US 2026/0065183 A1 · METHOD AND SYSTEM TO OPTIMIZE CLOUD COST BY ANALYZING RESOURCE UTILIZATION

Abstract

The under-utilized resources are attributed to various reasons such as over-provisioning of resources, diminishing use of resource, application upgrades. The present disclosure identifies one or more under-utilized resources from a set of resources by (i) deriving most recent steady state in utilization of metrics specific to set of resources, (ii) deriving one or more temporal patterns by analyzing derived most recent steady state in utilization of metrics specific to set of resources, (iii) computing a representative maximum utilization of metrics specific to set of resources for each of derived one or more temporal patterns, (iv) deriving headroom based on computed representative maximum utilization, (v) forecasting future behavior of utilization, (vi) deriving time to saturation for metrics specific to set of resources, and (vii) identifying one or more under-utilized resources based on derived time to saturation. One or more recommendations for optimizing identified one or more under-utilized resources are generated.

A processor implemented method, comprising:

identifying, via one or more hardware processors, one or more under-utilized resources from one or more set of resources specific to a cloud vendor, wherein the one or more under-utilized resources refers to (i) a first set of resources with consistently low utilization levels and (ii) the first set of resources that are not likely to saturate in near future, wherein the one or more set of resources are instances of one or more cloud services created within one or more resource groups, and wherein the one or more under-utilized resources are identified:
(i) deriving a most recent steady state in utilization of one or more metrics specific to the one or more set of resources using an ensemble of change detection algorithms;
(ii) deriving one or more temporal patterns by analyzing the derived most recent steady state in the utilization of the one or more metrics specific to the one or more set of resources across one or more temporal dimensions to identify one or more recurring patterns of at least one of an over utilization and a under-utilization;
(iii) computing a representative maximum utilization of the one or more metrics specific to the one or more set of resources for each of the derived one or more temporal patterns using one or more techniques comprising at least one of a 90th quantiles, a maximum after removing outliers and a mean+standard deviation;
(iv) deriving a headroom based on the computed representative maximum utilization and a value associated with full utilization for the one or more temporal dimensions pertaining to each of the derived one or more temporal patterns;
(v) forecasting a future behavior of the utilization pertaining to the one or more metrics specific to the one or more set of resources using the derived headroom by using an ensemble of forecasting algorithms tailored to one or more data characteristics pertaining to the one or more metrics specific to the one or more set of resources;
(vi) deriving a time to saturation pertaining to the one or more metrics specific to the one or more set of resources using the forecasted future behavior by finding when the utilization of the one or more metrics specific to the one or more set of resources consistently exceeds one or more safe utilization limits set by an enterprise;
and
(vii) identifying the one or more under-utilized resources based on the derived time to saturation;
and
deriving, via the one or more hardware processors, one or more recommendations for optimizing the identified one or more under-utilized resources using at least one of:
(i) deriving a first recommendation for a first status when the one or more metrics pertaining to the one or more set of resources comprised in a server indicate a first level headroom for a span of time in a recurring manner;
(ii) deriving a second recommendation for a second status when the one or more metrics pertaining to the one or more set of resources comprised in the server indicates a second level headroom and no near-term saturation for a span of time in a recurring manner;
and
(iii deriving a third recommendation for a third status, wherein multiple servers are packed within an existing server for consolidating the one or more set of resources, wherein consolidation of the one or more set of resources is performed using a greedy approach by:
a) selecting a second set of resources from the identified one or more under-utilized resources, wherein each of the selected second set of resources comprises (i) a set of metrics measuring the utilization of each of the selected second set of resources, and (ii) contains a set of associated one or more temporal patterns;
b) calculating a representative utilization for each of the set of metrics associated with each of the selected second set of resources;
and
c) iteratively perform:
a) identifying a resource having the highest utilization across each of the set of metrics;
b) finding one or more target servers having a headroom equal to a predefined headroom to accommodate the identified resource, wherein the one or more target servers are found by checking if the headroom available for each of the set of metrics on the one or more target servers exceeds the representative utilization;
c) calculating one or more key metrics including an available space and a space skew for each of the one or more target servers;
d) identifying a best suited server amongst the one or more target servers based on a score computed for each of the one or more target servers using the calculated one or more key metrics;
and
e) creating a new target server if none of the existing one or more target servers accommodate the identified resource.

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Method and system to optimize cloud cost by analyzing resource utilization

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Method and system to optimize cloud cost by analyzing resource utilization

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Abstract

Claims20