Capacity Recommendation Engine for Scalable Virtual Computer Groups
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Solution Overview
Problem
Existing network-based computing infrastructures face challenges in optimizing virtual computer group scaling to balance workload demands and cost efficiency, often resulting in either underutilization or inadequate resource allocation due to fixed compute capacity settings.
Innovation Solution
A system that includes a capacity recommendation engine to analyze performance metrics and scaling activities, providing recommendations for optimal compute capacity adjustments in automatically scalable computer groups, allowing for dynamic resource allocation based on workload variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed compute capacity settings are used in virtual computer groups, then device complexity is reduced and ease of operation is improved, but resource allocation efficiency deteriorates and cost efficiency worsens
Solution Approach 1:
The system enables virtual computer groups to automatically adjust their own compute capacity by monitoring performance metrics and triggering scaling operations when thresholds are met, eliminating the need for manual intervention while optimizing resource allocation dynamically
Solution Approach 2:
The system continuously monitors performance metrics such as CPU utilization, memory usage, and request rates, using this feedback to automatically determine when scaling operations should be triggered, creating a closed-loop control system that adapts to changing workload conditions
2Productivity
If compute capacity is increased to ensure responsiveness to workload demands, then productivity is improved, but cost efficiency deteriorates due to underutilization during low-demand periods
Solution Approach 1:
The system dynamically adjusts compute capacity by adding or removing virtual computers based on real-time performance metrics and predefined thresholds, allowing the infrastructure to adapt its resource allocation to match actual workload demands rather than maintaining fixed capacity
Solution Approach 2:
The system changes the parameter of compute capacity (number of virtual computers) based on monitored performance metrics, automatically increasing capacity when thresholds indicate high demand and decreasing capacity when thresholds indicate low demand, thereby optimizing both responsiveness and cost efficiency
3Loss of energy
If manual scaling operations are performed to optimize resource allocation, then cost efficiency is improved, but productivity deteriorates due to scaling frequency and operational overhead
Solution Approach 1:
The system automatically performs scaling operations by monitoring performance metrics and triggering capacity changes when thresholds are met, eliminating manual operational overhead while optimizing resource allocation and reducing unnecessary scaling events through intelligent threshold-based decision-making
Data Source
AI summary
Computers within automatically scalable virtual computer groups are automatically added and removed based on workload conditions. New computers are created with compute capacities or sizes that define the resources that form the computers. A capacity recommendation engine may be configured to monitor information surrounding scaling events to determine resulting utilization of scalable virtual computer groups, and to provide recommendations regarding compute capacity. The recommendations may be designed to balance cost and responsiveness.