Techniques for automatically interpreting metric values to evaluate the health of a computer-based service

Number: 11095728
Published: 2021-08-17
Filed: 2018-06-21
Assignee: Disney Enterprises, Inc.
Inventors: Morrison; Vincent
CPC: G06F7/50; G06N20/00; G06N3/045; G06N3/0455; G06N3/08; G06N3/09; G06N5/01; H04L41/5009; H04L67/51
Verdict: Set aside IT service health monitoring, ops
Source: Google Patents · FreePatentsOnline

Abstract

In various embodiments, a health evaluation application automatically monitors and evaluates the health of one or more computer-based services. The health evaluation application computes deviation values based on one or more machine-learned expected variations associated with multiple metrics. The metrics are associated with the computer-based service(s). The health evaluation application then performs classification operation(s) based on the deviation values and machine-learned classification criteria to compute anomaly indicators associated with a first service included in the one or more computer-based services. Subsequently, the health evaluation application computes a score that indicates the overall health of the first service based on the anomaly indicators. Advantageously, because the health evaluation application automatically computes the score, the time required to monitor and evaluate the health of the service is reduced compared to the time required to manually monitor and evaluate the health of the service.

Background

BACKGROUND Field of the Various Embodiments (1) Embodiments of the present invention relate generally to performance evaluation technology for monitoring and controlling the allocation of resources in a computer-based service. Description of the Related Art (2) Ensuring that a service is available and performing as intended or “healthy” is an important aspect of providing an effective service. As referred to herein, a “service” executes on one or more devices capable of executing instructions to perform a function for any number of users. A service is typically distributed via a network architecture. To evaluate the health of a service, oftentimes a service provider monitors a variety of metrics associated with the service and, based on those metrics, attempts to determine whether the service is healthy. If, at any point in time, the service provider determines that the performance of the service is subpar, then the service provider can take measures to restore the health of the service. For example, a web service provider could monitor requests per second, memory utilization, disk space, etc. to evaluate the performance of the web service. If the web service provider were to determine that the performance of the web service was subpar based on the metrics, then the web service provider could adjust the amount of resources (e.g., increase the memory) allocated to the web service in an attempt to resolve the performance issues and restore the “health” of the service. (3) In on

Claims

1. A computer-implemented method, comprising: computing a first plurality of deviation values based on one or more machine-learned expected variations associated with a plurality of metrics, wherein the plurality of metrics is associated with a first service, and the one or more machine-learned expected variations are derived from one or more models trained via one or more machine learning operations; performing one or more classification operations based on the first plurality of deviation values and a first set of machine-learned classification criteria to compute a first plurality of anomaly indicators associated with the first service; and computing a first score that indicates the overall health of the first service based on the first plurality of anomaly indicators. || 14. One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: computing a first plurality of deviation values based on one or more machine-learned expected variations associated with a plurality of metrics, wherein the plurality of metrics is associated with a first service, and the one or more machine-learned expected variations are derived from one or more models trained via one or more machine learning operations; performing one or more classification operations based on the first plurality of deviation values and a first set of machine-learned classification criteria to compute a first plurality of anomaly indicators associated with the first service; and computing a first score that indicates the overall health of the first service based on the first plurality of anomaly indicators. || 23. 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 first plurality of deviation values based on one or more machine-learned expected variations associated with a plurality of metrics, wherein the plurality of metrics is associated with a first service, and the one or more machine-learned expected variations are derived from one or more models trained via one or more machine learning operations; perform one or more classification operations based on the first plurality of deviation values and a first set of machine-learned classification criteria to compute a first plurality of anomaly indicators associated with the first service; and compute a first score that indicates the overall health of the first service based on the first plurality of anomaly indicators.