Cross-Domain Cabin Computing With Data Resource Distribution
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Solution Overview
Problem
Existing computing models, such as cloud computing and edge computing, face challenges in real-time data security, privacy, limited computing resources, and complex management in multi-cloud environments, leading to inefficient resource utilization and high operational costs.
Innovation Solution
A cross-domain cabin computing system that integrates a cabin generation and management system, a virtual data center system, and a cross-domain resource management system to dynamically allocate and manage resources, enabling flexible, scalable, and specialized computing environments through a data resource distribution map and cross-domain resource directory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud computing platforms provide unified platforms for resource usage, then resource utilization efficiency is improved, but data security and real-time processing capabilities deteriorate
Solution Approach 1:
The patent segments the centralized cloud computing platform into distributed edge computing nodes. Each edge node independently processes data locally, segmenting the monolithic cloud architecture into smaller, geographically distributed units. This segmentation enables both efficient resource utilization at the edge and enhanced data security through localized processing, resolving the contradiction between centralized efficiency and distributed security.
Solution Approach 2:
The patent introduces a new spatial dimension by deploying computing resources at the network edge rather than concentrating them in centralized cloud data centers. This dimensional shift from centralized to distributed architecture allows the system to achieve both high resource utilization (through local edge processing) and strong data security (through decentralized storage and computation), simultaneously addressing both requirements.
2Speed
If edge computing processes data locally, then data security and response time are improved, but computing resource limitations worsen
Solution Approach 1:
The patent merges edge computing nodes with centralized cloud platforms to form a hybrid architecture. Edge nodes handle time-critical local processing while maintaining connectivity to the cloud for heavy computational tasks. This combination allows the system to achieve fast response times through local edge processing while overcoming resource limitations by offloading complex computations to the cloud, thus resolving the contradiction between speed and resource availability.
3Productivity
If virtual organizations share resources, then resource utilization is improved, but management complexity increases
Solution Approach 1:
The patent implements a universal resource management platform that handles multiple functions: resource allocation, security management, task scheduling, and billing. This multi-functional platform consolidates various management tasks into a single system, enabling virtual organizations to share resources efficiently while reducing the overall management complexity through centralized orchestration and standardized interfaces.
4Reliability
If multi-cloud environments are used, then scalability and reliability are improved, but operational costs and management complexity increase
Solution Approach 1:
The patent introduces an intermediary layer (edge computing nodes and resource management platform) that mediates between multiple cloud environments and end-user applications. This intermediary abstracts the complexity of managing multiple clouds, providing unified resource allocation, consistent security policies, and simplified task scheduling across heterogeneous cloud infrastructures, thereby reducing management complexity while maintaining the reliability benefits of multi-cloud deployment.
Data Source
AI summary
A cross-domain cabin computing system and a cross-domain cabin computing method based on data resource distribution are provided, including: a cabin generation and management system obtains a data resource distribution map from a virtual data center system according to a cabin data and resource request, obtains a cross-domain resource directory from a cross-domain resource management system, and calculates a list of resource requirements of cabins; the cross-domain resource management system sends a resource request to a public cloud or a non-cloud resource to which resources belong based on the list of resource requirement of the cabins, and send information signaling that the resource request is successful to the cabin generation and management system to enable the cabin users to establish cabins based on cross-domain resources and data resources corresponding to the data resource distribution map, thereby realizing cabin computing.