Multi-Tier Edge Deployment Architecture for Remote IoT
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Solution Overview
Problem
Centralized cloud computing systems are inadequate for managing geographically remote IoT devices due to latency issues, network connectivity challenges, and time sensitivity requirements, especially in disconnected regions, necessitating a decentralized computing solution.
Innovation Solution
Implementing a distributed computing cluster with edge devices that operate as a control plane, enabling isolated computing environments without public network access, and periodically connecting to central cloud servers for synchronization and updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a centralized cloud computing system is used to manage remote IoT devices, then infrastructure complexity is reduced, but processing latency increases and time sensitivity requirements cannot be met
Solution Approach 1:
The system segments the centralized cloud infrastructure into distributed edge computing nodes deployed at multiple geographic locations. Each edge node independently manages local IoT devices, creating regional processing centers that reduce data transmission distance and latency while maintaining overall system functionality through distributed architecture
Solution Approach 2:
The patent introduces a hierarchical multi-tier architecture that adds a new dimension to cloud computing by deploying infrastructure not only at the central cloud level but also at edge locations between devices and the central cloud. This multi-level structure enables processing to occur at multiple distances from the data source, optimizing latency while preserving infrastructure manageability
2Loss of time
If edge devices operate in isolated computing environments without public network access, then data processing time sensitivity is improved, but device connectivity and synchronization with centralized cloud are reduced
Solution Approach 1:
The system implements periodic synchronization mechanisms where edge computing nodes autonomously determine when to sync with the centralized cloud based on data changes and connectivity availability. This allows continuous local processing in isolated environments while maintaining periodic updates of configurations, metadata, and critical data with the central cloud when network conditions permit
Solution Approach 2:
Edge computing nodes are designed with autonomous capabilities to operate independently in isolated environments, making self-managed decisions about data processing, local storage, and synchronization timing. Each node monitors its own connectivity status and data state to determine optimal sync moments without requiring constant centralized control, ensuring continuous operation while maintaining cloud synchronization
3Loss of time
If a distributed computing cluster with multiple edge devices is implemented, then processing latency is reduced, but system management complexity increases
Solution Approach 1:
The patent merges the management functions of multiple distributed edge nodes into a unified hierarchical architecture where a central management platform consolidates control over distributed edge computing nodes. This allows the system to leverage distributed processing for low latency while centralizing administrative functions such as provisioning, monitoring, and coordination to simplify overall system management
Solution Approach 2:
The system introduces an intermediary management layer between the centralized cloud and edge devices that handles coordination and communication overhead. This intermediate tier abstracts the complexity of managing numerous distributed nodes from the central system, providing simplified interfaces for resource allocation, workload distribution, and synchronization while enabling low-latency local processing at edge locations
Data Source
AI summary
Techniques discussed herein relate to implementing a distributed computing cluster (the “cluster”) including a plurality of edge devices (e.g., devices individually configured to selectively execute within an isolated computing environment). Each of the edge devices of the cluster may be configured with a respective control plane computing component. A subset of the edge devices may be selected to operate in a distributed control plane of the computing cluster. Any suitable combination of the subset selected to operate in the distributed control plane can instruct remaining edge devices of the distributed computing cluster to disable at least a portion of their respective control planes. Using these techniques, the cluster's distributed control plane can be configured and modified to scale as the cluster grows. The edge devices of the control plane may selectively connect to a centralized cloud, alleviating the remaining edge devices from needless and costly connections.