Cloud Resource Usage Monitoring for Allocation Inefficiency
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Solution Overview
Problem
Cloud-based computing platforms face inefficiencies in resource allocation, leading to over-provisioning, under-provisioning, and wastage of computing resources, which are difficult to track and manage effectively.
Innovation Solution
Implementing a system that stores usage data in blockchain or time-series databases to identify and rectify inefficiencies, reallocates resources, and estimates carbon footprint, using structured data to provide notifications and recommendations for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud computing resources are expanded to meet growing demand, then service capacity and availability are improved, but resource allocation inefficiency and waste increase
Solution Approach 1:
The system implements continuous monitoring of computing resource usage patterns and feeds this information back to an optimization engine that automatically adjusts resource allocation. This closed-loop feedback mechanism enables the system to adapt to changing demands in real-time, ensuring resources are allocated efficiently while maintaining service capacity.
Solution Approach 2:
The patent employs dynamic resource allocation where computing resources are not statically assigned but continuously adjusted based on real-time usage patterns and predictive analytics. This dynamic approach allows the system to scale resources up or down as needed, improving service capacity while minimizing waste during low-utilization periods.
2Productivity
If detailed tracking of computing resource usage is implemented, then allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary optimization layer that sits between the cloud computing resources and users. This intermediary automatically monitors usage patterns, analyzes allocation efficiency, and makes optimization decisions without requiring direct user intervention or complex manual tracking, thus improving allocation efficiency while managing system complexity.
Solution Approach 2:
The patent implements self-service optimization where the system automatically monitors its own resource usage patterns and performs optimization without external intervention. The automated monitoring and optimization engine continuously adjusts resource allocation based on observed patterns, improving efficiency while minimizing the complexity burden on users or operators.
3Productivity
If automated resource reallocation is implemented, then resource utilization efficiency is improved, but control and management complexity increase
Solution Approach 1:
The automated reallocation system incorporates feedback mechanisms that continuously monitor resource usage and automatically adjust allocations based on observed patterns. This automated feedback loop improves resource utilization efficiency while reducing manual control complexity, as the system self-regulates without requiring intensive human management.
Data Source
AI summary
Example implementations may include: receiving, from a computing system, usage data including entries specifying usage of computing resources of the computing system; storing, as structured data, records that include representations of the entries; after storing the records, determining, based on an efficiency criterion and for a time range of the records, inefficiencies related to the usage of the computing resources; and providing a notification indicating the inefficiencies and a subset of the computing resources that are producing the inefficiencies.