying common forwarding and policy logic according to logical application boundaries. In specific embodiments, the affinity includes a weighted average of network affinity, compute affinity and user specified affinity.

Abstract: An example method for discovering and grouping application endpoints in a network environment is provided and includes discovering endpoints communicating in a network environment, calculating affinity between the discovered endpoints, and grouping the endpoints into separate endpoint groups (EPGs) according to the calculated affinity, each EPG comprising a logical grouping of similar endpoints for applying common forwarding and policy logic according to logical application boundaries. In specific embodiments, the affinity includes a weighted average of network affinity, compute affinity and user specified affinity.

Abstract: An example method for discovering and grouping application endpoints in a network environment is provided and includes discovering endpoints communicating in a network environment, calculating affinity between the discovered endpoints, and grouping the endpoints into separate endpoint groups (EPGs) according to the calculated affinity, each EPG comprising a logical grouping of similar endpoints for applying common forwarding and policy logic according to logical application boundaries. In specific embodiments, the affinity includes a weighted average of network affinity, compute affinity and user specified affinity.

Abstract: An example method for discovering and grouping application endpoints in a network environment is provided and includes discovering endpoints communicating in a network environment, calculating affinity between the discovered endpoints, and grouping the endpoints into separate endpoint groups (EPGs) according to the calculated affinity, each EPG comprising a logical grouping of similar endpoints for applying common forwarding and policy logic according to logical application boundaries. In specific embodiments, the affinity includes a weighted average of network affinity, compute affinity and user specified affinity.

Abstract: An example method for discovering and grouping application endpoints in a network environment is provided and includes discovering endpoints communicating in a network environment, calculating affinity between the discovered endpoints, and grouping the endpoints into separate endpoint groups (EPGs) according to the calculated affinity, each EPG comprising a logical grouping of similar endpoints for applying common forwarding and policy logic according to logical application boundaries. In specific embodiments, the affinity includes a weighted average of network affinity, compute affinity and user specified affinity.

Abstract: An example method for discovering and grouping application endpoints in a network environment is provided and includes discovering endpoints communicating in a network environment, calculating affinity between the discovered endpoints, and grouping the endpoints into separate endpoint groups (EPGs) according to the calculated affinity, each EPG comprising a logical grouping of similar endpoints for applying common forwarding and policy logic according to logical application boundaries. In specific embodiments, the affinity includes a weighted average of network affinity, compute affinity and user specified affinity.

Abstract: An example method for discovering and grouping application endpoints in a network environment is provided and includes discovering endpoints communicating in a network environment, calculating affinity between the discovered endpoints, and grouping the endpoints into separate endpoint groups (EPGs) according to the calculated affinity, each EPG comprising a logical grouping of similar endpoints for applying common forwarding and policy logic according to logical application boundaries. In specific embodiments, the affinity includes a weighted average of network affinity, compute affinity and user specified affinity.

Abstract: Traffic in a radio communication system (FIG. 1) is adaptively balanced (300) to control congestion. A congestion model is maintained (302) with congestion dependencies including first and second types of congestion along with corresponding likelihood functions that indicate a likelihood that the first type of congestion will lead to the second type of congestion. An early warning of an impending congestion is detected (304) from an incoming traffic mix which exceeds an output traffic capability, given a current output allocation and configuration. The congestion model is accessed (306), in response to detecting the impending congestion, to identify and determine a priority for possible sources of the impending congestion. Action is then taken (308, 314, 318) to relieve the impending congestion by doing at least one of (a) increasing output resources and (b) decreasing traffic rates from the possible sources, prioritized in accordance with the congestion model.

Abstract: A communication system (100) includes a shared communication medium, such as a communication channel or a base station, for providing messages and transmission devices (110) for transmitting the messages over the shared communication medium during sharing periods defined by a sharing pattern (114). A trading center (105) coupled to the transmission devices (110) modifies the sharing pattern (114) in response to traffic handled by the transmission devices (110).

Abstract: Time sharing of a radio communication channel among a plurality of interfering cells (302) is provided. The radio communication channel transmits information in a plurality of time slots (502). The radio communication channel is partitioned (704) into a plurality of virtual channels (V1, V2). A virtual channel is used for communicating simultaneously with corresponding ones of the plurality of interfering cells which are assigned to the virtual channel. No more than one of the plurality of virtual channels operates during any one of the plurality of time slots, and the time sharing of the radio communication channel is optimized (708, 710) by activating selected ones of the plurality of virtual channels, based upon a traffic load applicable to the corresponding ones of the plurality of interfering cells.

Abstract: Delay in processing an interactive application for a portable subscriber unit (216) is reduced in a two-way messaging system (200) having a distributed architecture including a plurality of intelligent control elements (202, 204, 208, 212, 214, 216). A software messaging agent (210) is shipped (408) from a first one of the plurality of intelligent control elements to a second one of the plurality of intelligent control elements, the second one requiring less communication among the plurality of intelligent control elements to process the interactive application than required by the first one. Thereafter the software messaging agent operates (410) in the second one of the plurality of intelligent control elements for autonomously processing at least a portion of the interactive application, thereby reducing processing delay resulting from communication latency.