Systems and methods that adaptively model network traffic to predict network capacity utilization and quality of experience into the future. The adaptive model of network traffic may be used to recommend capacity upgrades based on a score expressed in a QoE space.

Methods and systems may be associated with application integration in a multi-cloud computing environment. A modeling platform may determine a pattern composition for an integration process associated with a business application. A cost-aware platform may transform a decomposed version of the pattern composition, including a plurality of integration pattern contract graphs, into a multi-cloud cost problem optimization representation. An initial solution to the cost problem representation may be calculated using a modified first fit decreasing bin packing or similar algorithm to determine a configuration of multi-cloud containers and integration pattern contract graph container assignments. The system may then iteratively and locally improve the solution to by applying feasible transformations to create variant container configurations and integration pattern contract graph container assignments that are evaluated based on multi-cloud costs (keeping the user in-the-loop). When a termination criteria is satisfied, an indication of the improved solution may be output.

An Internet of Things device is herein disclosed. The Internet of Things device comprises a communications module having circuitry to communicatively connect to a computer network, a memory operable to store data, a processor coupled to the memory and the communications module and operable to execute instructions stored in the memory, and an activity module, including at least one of a sensor and a control device. The activity module operates under control of the processor to perform a designated activity with at least one of the sensor and the control device. The activity module further communicates on the computer network via the communications module. The processor curtails a volume of communication of the communications module on the computer network if a measured value of a system parameter exceeds a threshold value.

Aspects of the subject technology relate to systems and methods for optimizing telemetry schemes with a constant insensible group delay. Systems and methods are provided for receiving an acquisition including compressed data and dummy data from a downhole logging system, determining a prior decompressor queue size based on the acquisition for a plurality of time intervals, determining a decompressor data size based on the acquisition for the plurality of time intervals, and determining a delay where the prior decompressor queue size is continuously greater than or equal to the decompressor data size for the plurality of time intervals.

Wireless communications and/or systems (e.g., 100) and/or methods (e.g., 200, 300, 400) may be provided for predicting of potential undetected flows in a DPI system using a machine learning (ML) model. The system may include an input packet module which may be configured for verifying packet parameters from a network traffic flow, and a processor which can be configured for processing the extracted parameters to identify whether the network traffic flow is potentially detectable or undetectable using a trained machine learning (ML) model based on at least the extracted parameters and perform DPI processing for the detectable flows. Thus, the system may provide an optimized DPI flow processing for high rate traffic networks with decreasing processing time.

Systems, methods, and computer-readable media for receiving one or more models of network intents, comprising a plurality of contracts between providers and consumers, each contract containing entries with priority values. Each contract is flattened into a listing of rules and a new priority value is calculated. The listing of rules encodes the implementation of the contract between the providers and the consumers. Each entry is iterated over and added to a listing of entries if it is not already present. For each rule, the one or more entries associated with the contract from which the rule was flattened are identified, and for each given entry a flat rule comprising the combination of the rule and the entry is generated, wherein a flattened priority is calculated based at least in part on the priority value of the given one of given entry and the priority value of the rule.

Systems, methods, and computer-readable media for receiving an indication of an equivalence failure, the equivalence failure corresponding to one or more models of network intents. The indication of the equivalence failure is analyzed and one or more constituent intents that caused the equivalence failure are identified, wherein the one or more constituent intents are associated with a model of the one or more models of network intents. The granularity of the equivalence failure and the identified one or more constituent intents is determined, and an event for external consumption is generated, the event based at least in part on the equivalence failure, the granularity of the equivalence failure, and the identified one or more constituent intents.

Techniques are described for monitoring application performance in a computer network. For example, a network management system (NMS) includes a memory storing path data received from a plurality of network devices, the path data reported by each network device of the plurality of network devices for one or more logical paths of a physical interface from the given network device over a wide area network (WAN). Additionally, the NMS may include processing circuitry in communication with the memory and configured to: determine, based on the path data, one or more application health assessments for one or more applications, wherein the one or more application health assessments are associated with one or more application time periods for a site, and in response to determining at least one failure state, output a notification including identification of a root cause of the at least one failure state.

The present invention provides a graph embedding-based virtual network mapping method including inputting a virtual network and a substrate network corresponding to a virtual network mapping scenario, in an optimized mapping mode, generating an embedding value for every substrate node by applying a graph convolution network (GCN), by a network encoder, to embed the virtual network, determining whether a difference between an embedding value for every substrate node and an embedding value for every previous substrate node which is previously embedded exceeds a set threshold, by a network decoder, and mapping an allocation node of the virtual network to a mapping node of a previous substrate network which is previously mapped according to an embedding value for every previous substrate node when the difference value does not exceed the threshold value, by the network decoder.

In one embodiment, a network assurance service that monitors a wireless network identifies a set of wireless network anomalies detected in the wireless network that are associated with a set of one or more network measurements. The network assurance service classifies the set of wireless anomalies as radio-related or backend-related. The network assurance service, when the set of wireless anomalies are classified as radio-related, determines that the wireless anomalies are recurring for a particular wireless access point in the network. The network assurance service initiates a change to the wireless network in part to move clients in the wireless network from the particular wireless access point to another wireless access point in the network.