Edge Computing Task Offloading via Intelligent Terminal Participation
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Solution Overview
Problem
The 5G communication network faces challenges with increased latency and network congestion due to the centralized computing in the Core Network, which can be exacerbated by sudden increases in user demand, and existing edge computing solutions do not fully utilize the computing power of intelligent terminals.
Innovation Solution
A method and system for incorporating intelligent terminals into edge computing by determining their availability and assigning edge computing tasks based on resource load conditions, establishing secure connections, and managing charging and resource sharing to reduce the load on the Multi-Access Edge Computing (MEC) server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all computing and control is completed by the Core Network, then centralized management is achieved, but latency increases and millisecond-level latency requirements cannot be met
Solution Approach 1:
The patent segments the computing function into two parts: the Core Network handles centralized management and control plane functions, while the MEC server handles user plane computing and data processing. This segmentation allows latency-sensitive operations to be performed locally at the edge, reducing latency while maintaining centralized management for non-time-critical functions.
Solution Approach 2:
The patent introduces a new spatial dimension by deploying MEC servers at the network edge (base station side) rather than solely in the core network. This dimensional change creates a hierarchical computing structure where local edge computing handles time-sensitive tasks and the core network handles management tasks, resolving the latency contradiction.
2Loss of time
If MEC server is deployed to provide local computing, then latency is reduced and service capacity is improved, but additional investment cost and deployment complexity increase
Solution Approach 1:
The MEC server is designed to perform multiple functions: it provides local computing for latency-sensitive applications, acts as a caching server for frequently accessed data, and serves as a gateway between the core network and user equipment. This multi-functionality reduces the need for separate dedicated devices, thereby reducing deployment complexity and investment cost.
Solution Approach 2:
The MEC server autonomously manages local computing tasks, caches data locally, and makes decisions about which computations to perform locally versus which to forward to the core network. This self-service capability reduces the operational complexity and management overhead, making deployment more feasible.
3Productivity
If MEC server computing power is increased to handle sudden user demand, then service capacity is improved, but cost increases and capacity limits still cause network congestion during peak demand
Solution Approach 1:
The system dynamically adjusts the computing workload distribution between the MEC server and the core network based on real-time demand conditions. When the MEC server experiences high load, it can offload tasks to the core network; when demand is low, it handles tasks locally. This dynamic adjustment allows the system to handle peak demand without permanently over-provisioning resources, reducing cost while maintaining service capacity.
Solution Approach 2:
The MEC server acts as an intermediary between the user equipment and the core network, absorbing peak demand locally and filtering out non-critical traffic before it reaches the core network. This intermediary function reduces network congestion and allows the system to handle sudden user demand without requiring proportional increases in core network capacity, thereby reducing cost.
Data Source
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AI summary
Provided by the present application are a method, device and system for implementing edge computing, the method comprising: determining an intelligent terminal participating in edge computing when the load of one or more resources of a multi-access edge computing (MEC) server meets a preset condition; for each intelligent terminal participating in the edge computing, assigning some edge computing tasks of the MEC server to the intelligent terminal participating in edge computing, and issuing the assigned edge computing tasks to the intelligent terminal participating in the edge computing; and receiving processing results of the assigned edge computing tasks sent by the intelligent terminal participating in the edge computing, and sending to a billing network element information about the participation in the MEC by the intelligent terminal participating in the edge computing.