Virtualized Computing Instance Purchasing Optimization
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Solution Overview
Problem
In virtualized computing environments, customers often face challenges in optimizing computing resource allocation, leading to inefficient usage and increased costs due to fluctuating demand and supply, as well as underutilization or overcommitment of resources.
Innovation Solution
The technology applies purchasing configuration optimization rules to calculate and recommend an optimized purchasing configuration for computing instances based on historical usage data, such as CPU and memory usage, allowing for the downgrading or upgrading of instance types and the recommendation to sell or purchase reserved instances through a marketplace, ensuring the best value for customers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If customers reserve computing resources for long durations, then resource availability is improved, but cost efficiency deteriorates due to overcommitment and fluctuating demand
Solution Approach 1:
The patent implements dynamic purchasing configuration optimization that automatically adjusts resource allocation based on real-time usage patterns and demand fluctuations. The system monitors historical usage data and dynamically modifies reservation levels, instance types, and purchasing options to match actual needs, preventing both overcommitment and underutilization while maintaining reliable resource availability.
2Productivity
If virtualization technologies are used to share physical computing machines, then resource utilization efficiency is improved, but complexity of managing diverse customer needs worsens
Solution Approach 1:
The patent creates a universal optimization system that handles multiple customer needs through a single automated platform. The system provides multi-functional capabilities including usage pattern analysis, purchasing configuration optimization, resource allocation recommendations, and cost analysis - all through one integrated solution that serves diverse customers with varying requirements without increasing operational complexity.
Solution Approach 2:
The patent implements self-service optimization where the system automatically analyzes customer usage patterns and generates optimized purchasing configurations without requiring manual intervention. The automated optimization engine independently manages the complexity of diverse customer needs by processing usage data and generating recommendations based on predefined optimization criteria, freeing customers from complex management tasks.
3Adaptability or versatility
If customers purchase computing resources on an ad-hoc basis, then flexibility is improved, but cost efficiency deteriorates due to not leveraging reserved instance discounts
Solution Approach 1:
The patent applies preliminary action by analyzing historical usage patterns to predict future resource needs and recommending appropriate reserved instance purchases in advance. The system identifies opportunities to purchase reserved instances before commitments are needed, allowing customers to secure discounted rates while maintaining the flexibility to adjust ad-hoc purchases when actual usage deviates from predictions.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the mix between reserved and ad-hoc purchasing based on usage pattern analysis. The system monitors usage metrics and automatically modifies purchasing configuration parameters, such as reserved instance duration, instance type, and allocation levels, to optimize the balance between cost efficiency and operational flexibility.
4Reliability
If resource allocation is increased to meet peak demand, then service reliability is improved, but resource utilization efficiency deteriorates during low-demand periods
Solution Approach 1:
The patent implements dynamic resource allocation that automatically adjusts provisioning levels based on analyzed usage patterns and predicted demand. The system monitors historical data to identify peak and low-demand periods, then dynamically modifies resource allocation to provide sufficient capacity during peaks while reducing or right-sizing resources during low-demand periods, maintaining service reliability without sacrificing utilization efficiency.
Data Source
AI summary
A technology to optimize virtualized computing is described. Usage of a plurality of virtualized computing instances is identified in a virtualized computing environment. Purchasing configuration optimization rules are applied to calculate an optimized purchasing configuration for the plurality of virtualized computing instances in a virtualized computing environment. The optimized purchasing configuration is recommended for the plurality of virtualized computing instances.