Intelligent Data Management System for Cloud IoT Storage Optimization
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Solution Overview
Problem
Cloud computing systems face performance bottlenecks in managing and storing massive amounts of IoT data due to inefficient data storage and movement, particularly in handling structured and unstructured data from a large number of network-connected devices, leading to degraded IoT network performance.
Innovation Solution
An intelligent data management system is implemented within cloud computing environments, comprising an application server, distributed data storage, and a software platform that optimizes data movement and storage by determining data types and selecting appropriate repositories, utilizing modules for resource allocation, data analytics, storage path optimization, and profiling to enhance data handling and storage efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud computing platforms use traditional back-end storage systems for IoT data, then existing storage infrastructure can be utilized, but storage performance degrades due to lack of optimization for IoT applications
Solution Approach 1:
The patent applies local quality by creating specialized storage repositories tailored to different IoT data types. Different repositories are designed with specific characteristics optimized for particular data categories (e.g., time-series data, binary data, text data), allowing each storage location to have the optimal structure for its specific workload rather than using a uniform storage system.
Solution Approach 2:
The patent segments the storage system into multiple specialized repositories based on data types. The storage infrastructure is divided into distinct segments (repositories) that handle different types of IoT data separately, with each segment optimized for its specific data characteristics, thereby improving overall storage performance and adaptability.
2Productivity
If the number of network connected IoT devices increases, then more data can be collected and processed, but network performance decreases due to bottleneck in data upload/download
Solution Approach 1:
The patent introduces an intelligent data management system as an intermediary layer between IoT devices and the storage infrastructure. This intermediary performs data classification, routing, and optimization before data reaches the storage system, enabling efficient data handling that scales with the number of devices without creating network bottlenecks.
Solution Approach 2:
The patent applies preliminary action by classifying and preparing data for optimal storage placement before it enters the storage system. The intelligent data management system performs data type determination and repository selection in advance, reducing processing delays and network congestion by pre-organizing data flows.
3Device complexity
If data is stored in a single centralized location, then storage management is simple, but data access latency increases and system scalability is limited
Solution Approach 1:
The patent segments the centralized storage into multiple distributed repositories, each optimized for specific data types. This segmentation allows data to be stored closer to where it is accessed, reducing latency while maintaining manageable complexity through automated data routing and classification mechanisms.
Data Source
AI summary
Systems and methods are provided for implementing an intelligent data management system for data storage and data management in a cloud computing environment. For example, a system includes an application server, a distributed data storage system, and an intelligent data management system. The application server is configured to host a data processing application. The distributed data storage system is configured to store data generated by a network of devices associated with the data processing application. The intelligent data management system is configured to manage data storage operations for storing the data generated by the network of devices in the distributed data storage system. For example, the intelligent data management system is configured to determine one or more data types of the data generated by the network of devices and select one of a plurality of repositories within the distributed data storage system to store the data based on the determined data types.