Abstract: Systems and methods for managing network operations are described. An illustrative system includes a plurality of network components, in which each network component generates a plurality of network component data, and a mediation node that receives the plurality of network component data. The mediation node associates a plurality of time series data and a network topology with the network component data. The mediation node includes at least one baseline module that receives a selected subset of the network component data and generates at least one baseline for anomaly detection. The system may further include at least one application component communicatively coupled to the mediation node, and the application component may receive the time series data, the plurality of subsets of the network component data, and a plurality of baselines and identify a relationship.

Abstract: Systems and methods for managing network operations are described. An illustrative system includes a plurality of network components, in which each network component generates a plurality of network component data, and a mediation node that receives the plurality of network component data. The mediation node associates a plurality of time series data and a network topology with the network component data. The mediation node includes at least one baseline module that receives a selected subset of the network component data and generates at least one baseline for anomaly detection. The system may further include at least one application component communicatively coupled to the mediation node, and the application component may receive the time series data, the plurality of subsets of the network component data, and a plurality of baselines and identify a relationship.

Abstract: This disclosure is directed to techniques for inferring traffic information or estimating total volume of traffic/data flowing through a target network/entity, wherein only a partial subset of inferred traffic information or volume of data is available to a predictor entity/network that infers such traffic information. In an embodiment, such partial subset of total traffic can either be made available to the entity/network for inferring and estimating total traffic or such partial data can actually flow through the entity/network.

Abstract: To improve network reliability and management in today's high-speed communication networks, we propose an intelligent system using adaptive statistical approaches. The system learns the normal behavior of the network. Deviations from the norm are detected and the information is combined in the probabilistic framework of a Bayesian network. The proposed system is thereby able to detect unknown or unseen faults. As demonstrated on real network data, this method can detect abnormal behavior before a fault actually occurs, giving the network management system (human or automated) the ability to avoid a potentially serious problem.

Abstract: This disclosure is directed to techniques for inferring traffic information or estimating total volume of traffic/data flowing through a target network/entity, wherein only a partial subset of inferred traffic information or volume of data is available to a predictor entity/network that infers such traffic information. In an embodiment, such partial subset of total traffic can either be made available to the entity/network for inferring and estimating total traffic or such partial data can actually flow through the entity/network.

Abstract: The present disclosure is directed to techniques for efficient streaming SVD computation. In an embodiment, streaming SVD can be applied for streamed data and/or for streamed processing of data. In another embodiment, the streamed data can include time series data, data in motion, and data at rest, wherein the data at rest can include data from a database or a file and read in an ordered manner. More particularly, the disclosure is directed to an efficient and faster method of computation of streaming SVD for data sets such that errors including reconstruction error and loss of orthogonality are error bounded. The method avoids SVD re-computation of already computed data sets and ensures updates to the SVD model by incorporating only the changes introduced by the new entrant data sets.

Abstract: To improve network reliability and management in today's high-speed communication networks, we propose an intelligent system using adaptive statistical approaches. The system learns the normal behavior of the network. Deviations from the norm are detected and the information is combined in the probabilistic framework of a Bayesian network. The proposed system is thereby able to detect unknown or unseen faults. As demonstrated on real network data, this method can detect abnormal behavior before a fault actually occurs, giving the network management system (human or automated) the ability to avoid a potentially serious problem.