Revision-Aware Resource Allocation for Kubernetes Right-Sizing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional resource allocation methods in Kubernetes-based systems are inefficient and imprecise due to the use of pre-defined static time windows, leading to issues such as over-provisioning, under-provisioning, and ineffective isolation, which result in higher costs and performance degradation.
Innovation Solution
A method that dynamically detects software service revisions and adjusts resource allocation by analyzing usage patterns in a time window, implementing recommendations that increase or decrease resources based on the significance of the revision, ensuring data-driven, autonomous optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional pre-defined static time windows are used for resource allocation recommendations, then the system is simple to implement, but the precision and accuracy of resource allocation recommendations deteriorates
Solution Approach 1:
The patent transforms the static time window approach into a dynamic revision-aware time window approach. Instead of using fixed pre-defined time windows, the system dynamically adjusts the analysis period based on detected software service revisions. When a revision is detected, the system identifies a new time window starting from that revision point, ensuring that resource allocation recommendations are based on relevant, up-to-date usage patterns rather than outdated static periods.
Solution Approach 2:
The system changes the parameter of time window selection from a fixed static value to a dynamic value that depends on revision detection. By monitoring software service revisions and adjusting the time window parameters accordingly, the system adapts the analysis period to match actual service evolution, thereby improving recommendation precision without significantly increasing implementation complexity.
2Ease of manufacture
If static time window analysis is used, then the implementation is straightforward, but the adaptability to changing service demands deteriorates
Solution Approach 1:
The system implements a feedback mechanism by continuously monitoring software service revisions and using this information to adjust the time window for usage pattern analysis. When a revision is detected, the system feeds this information back into the resource allocation recommendation process, resetting or adjusting the analysis period to start from the revision point. This feedback loop enables the system to adapt to changing service demands while maintaining a relatively simple implementation structure.
Solution Approach 2:
The patent introduces dynamics into the previously static time window approach by making the time window selection dependent on detected revisions. The system dynamically adjusts the analysis period based on service evolution, ensuring that resource allocation recommendations remain adaptable to changing demands without requiring complex reconfiguration or manual intervention.
3Measurement precision
If revision-aware dynamic time window selection is implemented, then the precision and adaptability of resource allocation recommendations improves, but the system complexity increases
Solution Approach 1:
The system implements self-service by automatically detecting software service revisions and autonomously adjusting the time window for usage pattern analysis without requiring manual configuration or complex external systems. The revision detection and time window adjustment processes are integrated into the existing resource allocation recommendation framework, allowing the system to improve precision while minimizing additional complexity through self-managing mechanisms.
Solution Approach 2:
The patent enhances the universality of the resource allocation system by making it applicable to multiple service revision scenarios without requiring separate specialized mechanisms. The revision-aware time window selection approach works across different service types and revision frequencies, providing improved precision through a single unified mechanism rather than multiple specialized systems, thereby limiting the increase in overall complexity.
4Quantity of substance
If usage patterns from before significant revisions are included, then more data is available for analysis, but the accuracy of recommendations for the current service version deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the usage data into distinct segments based on software service revisions. When a revision is detected, the system segments the usage patterns into pre-revision and post-revision periods, analyzing only the post-revision segment for generating resource allocation recommendations. This segmentation ensures that the analysis is based on relevant usage data from the current service version, improving recommendation accuracy while still allowing for systematic data collection and analysis.
Data Source
AI summary
In embodiments, a method includes dynamically detecting a current revision to a software service running within a container and identifying whether the current revision specifies a significant change or not. In response to the identification, the method further includes selecting a time window in which to analyze the service's usage patterns (the “analyzed time window”) and determining whether to include data regarding the service's usage patterns prior to the current revision in the analyzed time window or not. The method further includes recommending a right-sizing implementation for the service based on the usage patterns in the analyzed time window. In embodiments, until data for the entire analyzed time window has been acquired, the method only implements right-sizing recommendations that increase resources available to the service. Once such data has been acquired, the method implements right-sizing recommendations that both increase and decrease such resources.