Abstract: In computing networks, path availabilities are estimated according to the present disclosure. The path availabilities may be calculated based on connectivity and performance measurements provided by multiple data sources, including passive, active, and/or route monitoring data sources. The measurements may be classified using network topology and processed to determine availability indicators corresponding to the measurements. The availability indicators may be aggregated to determine an overall path availability score for a path associated with the indicators.

Abstract: Technologies are provided for analyzing packet loss in network packet streams using windowed loss durations. A computing device can be configured to detect a first loss period in a network packet stream and a second, subsequent loss period in the network packet stream. The computing device can determine a number of packets received in the stream between the two loss periods, and can compare this number of packets to a specified recovery window length. If the number of packets received between the two loss periods is less than the recovery window length, then the computing device can treat the two loss periods, and the packets received between the two loss periods, as a single period of packet loss. The packet loss period can be treated as a single loss event for the purposes of analyzing network packet stream quality of experience and/or tuning packet loss compensation mechanisms.

Abstract: Packet transmission rate and packet drop rate for discrete network devices in a network are used to estimate end-to-end traffic demand and loss in the network. Data regarding the packet transmission rate and drop rate are passively collected for each network device and transmitted to a network monitoring unit. The network monitoring unit compiles the data and generates a series of simultaneous equations that represent traffic demand and loss between the discrete network devices along the paths connecting respective source-destination pairs. By determining an optimal solution to the simultaneous equations, an estimate of end-to-end traffic loss and corresponding traffic demand, which takes into account packet loss at each network device, can be generated for each source-destination pair. The optimal solution can be formed as a traffic matrix, which aggregates source-to-destination traffic demands, and a loss matrix, which aggregates source-to-destination traffic losses.

Abstract: Passive monitoring by network devices can be used to validate a network traffic matrix, which aggregates end-to-end traffic demands between source-destination pairs in the network. Using information regarding the physical and logical topology of the network during the same time period as the traffic matrix, a model of the network is generated. Traffic load on each link between network devices in the network is predicted using the model and the traffic matrix. Actual traffic loads on each link are determined from the passive monitoring data from each network device. By comparing the predicted load with the actual load on each link, a measure of the validity or accuracy of the traffic matrix is obtained. The disclosed techniques can also be applied to validate a network loss matrix.

Abstract: A method and system are described for automatically determining network utilization bounds in order to meet per-link packet loss rate targets through the network. A loss-verses-utilization model is continuously re-generated as part of a feedback loop to update link utilization targets. A target loss rate can be used to generate a link utilization target value using the model. The link utilization target value can be used in a variety of network update models, such as traffic engineering, capacity planning and risk management. Updates can be applied to individual network links, network devices as a whole, or groups of devices (based on type or position in the network).

Abstract: Cause and remediation data from one or more prior network incidents can be identified using a determined global similarity between these prior incidents and a current incident. Various network data can be collected, and data determined to potentially be relevant to a current incident can be selected or clustered into a single document or file. This document can have relevant features extracted, which can be used to generate a smaller numerical representation, or fingerprint, of the current incident. This fingerprint may be broken down into three categories of incident data, as may include general, topology, and alert categories. These categories of data can be compared for a current network incident and one or more prior incidents using their respective fingerprints, such as by using cosine similarity determinations. These values can be combined into global similarity values, which can be used to identify similar incidents as well as to provide visualization.

Abstract: In computing networks, end-to-end loss distributions are estimated according to the present disclosure. The loss distributions may be calculated based on loss values reported by nodes in the network along an end-to-end communication path, which may be identified by a route tracing process. The loss values for each node in the path for a given time window may be reduced by a rounding process, and the reduced values for adjacent nodes in the path may be iteratively multiplied with one another in a pairwise manner to generate an end-to-end loss value vector, where the resulting product from each iteration may be reduced by the rounding process. A graphical or other representation of the values in the end-to-end loss value vector indicating a distribution of losses experienced along the path may be output via a user interface.

