Cloud Resource Planner Automating Acquisition via Dynamic Pricing Analysis
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Solution Overview
Problem
The complexity of managing and optimizing resource acquisition in cloud computing environments, particularly with dynamic pricing models, often leads to suboptimal choices by customers due to the multitude of available options and fluctuating prices, which can result in inefficient resource allocation and market disruptions.
Innovation Solution
A resource planner system that allows clients to specify job characteristics and resource acquisition policies, generating recommended acquisition plans and prices based on historical data and customizable algorithms, enabling clients to make informed decisions and automate resource procurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple resource acquisition modes and dynamic pricing options are provided to customers, then resource allocation flexibility and market efficiency are improved, but system complexity and customer decision-making difficulty increase
Solution Approach 1:
The patent introduces a resource planner system that acts as an intermediary between customers and the complex resource acquisition marketplace. This mediator analyzes job requirements, evaluates multiple acquisition modes (reservations, spot market, dedicated hosts), and provides optimized resource acquisition plans, thereby simplifying customer decision-making while maintaining system flexibility and efficiency
2Extent of automation
If customers make resource acquisition decisions independently without assistance, then system automation is improved, but resource allocation efficiency deteriorates due to suboptimal choices
Solution Approach 1:
The patent implements a self-service resource planner that automatically analyzes customer job requirements, accesses historical pricing data, evaluates acquisition strategies, and generates optimized resource acquisition plans without requiring expert intervention. This automated self-service system improves both automation extent and resource allocation efficiency by providing data-driven recommendations
3Productivity
If dynamic pricing is implemented in the resource marketplace, then market responsiveness and resource utilization are improved, but customer predictability and planning capability deteriorate
Solution Approach 1:
The patent employs preliminary action by analyzing historical pricing data and market trends before customers make acquisition decisions. The resource planner predicts future pricing patterns and recommends optimal acquisition timing and strategies, allowing customers to plan ahead despite dynamic pricing fluctuations and maintain both market responsiveness and price predictability
Data Source
AI summary
Methods and apparatus for a job resource planner for cloud computing environments are disclosed. A system includes a plurality of resource instances of a provider network, and a resource planner. The planner receives a plan request from a client, comprising a job goal and an indication of a resource acquisition policy to be used to obtain resource instances for the job. The policy specifies one or more instance data sources. The planner generates a resource acquisition plan for the job, based at least in part on an analysis of pricing data obtained from a specified data source. The analysis comprises one or more computation steps indicated in the policy. The generated plan includes at least one recommended acquisition price for a resource instance.