US 12,587,460 B2Granted

Health model for cloud service health monitoring

AssigneeMicrosoft Technology Licensing, LLC

InventorsZhangwei Xu, John Bronn Socha-Leialoha, Rajive Kumar, Yusuf Tinwala, Yingnong Dang

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Abstract

The techniques described herein automatically correlate the health of cloud resources to a broader health determination for an entity executing within, or supported by, a distributed computing environment. In contrast to the typical manual analysis that is required to make a broader health determination for a specific entity, the techniques generate and use a standard health model that can be applied, or scaled, to detect unhealthy scenarios across a variety of different entities with different owners (e.g., different tenants and/or different cloud resource providers). Furthermore, to meet varying owner perspectives on health, the techniques include a layer on top of the standard health model that enables an owner to provide input that customizes the standard health model for their own entity.

Claims16

Dependent claims

A method comprising:

generating a directed graph health model that defines dependencies between nodes within a distributed computing environment, wherein:
the nodes include lower-level nodes representing lower-level entities;
the nodes include higher-level nodes representing higher-level entities;
and
edges that connect respective pairs of nodes, the edges representing dependencies between the respective pairs of nodes;
for an individual lower-level entity of the lower-level entities:
monitoring, by the directed graph health model, values of a plurality of metrics that are collected in association with use of the individual lower-level entity;
and
categorizing, by the directed graph health model, the individual lower-level entity as one of healthy or unhealthy by applying an anomaly detection algorithm to the values of the plurality of metrics;
determining, by the directed graph health model, a number of lower-level entities have been categorized as unhealthy, wherein each lower-level entity in the number of lower-level entities is connected to a higher-level entity in the directed graph health model via a respective edge;
determining, by the directed graph health model, that the number of lower-level entities satisfies a threshold established to indicate that a state of the higher-level entity is unhealthy, wherein:
the threshold defines a percentage of a total number of lower-level entities connected to the higher-level entity in the directed graph health model;
and
the percentage and the total number of lower-level entities are specific to a type of lower-level entity;
in response to determining that the number of lower-level entities satisfies the threshold, accessing, by the directed graph health model, a rule associated with the higher-level entity, the rule defining an action to execute for the higher-level entity when the state of the higher-level entity is unhealthy;
and
executing, by the directed graph health model, the action for the higher-level entity.

14 more elements

A system comprising:

a processing system;
and
a computer readable storage medium storing instructions that, when executed by the processing system, cause the system to perform operations comprising:
generating a directed graph health model that defines dependencies between nodes within a distributed computing environment, wherein:
the nodes include lower-level nodes representing lower-level entities;
the nodes include higher-level nodes representing higher-level entities;
and
edges that connect respective pairs of nodes, the edges representing dependencies between the respective pairs of nodes;
for an individual lower-level entity of the lower-level entities:
monitoring, via the directed graph health model, values of a plurality of metrics that are collected in association with use of the individual lower-level entity;
and
categorizing, via the directed graph health model, the individual lower-level entity as one of healthy or unhealthy by applying an anomaly detection algorithm to the values of the plurality of metrics;
determining, via the directed graph health model, a number of lower-level entities have been categorized as unhealthy, wherein each lower-level entity in the number of lower-level entities is connected to a higher-level entity in the directed graph health model via a respective edge;
determining, via the directed graph health model, that the number of lower-level entities satisfies a threshold established to indicate that a state of the higher-level entity is unhealthy, wherein:
the threshold defines a percentage of a total number of lower-level entities connected to the higher-level entity in the directed graph health mode;
and
the threshold is determined by a machine learning model;
in response to determining that the number of lower-level entities satisfies the threshold, accessing, via the directed graph health model, a rule associated with the higher-level entity, the rule defining an action to execute for the higher-level entity when the state of the higher-level entity is unhealthy;
and
executing, via the directed graph health model, the action for the higher-level entity.

17 more elements

A method comprising:

generating a directed graph health model that defines dependencies between nodes within a distributed computing environment, wherein:
the nodes include lower-level nodes representing lower-level entities;
the nodes include higher-level nodes representing higher-level entities;
and
edges that connect respective pairs of nodes, the edges representing dependencies between the respective pairs of nodes;
for an individual lower-level entity of the lower-level entities:
monitoring, by the directed graph health model, values of a plurality of metrics that are collected in association with use of the individual lower-level entity;
and
categorizing, by the directed graph health model, the individual lower-level entity into one of a predefined set of health categories;
determining, by the directed graph health model, a number of lower-level entities have been categorized into a particular one of the predefined set of lower-level health categories, wherein each lower-level entity in the number of lower-level entities is connected to a higher-level entity in the directed graph health model via a respective edge;
determining, by the directed graph health model, that the number of lower-level entities satisfies a threshold established to indicate that a state of the higher-level entity has changed, wherein:
the threshold defines a percentage of a total number of lower-level entities connected to the higher-level entity in the directed graph health model;
and
the percentage and the total number of lower-level entities are specific to a type of lower-level entity;
in response to determining that the number of lower-level entities satisfies the threshold, accessing, by the directed graph health model, a rule associated with the higher-level entity, the rule defining an action to execute for the higher-level entity when the state of the higher-level entity changes;
and
executing, by the directed graph health model, the action for the higher-level entity.

14 more elements

Dependencies are inferred from the claim text.

Facts

Application: 18/642,029Filed 22 Apr 2024
Published: US 2025/0330403 A123 Oct 2025
Granted: 24 Mar 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification12 codes

H04L 43/0817
By checking availability: by checking functioning
H04L 41/0816
Characterised by the conditions triggering a change of settings: the condition being an adaptation, e.g. in response to network events
H04L 41/145
Network analysis or design: involving simulating, designing, planning or modelling of a network
H04L 43/045
Processing captured monitoring data, e.g. for logfile generation: for graphical visualisation of monitoring data