Cloud Pricing Optimizer for Dynamic Capacity Allocation
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Solution Overview
Problem
Managing dynamic pricing and resource allocation in large-scale data centers is complex due to varying resource demands and prices, making it difficult for providers to maintain customer satisfaction and resource utilization while minimizing budgeting complexity for clients.
Innovation Solution
A pricing optimizer tool that uses programmatic interfaces to analyze client resource usage data, pricing policies, and optimization goals to recommend resource instance reservations and acquisitions, allowing clients to specify preferences and opt-in for automated implementation of recommendations, and dynamically adjust interruptibility settings and pricing based on supply and demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dynamic pricing is implemented to reflect supply and demand, then resource utilization and revenue are improved, but pricing complexity and customer satisfaction are worsened
Solution Approach 1:
A pricing optimizer service is introduced as an intermediary between the resource manager and customers. This service automatically analyzes usage data, determines optimal pricing strategies, and generates recommendations, thereby mediating the complexity of dynamic pricing while maintaining high resource utilization and customer satisfaction
2Measurement precision
If manual resource budget management is required, then pricing control precision is improved, but ease of operation is worsened
Solution Approach 1:
The system implements self-service through automated pricing optimization. The pricing optimizer service automatically manages resource budget analysis, pricing strategy generation, and recommendation implementation without requiring manual customer intervention, thereby maintaining pricing control precision while significantly improving ease of operation
3Ease of operation
If excess capacity is sold at fixed prices, then ease of operation is improved, but productivity is worsened
Solution Approach 1:
The pricing system transitions from static fixed pricing to dynamic pricing for excess capacity. The pricing optimizer continuously adjusts prices based on real-time supply and demand conditions, allowing the system to maintain ease of operation through automation while significantly improving revenue generation from excess capacity
Data Source
AI summary
Methods and apparatus for notification-based pricing of excess cloud capacity are disclosed. A system includes a resource manager and a plurality of resource instances, each of which has an interruptibility setting. In response to a first acquisition request, the resource manager allocates a first instance with a first interruptibility setting to a first client, allowing the resource manager to revoke the first client's access to the first instance without a notification. In response to an approval of a second acquisition request, the resource manager allocates a second instance with a second interruptibility setting to a second client, allowing the client to retain access to the instance for a delay interval after the second client receives an access revocation notification. Respective billing amounts for the clients' use of the instances are determined based on the interruptibility settings used.