Abstract: Systems and methods for estimating a random distribution for an overall metric for a composite node, the composite node comprising a plurality of nodes. For each data atom of a plurality of data atoms being input to the composite node, and for each node of the plurality of nodes, at least one value may be generated for a per-node metric with respect to the data atom. A value for the overall metric with respect to the data atom may be generated based on the per-node metric values of the plurality of nodes. At least one parameter of the random distribution for the overall metric for the composite node may be estimated based on the overall metric values with respect to the plurality of data atoms.

Abstract: Systems and methods for estimating a random distribution for an overall metric for a composite node, the composite node comprising a plurality of nodes. For each data atom of a plurality of data atoms being input to the composite node, and for each node of the plurality of nodes, at least one value may be generated for a per-node metric with respect to the data atom. A value for the overall metric with respect to the data atom may be generated based on the per-node metric values of the plurality of nodes. At least one parameter of the random distribution for the overall metric for the composite node may be estimated based on the overall metric values with respect to the plurality of data atoms.

Abstract: Systems and methods for estimating a random distribution for an overall metric for a composite node, the composite node comprising a plurality of nodes. For each data atom of a plurality of data atoms being input to the composite node, and for each node of the plurality of nodes, at least one value may be generated for a per-node metric with respect to the data atom. A value for the overall metric with respect to the data atom may be generated based on the per-node metric values of the plurality of nodes. At least one parameter of the random distribution for the overall metric for the composite node may be estimated based on the overall metric values with respect to the plurality of data atoms.

Abstract: A network traffic shaper adjusts the rate of data flowing from a packet source to a packet sink responsive to priority assigned to the packets pursuant to their ability to match a series of filters. Each packet is applied to a queue depending on its classification. The packets are read from each queue in accordance with a queue reading policy. Singular spectrum analysis of the traffic load produces a set of vectors that capture the spectral bases of the traffic state. These vectors are associated with the bandwidth demanded by the traffic, a relationship that is recorded in a queue/time/eigenvector/bandwidth/control table. Samples of the current network load are compared with previously recorded vector bases to reveal how similar the current traffic state is to previous traffic states, which in turn predicts the traffic's bandwidth needs for the immediate future. The queue reading regime is updated in response to this prediction.