Multi-Engine Deployment Plan Calculation for Hybrid Cloud
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Solution Overview
Problem
Existing systems face challenges in creating deployment plans for hybrid cloud and multi-cloud environments that consider multiple user requests efficiently, as they require complex optimization and increased man-hours, and simply combining optimization engines for single requests results in multiple plans without considering multiple requests effectively.
Innovation Solution
A deployment plan calculation device and method that uses multiple calculation engines with different policies to generate deployment plans for data and processing components across various site systems, integrating candidate information to create a comprehensive plan that considers performance and cost targets, including renewable energy utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single optimization engine is used to determine deployment plans, then calculation speed is improved, but the ability to consider multiple user requests and various constraints (performance, cost, renewable energy) deteriorates
Solution Approach 1:
The patent divides the optimization process into multiple independent calculation engines, each responsible for specific aspects (performance optimization, cost optimization, renewable energy optimization). Each engine processes specific user requests independently, then results are integrated to form comprehensive deployment plans that satisfy multiple constraints simultaneously.
2Adaptability or versatility
If multiple optimization engines are combined to consider various constraints, then adaptability to multiple user requests is improved, but device complexity and man-hours increase
Solution Approach 1:
The patent creates a universal optimization system where multiple calculation engines share common infrastructure including data storage, communication interfaces, and integration mechanisms. Each engine is multi-functional, capable of handling different types of optimization requests (performance, cost, renewable energy) while utilizing shared resources, thereby reducing overall system complexity despite multiple engines.
3Manufacturing precision
If multiple optimization engines are used to consider various constraints, then deployment plan quality is improved, but calculation time and man-hours increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing optimization results for different constraints in data storage units. When user requests arrive, the calculation engines quickly retrieve and integrate pre-computed data rather than performing complete optimizations from scratch, significantly reducing calculation time while maintaining high deployment plan quality.
Data Source
AI summary
A deployment optimization program causes each of a plurality of optimization engines that use different policies for calculating a deployment plan for data and containers to calculate candidate information including a candidate deployment plan that is a candidate for the deployment plan, and an evaluation value obtained by evaluating a process related to the data in the candidate deployment plan, and integrates a plurality of pieces of the candidate information based on the candidate deployment plan included in the calculated plurality of pieces of the candidate information so as to generate data and container deployment plan information.