End-to-End DNN Reconfiguration for Mobile Edge Computing
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Solution Overview
Problem
The complexity of managing edge computing resources is increased by device mobility in wireless networks, as UEs move between coverage areas with and without edge compute servers, requiring dynamic redirection of application processing.
Innovation Solution
Adapting end-to-end deep neural networks (DNNs) to dynamically reconfigure based on changes in edge compute server participation, optimizing processing for edge or cloud-based computing modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the wireless network dynamically redirects application processing between edge compute servers and cloud-based services to accommodate device mobility, then the system maintains service availability, but the complexity of managing edge computing resources increases
Solution Approach 1:
The patent implements dynamic DNN adaptation where the neural network configuration is automatically adjusted based on real-time participation mode changes of edge compute servers. The system transitions from static edge computing configurations to dynamic reconfiguration, allowing the DNN architecture, layers, and parameters to adapt automatically when UEs move between coverage areas with and without ECS support, thereby maintaining service availability while reducing management complexity
Solution Approach 2:
The system changes key parameters of the DNN including architecture, number of layers, and computational parameters based on the participation mode of edge compute servers. When a UE moves from an area with ECS support to without, the system automatically modifies DNN parameters to transition from edge-based processing to cloud-based processing, and vice versa, enabling seamless service continuity without manual intervention
2Loss of time
If the system uses edge compute servers for application processing to reduce data transfer latency, then the network response time improves, but the system requires dynamic redirection when UEs move between coverage areas
Solution Approach 1:
The system enables self-service through automatic DNN reconfiguration triggered by participation mode changes of edge compute servers. When UEs move between coverage areas, the system automatically detects the change in ECS availability and reconfigures the appropriate DNNs without requiring manual intervention or complex redirection protocols, thereby maintaining low latency while simplifying the redirection process
3Power
If the wireless network incorporates additional computing devices and data storage resources to accommodate increased data usage, then the computing power increases, but the complexity of managing and redirecting application processing increases
Solution Approach 1:
The patent creates a universal DNN framework that can operate in multiple modes (edge-based and cloud-based processing) through a single adaptable architecture. The same DNN infrastructure serves both edge computing scenarios with low latency requirements and cloud computing scenarios with higher latency tolerance, eliminating the need for separate management systems for different computing modes and reducing overall system complexity
Data Source
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AI summary
Techniques and apparatuses are described for adapting an end-to-end, E2E, machine-learning, ML, configuration for processing communications transferred through an E2E communication. A network entity directs (805) a user equipment (UE) and a base station participating in the E2E communication to implement the E2E communication by forming at least a portion of an E2E deep neural network, DNN, based on a first E2E ML configuration. The network entity determines (815) to update the first E2E ML configuration based on a change in a participation mode of an edge compute server (ECS) in the E2E communication. The network entity identifies (820) a second E2E ML configuration based on the change in participation mode and directs (825) the UE or the base station to update the portion of the E2E DNN using the second E2E ML configuration.