Cloud Resource Combinatorial Optimization Engine
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cloud tools fail to comprehensively evaluate and optimize combinations of cloud resources across diverse cloud sets, making it difficult for users to achieve the best cost ratio and redundancy for processor, memory, and storage capabilities.
Innovation Solution
A cloud management system that performs combinatorial optimization by analyzing user-specified criteria and selecting the best available cloud-resource combinations, using a deployment engine to identify and evaluate resources across multiple clouds, and presenting optimized deployment options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually track and evaluate cloud resources across multiple clouds, then they can monitor their deployment, but the complexity and time required increases significantly
Solution Approach 1:
The system performs self-service by automatically evaluating cloud resource combinations without requiring manual user intervention. The combinatorial optimization engine autonomously analyzes usage history, generates resource combinations, and identifies optimal configurations across multiple clouds, freeing users from time-consuming manual tracking while maintaining high evaluation accuracy.
Solution Approach 2:
The patent replaces the mechanical manual process of tracking and evaluating cloud resources with an automated computational system. The combinatorial optimization engine uses algorithms to systematically evaluate resource combinations, substituting human effort with automated processing that achieves both high precision and efficiency.
2Reliability
If users explore all possible cloud-resource combinations to find the best cost ratio and redundancy, then optimization quality improves, but the computational complexity and difficulty increases
Solution Approach 1:
The system segments the complex task of evaluating all cloud-resource combinations into manageable components. It divides resources into categories (compute, storage, networking), evaluates them separately based on usage history, and then recombines them optimally. This segmentation reduces computational complexity while maintaining comprehensive evaluation for high optimization quality.
Solution Approach 2:
The system performs preliminary action by pre-analyzing usage history and pre-evaluating resource characteristics before the actual optimization task. It stores evaluated resource data and usage patterns, so when optimization is needed, the system can quickly retrieve pre-processed information rather than starting from scratch, reducing the complexity of real-time evaluation.
3Adaptability or versatility
If the system provides comprehensive evaluation of cloud combinations, then users get better deployment options, but the ease of operation decreases due to complexity
Solution Approach 1:
The system handles the complex task of evaluating cloud combinations and generating optimized deployment options autonomously. Users simply provide their requirements, and the system self-services by performing the comprehensive analysis, generating multiple optimized combinations, and presenting them for selection. This maintains high adaptability while preserving ease of operation.
Solution Approach 2:
The combinatorial optimization engine acts as an intermediary between user requirements and cloud resource selection. It translates high-level user needs into detailed resource configurations, evaluating all combinations and presenting simplified options. This intermediary layer provides comprehensive evaluation capability while shielding users from operational complexity.
4Adaptability or versatility
If users manage multiple independent clouds with multiple applications, then resource diversity increases, but the difficulty of tracking and monitoring increases
Solution Approach 1:
The system provides universal tracking and monitoring capabilities that work across multiple independent clouds and diverse resource types. It uses a unified approach to collect, analyze, and evaluate resources from different cloud providers, applying the same optimization algorithms regardless of cloud source. This universal methodology handles resource diversity while maintaining consistent, manageable tracking processes.
Data Source
AI summary
Embodiments relate to combinatorial optimization of multiple resources across a set of cloud-based networks. In aspects, a set of usage histories can store patterns for users in a host cloud-based network recording the consumption of processor, memory, storage, operating system, application, or other resources subscribed to by the user. The user can be a corporation or other collective user. A deployment engine can identify similar target resources available in a set of target clouds. The engine can receive selection criteria for selecting target clouds for a migration of the user's deployment. In the combinatorial selection, each target cloud can independently supply one or more target resources. The engine can thus identify a series of combinations of target resources and target clouds supplying those resources to discover combinations of target clouds and target resources that can satisfy or optimize the selection criteria, such as cost or others.