Predictive Computing Resource Allocation for Distributed Clusters
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Solution Overview
Problem
Current computing resource allocation in distributed environments is inefficient, as it often leads to delayed resource activation and increased overhead due to the lack of predictive capabilities, resulting in suboptimal resource utilization and higher operational costs.
Innovation Solution
A method for predictive computing resource allocation that models resource usage, predicts future demand, identifies available resources, calculates resource costs, determines the least expensive resource set capable of meeting demand, and allocates resources accordingly, while also activating or deactivating nodes based on predicted requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If computing resources are allocated based on current usage without predictive capabilities, then resource allocation is simple and responsive to immediate demand, but resource activation is delayed and operational costs increase
Solution Approach 1:
The patent implements predictive modeling that analyzes historical resource usage patterns to forecast future demand before it occurs. This preliminary action enables the system to proactively allocate and activate computing resources in advance, eliminating activation delays while maintaining manageable complexity through automated forecasting algorithms
2Reliability
If more computing resources are activated to meet peak demand, then service reliability is improved, but operational costs increase
Solution Approach 1:
The patent employs dynamic resource allocation that continuously adjusts computing resource activation based on predicted demand patterns. The system activates resources proactively before peak demand occurs and deactivates them when demand decreases, maintaining service reliability while optimizing operational costs through adaptive, real-time decision-making
Solution Approach 2:
The system changes operational parameters by transitioning computing resources between active and inactive states based on predictive analytics. This parameter change strategy ensures sufficient resources are available for reliability while minimizing energy consumption and operational costs by avoiding continuous activation of unnecessary resources
3Productivity
If computing resources are allocated without cost analysis, then allocation speed is maintained, but resource optimization and cost reduction are lost
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors actual resource usage against predicted demand and cost metrics. This feedback loop enables the system to learn from past allocations, refine predictive models, and optimize future resource allocation decisions to balance allocation speed with cost efficiency and operational optimization
Data Source
AI summary
For predictive computing resource allocation in a distributed environment, a model module generating a model of computing resource usage in a distributed computer system having a plurality of geographically distributed nodes organized into a plurality of clusters, a demand module predicting future demand for computing resources, a cost module calculating an operation cost for each computing resource, an available resource module identifying a set of available computing resources in the computer system, a resource set module that determines a minimum cost set of computer resources capable of meeting the predicted demand based on the set of available computing resources and on operating costs, and an activation module that determines whether to activate or deactivate each of the plurality of nodes based on the set of computer resources capable of meeting the predicted demand.