Segregated Video Storage and Retrieval Servers for Network Scalability
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Solution Overview
Problem
There is a lack of scalable solutions for high-throughput camera provisioning and event recognition in real-world environments, particularly for systems that need to manage millions of video camera devices and users, while overcoming network impairments and ensuring user convenience through real-time notifications and secure data access.
Innovation Solution
A system utilizing modern cloud computing technologies, including automated service provisioning, virtual machine migration, and RESTful APIs, to create a scalable video cloud service that segregates retrieval and storage servers, employs 'heartbeat' messages for camera status monitoring, and uses encryption and time-expiring tokens for security, allowing for real-time event detection and notification across millions of cameras and users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If video data is stored on the same server that retrieves and streams it, then system complexity is reduced, but network throughput and scalability deteriorate due to server load bottlenecks
Solution Approach 1:
The patent divides the video server infrastructure into separate functional components: video storage servers dedicated to storing video data, and video retrieval servers dedicated to retrieving and streaming video data to clients. This segmentation allows each server type to be optimized for its specific function and enables independent scaling of storage and retrieval capacities without mutual interference, thereby resolving the contradiction between system complexity and network throughput.
Solution Approach 2:
The patent introduces a video retrieval server as an intermediary component between the video storage server and the client device. The retrieval server receives client requests, retrieves video data from storage servers, and streams it to clients, thereby decoupling the storage and retrieval functions. This intermediary architecture prevents storage servers from becoming bottlenecks for retrieval operations, improving overall network throughput while maintaining manageable system complexity through clear functional separation.
2Adaptability or versatility
If the system supports millions of camera devices and users, then system versatility and coverage improve, but server load and network resource requirements worsen
Solution Approach 1:
The patent segments the video server infrastructure into multiple specialized servers (storage servers and retrieval servers) that can be independently scaled. This allows the system to accommodate millions of camera devices and users by adding more storage servers for capacity expansion and more retrieval servers for handling increased request volumes, without each individual server becoming overwhelmed by the total system load.
Solution Approach 2:
The patent creates a modular video server architecture where storage servers and retrieval servers can be replicated and distributed across multiple nodes. Each server type performs its specific function universally across the entire system, allowing the infrastructure to scale horizontally to support millions of devices while distributing the server load across numerous individual servers rather than concentrating it on a few overloaded machines.
3Device complexity
If video data is retrieved directly from storage servers by clients, then the number of server components is reduced, but network efficiency deteriorates due to direct peer-to-peer traffic patterns
Solution Approach 1:
The patent introduces video retrieval servers as intermediary components that mediate between video storage servers and client devices. These retrieval servers receive retrieval requests from clients, fetch video data from storage servers, and stream it to clients. This intermediary architecture optimizes network efficiency by establishing dedicated retrieval pathways and reducing direct peer-to-peer traffic patterns, thereby minimizing network resource conflicts and improving overall network throughput despite the increased number of server components.
Data Source
AI summary
The invention is based, in part, on a system and method designed to be able to easily and automatically scale up to millions of cameras and users. To do this, this discourse teaches use of modern cloud computing technology, including automated service provisioning, automated virtual machine migration services, RESTful API, and various firewall traversing methods to facilitate the scaling process. Moreover, the system and method described herein teaches scalable cloud solutions providing for higher though-put camera provisioning and event recognition. The network may segregate the retrieval server from the storage server, and by doing so, minimizing the load on any one server and improving network efficiency and scalability