Forecast-Driven Vertical Scaling for Virtual Machine Resource Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional computing scaling technologies face inefficiencies and insufficiencies in dynamically accommodating changing demands, leading to improper provisioning of resources, potential service disruptions, and costly over-provisioning or insufficient resource allocation.
Innovation Solution
A method and system for automated vertical scaling that uses historical resource utilization data to forecast future demands, allowing for the spontaneous allocation of resources within a virtual machine computing environment, eliminating the need for manual intervention and coarse-grained resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual provisioning of computing instances is used, then administrators can control resource allocation, but the process is time-consuming and inefficient in responding to network conditions
Solution Approach 1:
The system enables self-service automation through forecast-driven auto-scaling policies. The forecasting service automatically analyzes historical resource utilization data and predicts future demand, triggering spontaneous resource allocation without administrator intervention. This resolves the contradiction by eliminating manual operations while maintaining control through automated decision-making based on predictive analytics.
Solution Approach 2:
The forecasting service performs preliminary analysis of resource utilization trends before actual demand occurs. By predicting future resource needs based on historical data and patterns, the system proactively provisions resources in advance, improving response speed while maintaining controlled allocation through pre-established forecasting models and policies.
2Ease of manufacture
If horizontal scaling with fixed VM characteristics is used, then implementation is simple, but resource utilization is inefficient and users overpay for allocated resources
Solution Approach 1:
The system transitions from static fixed-size VMs to dynamic resource allocation through forecast-driven auto-scaling. Virtual machine characteristics such as CPU and memory allocation are dynamically adjusted based on predicted demand patterns. This maintains implementation simplicity through automated policies while eliminating resource waste by allocating exactly the right amount of resources needed at each time period.
Solution Approach 2:
The forecasting service dynamically changes resource allocation parameters (CPU, memory, storage) based on predicted demand. Instead of fixed VM characteristics, the system continuously adjusts parameters according to forecasted workload patterns, achieving efficient resource utilization while keeping implementation simple through automated parameter optimization based on historical data analysis.
3Loss of energy
If vertical scaling is used to allow fine-grained control, then resource utilization improves, but the system complexity increases and requires automated forecasting mechanisms
Solution Approach 1:
The forecasting service acts as an intermediary layer between resource demand and vertical scaling execution. It analyzes historical utilization data, predicts future needs, and triggers appropriate scaling actions. This intermediary approach achieves fine-grained resource control and high utilization efficiency while managing system complexity by encapsulating the forecasting logic and automation mechanisms in a dedicated service layer.
4Reliability
If computing resources are gradually replaced to maintain service continuity, then service disruption is avoided, but the replacement time and number of resources affected increase
Solution Approach 1:
The forecasting service performs preliminary demand analysis before resource replacement is needed. By predicting future resource requirements and utilization patterns, the system proactively identifies optimal replacement timing and sequences. This allows gradual resource replacement to be planned and executed in advance, maintaining service continuity while minimizing replacement time through predictive scheduling and automated coordination of replacement activities.
Data Source
AI summary
A feature capacity scaling methodology is disclosed. In a computer-implemented method, components of a computing environment are automatically monitored, and have a feature capacity analysis using a forecast performed thereon. Provided the feature capacity analysis determines that features of the components are well utilized, a vertical scaling of the features is performed.