Container-Level Metering for Granular IT Resource Visibility
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Solution Overview
Problem
Containerized IT infrastructure faces inefficiencies due to the inability to accurately track and optimize computing resource usage, leading to waste and increased costs, as current methods lack granular visibility and precise billing for dynamic and volatile resource consumption patterns.
Innovation Solution
A system comprising collectors, meters, and analytics platforms that utilize a Workload Allocation Cube (WAC) calculator to collect, measure, aggregate, and optimize computing resource usage data from container platforms, providing real-time insights and utility-style billing based on true costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If containerization is used to revolutionize IT infrastructure, then flexibility and resource efficiency are improved, but granular visibility and precise tracking of computing resource usage deteriorate
Solution Approach 1:
The patent segments the measurement system into multiple components: collectors at the container level, meters at the infrastructure level, and analytics platforms for aggregation. This segmentation enables precise tracking of individual container resource usage while maintaining overall infrastructure visibility, resolving the contradiction between containerization flexibility and measurement precision.
Solution Approach 2:
The patent introduces collectors as intermediary components that bridge the gap between containerized applications and the underlying infrastructure. These collectors capture granular metrics from containers and transmit them to meters for aggregation, enabling precise measurement without disrupting the flexibility of containerized deployments.
2Ease of manufacture
If traditional metering methods are used for IT infrastructure, then billing and cost allocation are simplified, but accuracy in tracking dynamic container consumption patterns deteriorates
Solution Approach 1:
The patent divides the metering system into hierarchical levels: container-level collectors capturing fine-grained metrics, infrastructure-level meters aggregating data, and analytics platforms processing information for billing. This segmentation enables both high precision in tracking dynamic container consumption and simplified billing through aggregated views at higher levels.
Solution Approach 2:
The patent adds temporal and hierarchical dimensions to traditional metering by implementing continuous monitoring at multiple levels (container, node, cluster) and time granularities. This multi-dimensional approach enables accurate tracking of dynamic consumption patterns while providing simplified aggregated views for billing purposes.
3Productivity
If container-level metering is implemented, then resource allocation efficiency is improved, but system complexity and deployment difficulty increase
Solution Approach 1:
The patent segments the metering system into independent, modular components (collectors, meters, analytics platforms) that can be deployed and managed separately. This modularity reduces system complexity by allowing incremental implementation and independent optimization of each component while maintaining resource allocation efficiency.
Solution Approach 2:
The patent designs collectors and meters with universal interfaces and standardized protocols that work across different container platforms and infrastructure types. This universality reduces deployment complexity by enabling a single implementation to serve multiple purposes and environments while maintaining high resource allocation efficiency.
Data Source
AI summary
Systems and methods for containerized IT intelligence and management. In one embodiment, a system for containerized IT financial management comprises at least one collector, at least one meter, at least one connector, and a reporting dashboard. The at least one collector is customized and connected to at least one container platform. The at least one collector sends capacity and consumption metrics to the at least one meter for processing and aggregation.