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US 11,057,284 B2Granted
Cognitive quality of service monitoring
AssigneeInternational Business Machines Corporation
InventorsObinna B. Anya, Heiko H. Ludwig, Nagapramod S. Mandagere, Mohamed Mohamed, Mu Qiao, Ramani R. Routray, Samir Tata
Life of this patent
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Abstract
One embodiment provides a quality of service (QoS) monitoring framework for dynamically binding one or more customer applications to one or more microservices in a dynamic service environment, collecting compliance data and contextual data from the dynamic service environment and one or more hosting environments, and modifying a monitoring infrastructure for the one or more customer applications based on the compliance data and the contextual data.
Claims20
A quality of service (QoS) monitoring framework comprising:
- at least one processor;
- and
- a non-transitory processor-readable memory device storing instructions that when executed by the at least one processor causes the at least one processor to perform operations including:
- dynamically binding one or more customer applications to one or more microservices in a dynamic service environment;
- collecting compliance data and contextual data from the dynamic service environment and one or more hosting environments;
- training a predictive model by applying machine learning to a portion of the compliance data that is indicative of occurrence of one or more historical service level agreement (SLA) violations;
- predicting probability of a SLA violation occurring by applying the predictive model to the compliance data and the contextual data;
- and
- dynamically switching a frequency at which a monitoring infrastructure for the one or more customer applications collects monitoring data for the one or more customer applications between a continuous basis or a periodic basis based on the predicted probability, wherein the dynamically switching comprises:
- if the monitoring data is collected continuously and the predicted probability is less than a pre-determined threshold, dynamically switching the frequency from the continuous basis to the periodic basis;
- and
- if the monitoring data is collected at pre-determined intervals and the predicted probability exceeds the pre-determined threshold, dynamically switching the frequency from the periodic basis to the continuous basis.
9 more elements
A method comprising:
- dynamically binding one or more customer applications to one or more microservices in a dynamic service environment;
- collecting compliance data and contextual data from the dynamic service environment and one or more hosting environments;
- training a predictive model by applying machine learning to a portion of the compliance data that is indicative of one or more historical service level agreement (ISLA) violations;
- predicting probability of a SLA violation occurring by applying the predictive model to the compliance data and the contextual data;
- and
- dynamically switching a frequency at which a monitoring infrastructure for the one or more customer applications collects monitoring data for the one or more customer applications between a continuous basis or a periodic basis based on the predicted probability, wherein the dynamically switching comprises:
- if the monitoring data is collected continuously and the predicted probability is less than a pre-determined threshold, dynamically switching the frequency from the continuous basis to the periodic basis;
- and
- if the monitoring data is collected at pre-determined intervals and the predicted probability exceeds the pre-determined threshold, dynamically switching the frequency from the periodic basis to the continuous basis.
6 more elements
A computer program product comprising a computer-readable hardware storage medium having program code embodied therewith, the program code being executable by a computer to implement a method comprising:
- dynamically binding one or more customer applications to one or more microservices in a dynamic service environment;
- training a predictive model by applying machine learning to a portion of the compliance data that is indicative of one or more historical service level agreement (SLA) violations;
- predicting probability of a SLA violation occurring by applying the predictive model to the compliance data and the contextual data;
- and
- dynamically switching a frequency at which a monitoring infrastructure for the one or more customer applications collects monitoring data for the one or more customer applications between a continuous basis or a periodic, basis based on the predicted probability, wherein the dynamically switching comprises:
- if the monitoring data is collected continuously and the predicted probability is less than a pre-determined threshold, dynamically switching the frequency from the continuous basis to the periodic basis;
- and
- if the monitoring data is collected at pre-determined intervals and the predicted probability exceeds the pre-determined threshold, dynamically switching the frequency from the periodic basis to the continuous basis.
5 more elements
Dependencies are inferred from the claim text.