Abstract: A system and method for dynamically (re)configuring a service network based on profile information obtained from a Big Data Analytics platform. Received dynamic situation profiles relative to network states, subscriber states, etc. may be compared against corresponding characteristic situation profiles. If there is a similarity, a dynamic design change action may be effectuated for changing configuration of at least a part of the service network, e.g., a service chaining mechanism, operating to service user data flows of the subscribers.

Abstract: A system and method for dynamically (re)configuring a service network based on profile information obtained from a Big Data Analytics platform. Received dynamic situation profiles relative to network states, subscriber states, etc. may be compared against corresponding characteristic situation profiles. If there is a similarity, a dynamic design change action may be effectuated for changing configuration of at least a part of the service network, e.g., a service chaining mechanism, operating to service user data flows of the subscribers.

Abstract: A system and method for dynamically (re)configuring a service network based on profile information obtained from a Big Data Analytics platform. Received dynamic situation profiles relative to network states, subscriber states, etc. may be compared against corresponding characteristic situation profiles. If there is a similarity, a dynamic design change action may be effectuated for changing configuration of at least a part of the service network, e.g., a service chaining mechanism, operating to service user data flows of the subscribers.

Abstract: A system and method for dynamically (re)configuring a service network based on profile information obtained from a Big Data Analytics platform. Received dynamic situation profiles relative to network states, subscriber states, etc. may be compared against corresponding characteristic situation profiles. If there is a similarity, a dynamic design change action may be effectuated for changing configuration of at least a part of the service network, e.g., a service chaining mechanism, operating to service user data flows of the subscribers.

Abstract: A computer implemented fault diagnosis method employing both probabilistic models and statistical learning that diagnoses faults using probabilities and time windows learned during the actual operation of a system being monitored. In a preferred embodiment, the method maintains for each possible root cause fault an a-priori probability that the fault will appear in a time window of specified length as well as maintaining—for each possible resulting symptom(s)—probabilities that the symptom(s) will appear in a time window containing the fault and probabilities that the alarm will not appear in a time window containing the fault. Consequently, the method according to the present invention may advantageously determine—at any time—the probability that a fault has occurred, and report faults which are sufficiently likely to have occurred. These probabilities are updated based upon past time windows in which we have determined fault(s) and their cause(s).

Abstract: Avoiding vehicle collisions using an in-vehicle device is provided. The method comprises receiving a connection request from an in-vehicle device within a moving vehicle when the moving vehicle is within a selected distance from a traffic intersection, establishing connection with the in-vehicle device within a moving vehicle via a serving base station, receiving position information from the in-vehicle device within a moving vehicle corresponding to a position of the moving vehicle, receiving control information for a traffic light at the traffic intersection from a traffic management center and relaying the control information for the traffic light to the in-vehicle device within a moving vehicle. A warning is issued based upon a vehicle speed, a location and a position relative to a traffic intersection. A warning is also issued based upon multiple vehicles speed, relative locations to each other and each position relative to a traffic intersection.

Abstract: A computer implemented fault diagnosis method employing both probabilistic models and statistical learning that diagnoses faults using probabilities and time windows learned during the actual operation of a system being monitored. In a preferred embodiment, the method maintains for each possible root cause fault an a-priori probability that the fault will appear in a time window of specified length as well as maintaining—for each possible resulting symptom(s)—probabilities that the symptom(s) will appear in a time window containing the fault and probabilities that the alarm will not appear in a time window containing the fault. Consequently, the method according to the present invention may advantageously determine—at any time—the probability that a fault has occurred, and report faults which are sufficiently likely to have occurred. These probabilities are updated based upon past time windows in which we have determined fault(s) and their cause(s).

Abstract: A policy change management scheme for network resource management through dynamic policy adaptations especially suitable for DiffServ-enabled MPLS networks is presented. The scheme incorporates automated resource adaptation capabilities to assure QoS for user traffic and to promote resource utilization in DiffServ-enabled MPLS networks. A suite of resource management policies, an ordered set of methods for adjusting policies, and interfaces to a companion policy-based network management system are provided. The policies are periodically adjusted based on predictive bandwidth estimation algorithms ensuring optimal resource allocation to individual service classes and enabling adjustment of resources for handling current traffic and traffic expected for the near future.

Abstract: A policy change management scheme for network resource management through dynamic policy adaptations especially suitable for DiffServ-enabled MPLS networks is presented. The scheme incorporates automated resource adaptation capabilities to assure QoS for user traffic and to promote resource utilization in DiffServ-enabled MPLS networks. A suite of resource management policies, an ordered set of methods for adjusting policies, and interfaces to a companion policy-based network management system are provided. The policies are periodically adjusted based on predictive bandwidth estimation algorithms ensuring optimal resource allocation to individual service classes and enabling adjustment of resources for handling current traffic and traffic expected for the near future.