Cloud Cost Anomaly Analysis Using Resource Clustering
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Solution Overview
Problem
Existing cloud cost optimization tools fail to effectively identify systemic spend anomalies and provide actionable recommendations due to their inability to analyze cloud resources in a holistic manner, leading to unpredictable and chaotic cloud expenditures.
Innovation Solution
A method and system that identifies homogeneous resources, creates two-dimensional clusters using k-means clustering, computes effective prices, and uses decision trees and Interesting Subset Discovery (ISD) to detect spend anomalies, iteratively relax attribute constraints, and expand the search space for optimization recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud resources are analyzed in isolation using existing tools, then individual resource spend can be detected, but systemic spend anomalies and their business impact cannot be captured
Solution Approach 1:
The patent segments cloud resources into homogeneous groups based on domain constraints (cloud vendor, resource series type) and user-defined constraints (location, resource group, subscription). This segmentation enables systematic analysis of resource relationships while maintaining manageability. Each segment can be analyzed independently and then aggregated to capture systemic anomalies.
Solution Approach 2:
The patent introduces an intermediary analysis layer that connects individual resource analysis with business impact assessment. This intermediary layer uses clustering algorithms to identify spend patterns and anomalies, then links them to business functions through the hierarchical IT infrastructure context, enabling both precise detection and systemic understanding.
2Adaptability or versatility
If cloud spend flexibility and dynamic options are increased, then resource adaptability improves, but spend unpredictability and chaos increase
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring cloud resource usage and spend patterns, then using clustering analysis to identify deviations from expected behavior. The system provides feedback on spend anomalies and their business impact, enabling proactive budget adjustments while maintaining resource flexibility. This closed-loop approach reconciles adaptability with predictability.
Solution Approach 2:
The patent performs preliminary clustering and anomaly detection analysis on cloud resource patterns before spend chaos occurs. By establishing baseline spending patterns through k-means clustering and identifying homogeneous resource groups in advance, the system can predict potential spend anomalies and take preventive actions, maintaining both flexibility and reliability.
3Loss of information
If existing statistical tools are used to generate spend dashboards and reports, then spend distribution can be visualized, but actionable recommendations and prioritization perspectives are not provided
Solution Approach 1:
The patent enables self-service cloud cost optimization by automatically performing clustering analysis, anomaly detection, and recommendation generation without requiring manual intervention. The system processes cloud resource data, identifies spend anomalies, determines business impact, and generates prioritized recommendations automatically, transforming raw spend information into actionable insights that users can directly implement.
Solution Approach 2:
The patent replaces manual spend analysis and recommendation generation with automated computational mechanisms. Instead of requiring users to manually interpret spend dashboards and generate recommendations, the system uses k-means clustering algorithms, anomaly detection mechanisms, and business impact assessment models to automatically produce actionable, prioritized recommendations.
4Reliability
If cloud budget is increased to maintain forehand control and foresight, then budget security improves, but cost efficiency decreases
Solution Approach 1:
The patent performs preliminary clustering and anomaly detection analysis to establish baseline spending patterns and identify potential issues before they impact the budget. By proactively detecting spend anomalies and assessing their business impact, the system enables early intervention and optimization, allowing businesses to maintain leaner budgets with adequate control and foresight rather than requiring excessive budget buffers.
Data Source
AI summary
Existing tools detect abnormal spends but fail to capture their systemic impact as these tools analyze resources in isolation and further fail to offer actionable recommendations. The present disclosure identifies one or more set of homogeneous resources from one or more set of resources. One or more two-dimensional clusters are created between dimensions of spend and dimensions of quantity. An effective price for each of one or more two-dimensional clusters is created and a baseline price is identified. One or more spend anomalies are identified based on comparison of associated effective prices of one or more two-dimensional clusters and identified baseline price. One or more attribute constraints are identified which when relaxed provide maximum reduction in defined baseline price to rectify identified one or more anomalies. Expands a search space to generate one or more recommendations within a new search space with relaxed one or more attribute constraints.