Cloud Resource Graph Ranking for Usage Longevity Forecasting
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Solution Overview
Problem
Existing cloud computing systems lack efficient methods to manage and forecast the usage and longevity of computing resources, leading to suboptimal resource allocation, increased costs, and inefficiencies in cloud migrations, maintenance, and recommendations.
Innovation Solution
A network graph approach using AI-based models to calculate resource longevity scores and forecast usage, incorporating direct and indirect connections, and utilizing node-weighted harmonic centrality to rank resources by importance, with AI models for classification and forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud computing resources are scaled up to ensure availability, then service reliability is improved, but resource costs and waste increase
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring resource usage patterns and adjusting provisioning accordingly. The system transitions from static over-provisioning to dynamic scaling, ensuring resources are available when needed while reducing waste during low-utilization periods. This resolves the contradiction by making the system adaptable to changing demands rather than maintaining fixed capacity.
Solution Approach 2:
The system changes key parameters such as resource provisioning levels, allocation strategies, and capacity planning based on analyzed usage patterns. By modifying these parameters dynamically rather than maintaining constant over-provisioning, the system achieves both reliability and resource efficiency, resolving the contradiction between service availability and resource consumption.
2Stability of the object's composition
If resource provisioning is increased to prevent service disruptions, then service continuity is improved, but operational costs increase
Solution Approach 1:
The system performs preliminary analysis of resource usage patterns and predicts future needs before service disruptions occur. By proactively identifying trends and potential bottlenecks, the system can prepare appropriate resource allocations in advance, ensuring continuity while avoiding unnecessary over-provisioning and associated costs.
Solution Approach 2:
The patent implements continuous feedback loops that monitor resource utilization, service performance, and cost metrics. This feedback enables the system to adjust provisioning strategies in real-time, maintaining service continuity while optimizing operational costs by eliminating wasteful over-provisioning based on actual rather than theoretical demands.
3Productivity
If comprehensive resource monitoring is implemented to improve allocation efficiency, then resource optimization is improved, but system complexity increases
Solution Approach 1:
The patent implements a unified monitoring and analysis platform that handles multiple functions including resource tracking, pattern recognition, predictive analytics, and optimization recommendations. This multi-functional approach consolidates what would otherwise require multiple separate systems, improving allocation efficiency while minimizing the increase in system complexity through consolidation rather than proliferation of components.
Data Source
AI summary
An approach is disclosed that creates a network graph that identifies computing resources as nodes. Each of the computing resources is weighted based upon the corresponding resource's attributes and usage. The approach connects a first set of nodes as directly connected and a second set of nodes as indirectly connected. Node longevity values are calculated for each node in the network graph with each of the node longevity values corresponding to one of the nodes in the network graph. The calculations are based on the direct and indirect connections between each of the nodes. The computing resources are managed based on the corresponding node longevity values.