Abstract: The numbers of packets transmitted and packets lost for network devices in a computer network are used to estimate a probability of end-to-end packet loss for traffic between a source-destination pair in the network. Metrics of packet transmission and loss at each network device interface are passively collected and transmitted to a network monitoring unit, which uses the metrics to calculate probability of successful packet transmission at each hop. For a particular end-to-end path between the source-destination pair, the network monitoring unit combines the probabilities of successful packet transmission for the hops along the path to yield a probability of successful end-to-end packet transmission. Probability of packet loss along the end-to-end path is determined based on the probability of successful end-to-end packet transmission.

Abstract: An optimization agent optimizes configuration parameters of a shared memory buffer for ports of a network device (e.g., router). The optimization agent located on the network device (or remotely) takes an input state from the network device's switching logic, such as the packets transmitted or dropped per port, and determines port buffer settings to optimize the settings to achieve a goal. The buffer settings can include minimum and maximum buffer limits per port and the goal can be to maximize port utilization or minimize port loss. The optimization agent can then take an action by applying the buffer configurations to the switching logic and repeat the process iteratively until the goal is reached.

Abstract: Aspects of the embodiments are directed to systems, methods, and computer program products to program, via a northbound interface, a mapping between an endpoint identifier (EID) and a routing locator (RLOC) directly into a mapping database at a mapping system; receive, from a first tunneling router associated with a first virtual network, a mapping request to a second virtual network, the first router compliant with a Locator/ID Separation Protocol, the mapping request comprising an EID tuple that includes a source identifier and a destination identifier; identify an RLOC based, at least in part, on the destination identifier of the EID tuple from the mapping database; and transmit the RLOC to the first tunneling router implementing an high level policy that has been dynamically resolved into a state of the mapping database.

Abstract: In an example, there is disclosed a computing apparatus, having: one or more logic elements, including at least a processor and a memory, providing a network simulation engine to: periodically perform a network traffic simulation; cache at least one network traffic simulation in a traffic state cache; receive a quest for additional network demand; and compute a network delta based at least in part on a difference between the request for additional network demand and the traffic state cache.

Abstract: In an example, there is disclosed a computing apparatus, having: one or more logic elements, including at least a processor and a memory, providing a network simulation engine to: periodically perform a network traffic simulation; cache at least one network traffic simulation in a traffic state cache; receive a quest for additional network demand; and compute a network delta based at least in part on a difference between the request for additional network demand and the traffic state cache.

Abstract: In an example, there is disclosed a computing apparatus, having: one or more logic elements, including at least a processor and a memory, providing a network simulation engine to: periodically perform a network traffic simulation; cache at least one network traffic simulation in a traffic state cache; receive a quest for additional network demand; and compute a network delta based at least in part on a difference between the request for additional network demand and the traffic state cache.

Abstract: Aspects of the embodiments are directed to systems, methods, and computer program products to program, via a northbound interface, a mapping between an endpoint identifier (EID) and a routing locator (RLOC) directly into a mapping database at a mapping system; receive, from a first tunneling router associated with a first virtual network, a mapping request to a second virtual network, the first router compliant with a Locator/ID Separation Protocol, the mapping request comprising an EID tuple that includes a source identifier and a destination identifier; identify an RLOC based, at least in part, on the destination identifier of the EID tuple from the mapping database; and transmit the RLOC to the first tunneling router implementing an high level policy that has been dynamically resolved into a state of the mapping database.

Abstract: A provider edge (PE) device provides subscribers with access to an Internet Service Provider (ISP) network. The subscribers are connected to the PE device via a broadband aggregation device. The PE device includes a processor operable to snoop control plane traffic for a logical identifier that allows subscriber traffic having varying data plane information to be treated as a session group. The processor is further operable to instantiate the session group based on the logical identifier. A unit of the PE device applies a QOS policy to traffic flow associated with the session group. It is emphasized that this abstract is provided to comply with the rules requiring an abstract that will allow a searcher or other reader to quickly ascertain the subject matter of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.