TECHNIQUES FOR ANALYZING A NETWORK AND INCREASING NETWORK AVAILABILITY

Number: 20200177468
Published: 2020-06-04
Filed: 2018-11-30
Assignee: DISNEY ENTERPRISES, INC.
Inventors: QUACHTRAN; Benjamin, MCLEIN; Ian Conrad, HARE; Daniel Ryan, SANCHEZ; Nina Zalah, KOKONYAN; Sona
CPC: G06N20/00; G06N3/042; G06N3/044; G06N3/0442; G06N3/088; G06N3/09; H04L41/147; H04L41/16
Verdict: Set aside network analysis/availability, IT infra
Source: Google Patents · FreePatentsOnline

Abstract

In various embodiments, a prediction subsystem automatically predicts a level of network availability of a device network. The prediction subsystem computes a set of predicted attribute values for a set of devices attributes associated with the device network based on a trained recurrent neural network (RNN) and set(s) of past attribute values for the set of device attributes. The prediction subsystem then performs classification operation(s) based on the set of predicted attribute values and one or more machine-learned classification criteria. The result of the classification operation(s) is a network availability data point that predicts a level of network availability of the device network. Preemtive action(s) are subsequently performed on the device network based on the network availability data point. By performing the preemptive action(s), the amount of time during which network availability is below a given level can be substantially reduced compared to prior art, reactive approaches.

Background

BACKGROUNDField of the Various Embodiments

Embodiments of the present invention relate generally to networking technology and, more specifically, to techniques for analyzing a network to increase network availability.Description of the Related Art

Ensuring that network devices are available to users is an important aspect of providing effective network-based services. Typically, if the operation of one or more devices included in a device network is deficient, then the overall availability of the device network can be compromised, and some users may be unable to access or use the services provided by the device network. To maintain appropriate levels of network availability, service providers oftentimes implement monitoring tools that automatically monitor a variety of attributes related to the operation of different devices included in a device network. If, at any point in time, a monitoring tool receives an attribute value that indicates that the operation of a particular network device is deficient, then the monitoring tool generates an alert. When an engineer notices the alert, the engineer is able to troubleshoot the device and perform remediation operations on the device and/or device network to restore network availability to the appropriate level.

One drawback of the “reactive” approach to network analysis and remediation described above is that the operation of at least one network device needs to be deficient before a monitoring tool generates an a

Claims

1. A computer-implemented method, comprising: computing a set of predicted attribute values for a set of device attributes associated with a device network based on a trained recurrent neural network (RNN) and at least one set of past attribute values for the set of device attributes; and performing one or more classification operations based on the set of predicted attribute values and one or more machine-learned classification criteria to generate a network availability data point that predicts a first level of availability of the device network, wherein at least one preemptive action is subsequently performed on the device network based on the network availability data point. 10. One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: computing a set of predicted attribute values for a set of device attributes associated with a device network based on a trained recurrent neural network (RNN) and at least one set of past attribute values for the set of device attributes; and performing one or more classification operations based on the set of predicted attribute values and one or more machine-learned classification criteria to generate a network availability data point that predicts a first level of availability of the device network, wherein at least one preemptive action is subsequently performed on the device network based on the network availability data point. 19. A system, comprising: one or more memories storing instructions; and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to: compute a set of predicted attribute values for a set of device attributes associated with a device network based on a trained recurrent neural network (RNN) and at least one set of past attribute values for the set of device attributes; and perform one or more classification operations based on the set of predicted attribute values and one or more machine-learned classification criteria to generate a network availability data point that predicts a first level of availability of the device network, wherein at least one preemptive action is subsequently performed on the device network based on the network availability data point.