Cloud Resource Sizing via Historical Utilization Trajectories
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Solution Overview
Problem
Cloud computing systems face inefficiencies in resource allocation due to oversizing or undersizing of virtual machines, leading to performance degradation and increased costs, as existing methods lack precise analytics for determining optimal resource configurations based on historical utilization data and tagging information.
Innovation Solution
A compute sizing correction (CSC) stack that processes historical utilization data to generate cleansed metrics and determine a CSC trajectory, selecting appropriate resource configurations to adjust virtual machine provisioning, ensuring optimal resource allocation while preserving critical parameters like operating systems and networking throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If virtual machines are oversized to ensure adequate resource availability, then reliability is improved, but loss of substance increases due to wasted computing resources
Solution Approach 1:
The system dynamically changes resource allocation parameters by analyzing historical utilization data and adjusting virtual machine configurations. It transforms static resource allocation into dynamic parameter optimization, modifying CPU, memory, and storage allocations based on actual usage patterns to eliminate waste while maintaining reliability
Solution Approach 2:
The system enables virtual machines to self-optimize their resource allocation by automatically analyzing their own historical utilization data and tagging information. The autonomous adjustment mechanism allows VMs to self-regulate their resource consumption without manual intervention, balancing availability and waste reduction
2Loss of substance
If virtual machines are undersized to reduce resource consumption, then loss of substance decreases, but productivity deteriorates due to insufficient resources
Solution Approach 1:
The system implements dynamic resource allocation that adapts virtual machine configurations in real-time based on workload demands. By continuously monitoring utilization patterns and adjusting resource allocation dynamically, the system ensures productivity is maintained during peak demands while reducing consumption during low-utilization periods
Solution Approach 2:
The system performs preliminary analysis of historical utilization data and tagging information to predict future resource needs. By proactively adjusting resource allocations before performance degradation occurs, the system prevents productivity issues while avoiding excessive resource consumption
3Ease of operation
If manual resource allocation methods are used to simplify operations, then ease of operation is improved, but manufacturing precision deteriorates due to lack of precise analytics
Solution Approach 1:
The system replaces manual resource allocation mechanisms with an automated analytical engine that processes historical utilization data and tagging information. This substitution eliminates the need for manual intervention while providing precise, data-driven sizing recommendations that improve accuracy without sacrificing operational simplicity
Solution Approach 2:
The system introduces an intermediary analytics layer between manual operations and resource allocation decisions. This intermediary automatically analyzes complex data patterns and translates them into precise sizing recommendations, maintaining ease of operation while dramatically improving sizing accuracy
Data Source
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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. The input layer may receive one or more resource configurations that may be applied to implement the sizing correction. A prescriptive engine layer may generate a compute sizing correction trajectory indicative of a sizing adjustment to a computing resource. The compute sizing correction trajectory may account of historic processor, network, and memory utilization. Based on the compute sizing correction trajectory and a selected resource configuration, the prescriptive engine layer may generate the compute sizing correction tokens that that may be used to control compute sizing adjustments prescriptively.