Prescriptive Analytics Compute Sizing Correction Stack
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Solution Overview
Problem
Cloud computing systems face inefficiencies due to over- or under-provisioning of computing resources, leading to performance degradation and inefficient hardware deployment, as virtual machines may be underutilized or overloaded, resulting in sporadic or non-responsive behavior.
Innovation Solution
A prescriptive analytics-based compute sizing correction (CSC) stack that analyzes historical utilization data, consumption metrics, and tagging data to provide recommendations for optimal resource sizing, utilizing a multi-layered architecture including a data staging layer, input layer, configuration layer, prescriptive engine layer, and presentation layer to predict future utilization and adjust resource allocation accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computing resources are over-provisioned to ensure capacity, then reliability is improved, but device complexity and resource waste increase
Solution Approach 1:
The patent implements dynamic resource provisioning that automatically adjusts compute capacity based on real-time utilization metrics and predictive analytics. The system transitions from static over-provisioning to dynamic scaling, maintaining reliability by provisioning resources only when utilization thresholds are exceeded, thus eliminating unnecessary resource waste while ensuring service availability.
Solution Approach 2:
The patent employs feedback mechanisms that continuously monitor virtual machine utilization metrics and feed this information back to the provisioning system. This closed-loop control enables the system to detect when resources are underutilized and trigger right-sizing operations, preventing both over-provisioning waste and under-provisioning failures, thereby optimizing the balance between reliability and resource efficiency.
2Productivity
If compute sizing is increased to handle peak loads, then productivity is improved, but loss of substance increases due to underutilized resources during low-demand periods
Solution Approach 1:
The patent implements preliminary action by using predictive analytics and historical utilization patterns to anticipate future resource needs before peak loads occur. The system proactively provisions resources in advance of predicted demand spikes, ensuring productivity is maintained during high-demand periods while avoiding the need for permanent over-provisioning that would result in resource waste during low-demand periods.
3Manufacturing precision
If manual resource allocation is used to optimize utilization, then manufacturing precision is improved, but device complexity and operational overhead increase
Solution Approach 1:
The patent implements self-service by enabling the cloud computing system to automatically perform resource right-sizing operations without manual intervention. The system autonomously monitors utilization metrics, analyzes predictive models, and executes provisioning adjustments based on predefined policies and thresholds, achieving high allocation accuracy while eliminating the operational overhead and complexity associated with manual resource management.
Data Source
AI summary
A multi-layer compute sizing correction stack may generate prescriptive compute sizing correction tokens for controlling sizing adjustments for computing resources. The input layer of the compute sizing correction stack may generate cleansed utilization data based on historical utilization data received via network connection. A prescriptive engine layer may generate a compute sizing correction trajectory detailing adjustments to sizing for the computing resources. Based on the compute sizing correction trajectory, the prescriptive engine layer may generate the compute sizing correction tokens that that may be used to control compute sizing adjustments prescriptively.