LAN Local Cloud Controller Failover for Low-Latency Continuity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current devices used for immersive technologies face challenges with processing capacity, storage capacity, energy consumption, and weight, leading to uncomfortable user experiences and high costs due to the need for heavy hardware and high processing power, while edge computing solutions struggle with reliability and computational latencies.
Innovation Solution
A nonstop controller system is implemented within a local area network (LAN) to distribute computational tasks across multiple devices, ensuring continuous service by synchronizing active and available controllers to maintain a reliable local cloud environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If computational tasks are performed at remote data centers or cloud servers, then processing capacity and storage capacity are improved, but network latency increases and bandwidth requirements increase
Solution Approach 1:
The system segments the cloud computing function into distributed edge nodes deployed at network edges closer to users. Each edge node handles local computational tasks, dividing the monolithic remote cloud into multiple geographically distributed segments that reduce network latency while maintaining processing capacity.
Solution Approach 2:
The system transitions from a single centralized cloud dimension to a multi-dimensional architecture where computing resources are distributed across multiple spatial locations (edge nodes) and hierarchical levels (local, regional, global), adding spatial distribution as a new dimension to resolve the latency-capacity tradeoff.
2Loss of time
If edge computing is used to bring computing power closer to users, then network latency is reduced and bandwidth requirements are reduced, but reliability decreases due to potential controller unresponsiveness
Solution Approach 1:
The system performs preliminary actions by pre-establishing multiple standby edge controllers and pre-configuring failover mechanisms. When the active controller becomes unresponsive, standby controllers are already positioned and configured to immediately take over, ensuring service continuity without interruption.
Solution Approach 2:
The system provides beforehand cushioning by implementing redundant controller instances and automatic failover protocols. This cushioning mechanism protects against controller failures by having backup resources ready in advance, absorbing the impact of potential unresponsiveness and maintaining reliable service.
3Reliability
If multiple controllers are provided for failover, then reliability is improved, but device complexity increases due to recruitment and task assignment processes
Solution Approach 1:
The system implements self-service by enabling edge controllers to automatically perform health checks, detect failures, and trigger failover without external intervention. The controllers autonomously manage their own state, monitor each other, and execute takeover procedures, reducing the need for complex external coordination mechanisms.
Solution Approach 2:
The system uses feedback mechanisms where controllers continuously exchange heartbeats and status information. This feedback loop enables automatic detection of unresponsive controllers and triggers appropriate failover actions, simplifying the coordination complexity through decentralized, rule-based responses to system state changes.
4Power
If high-end hardware devices are used for immersive technologies, then processing capacity and storage capacity are improved, but device weight increases and cost increases
Solution Approach 1:
The system extracts heavy processing and storage functions from the user device and relocates them to edge servers. The user device retains only lightweight client functionality for rendering and interaction, while computationally intensive tasks such as physics simulation, AI processing, and 3D rendering are performed remotely at the edge, dramatically reducing device weight.
Solution Approach 2:
The system implements universality by creating a multi-functional architecture where edge servers provide multiple services (rendering, physics, AI, streaming) to multiple user devices simultaneously. This allows a single lightweight device to access high-end processing capabilities shared across the network, eliminating the need for each device to have dedicated high-end hardware.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method is performed by a network node connected to one or more additional network nodes via a local area network (LAN). The network node and the one or more additional network nodes form a cloud. The method includes setting a first timer to a first value, the first timer being associated with determining whether one of the one or more additional network nodes is an active controller in the cloud. The method includes starting the first timer. The method includes determining, before the first timer expires, whether a first message that indicates the one of the one or more additional network nodes is the active controller is received. Based on determining the first message is received before the first timer expires, the method includes synchronizing with the one of the one or more additional network nodes that is the active controller, and restarting the first timer.