Adaptive Compute Resource Allocation With ML Feedback Scaling
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Solution Overview
Problem
Traditional compute environments statically allocate resources, leading to overprovisioning during low demand periods, resulting in underutilization and increased operating costs.
Innovation Solution
A machine learning-informed feedback sequence dynamically adjusts compute resources by using machine learning models to adapt the compute environment in real-time, scaling resources based on utilization data and request types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If compute resources are statically allocated to handle worst-case demand scenarios, then system reliability is improved, but resource utilization deteriorates during normal or low demand periods
Solution Approach 1:
The patent implements dynamic resource allocation where the compute environment automatically adjusts resource allocation based on real-time utilization data and machine learning predictions. This transforms the static allocation model into a dynamic one that adapts to changing demand conditions, maintaining reliability while optimizing utilization.
Solution Approach 2:
The system employs a feedback mechanism where utilization data is continuously collected and fed into machine learning models that predict future resource needs. These predictions trigger automatic resource allocation adjustments, creating a closed-loop control system that balances reliability and utilization through continuous adaptation.
2Reliability
If compute resources are statically allocated to handle peak demand, then service availability is improved, but operating costs increase due to underutilization during low demand periods
Solution Approach 1:
The system dynamically adjusts compute resource allocation based on predicted demand patterns, allowing organizations to maintain service availability during peak periods while reducing resource allocation during low demand periods. This dynamic adaptation directly addresses the cost inefficiency of static over-provisioning.
Solution Approach 2:
The machine learning models analyze utilization data to predict future resource requirements, enabling the system to change allocation parameters proactively. This allows the compute environment to maintain adequate resources for service availability while optimizing utilization to reduce operating costs.
3Measurement precision
If machine learning models are used to predict resource needs, then resource allocation accuracy is improved, but system complexity increases
Solution Approach 1:
The system implements self-service automation where machine learning models automatically analyze utilization data, predict resource needs, and trigger resource allocation adjustments without human intervention. This automation handles the complexity internally while providing simple, accurate resource allocation outcomes to users.
Solution Approach 2:
The patent introduces an intermediary layer consisting of machine learning models and orchestration services that sit between the compute resources and users. This intermediary handles the complexity of prediction and allocation automatically, providing simplified interfaces to users while achieving high allocation accuracy through sophisticated underlying mechanisms.
Data Source
AI summary
A system, method, and computer-program product includes obtaining an analytical request that specifies an analytical task to be performed using computing resources of an adaptive analytics compute service, determining, by the adaptive analytics compute service, an initial set of compute resources for executing the analytical request based on identifying a type of the analytical request, deploying, by the adaptive analytics compute service, a compute environment for executing the analytical request based on the initial set of compute resources, observing utilization data of the initial set of compute resources during a period of executing the analytical request within the compute environment, and commencing a machine learning-informed feedback sequence for autonomously adapting the compute environment, wherein one iteration of the machine learning-informed feedback sequence includes: generating a proposed set of compute resources, and encoding, based on the proposed set of compute resources, a set of instructions for automatically adapting the compute environment.