Edge Computing Offloading Framework for Cellular Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently offloading computationally demanding tasks from mobile devices to edge computing resources, due to limitations in battery capacity, thermal constraints, and device size, which results in significant communication delays unsuitable for real-time applications.
Innovation Solution
A dynamic service discovery and offloading framework for edge computing based cellular network systems, where user equipment (UE) devices can discover available edge server resources, request capability information, and dynamically determine whether to offload tasks to edge servers or execute them locally based on channel conditions, network parameters, and application requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If computationally demanding tasks are offloaded to centralized cloud servers, then device computational load is reduced, but communication delay increases significantly
Solution Approach 1:
The patent transitions from centralized cloud computing to edge computing by placing computing resources at the network edge (base stations, gNodeBs) rather than in distant centralized data centers. This spatial redistribution reduces the physical distance for data transmission, thereby reducing communication delay while maintaining computational processing capabilities through distributed edge servers.
Solution Approach 2:
The patent introduces edge computing nodes as intermediary components between user devices and centralized cloud servers. These edge nodes act as local computing intermediaries that can process tasks nearby, reducing the need for direct long-distance communication with centralized clouds and thereby reducing latency while preserving computational offloading benefits.
2Loss of time
If edge computing resources are deployed closer to users, then communication delay is reduced, but network infrastructure complexity increases
Solution Approach 1:
The patent enables existing network infrastructure elements (base stations, gNodeBs) to serve multiple functions by adding edge computing capabilities to them. Rather than requiring entirely new dedicated edge computing infrastructure, existing network nodes perform both their traditional communication functions and computational offloading functions, thereby reducing overall system complexity.
Solution Approach 2:
The patent combines communication and computing functions by integrating edge computing resources with existing cellular network infrastructure. Base stations and gNodeBs are merged with computing servers to provide both wireless communication and computational processing services, reducing the need for separate dedicated edge computing infrastructure.
3Measurement precision
If dynamic service discovery is implemented, then offloading decision accuracy is improved, but discovery process complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the network provides capability information about available edge computing services to user devices. This feedback loop enables devices to make more accurate offloading decisions based on real-time network conditions and service capabilities, improving decision accuracy while the automated feedback process manages complexity.
Solution Approach 2:
The patent performs preliminary service discovery and capability information exchange before actual task offloading occurs. By pre-establishing knowledge of available edge services and their capabilities, the system prepares necessary information in advance, enabling more accurate offloading decisions without increasing operational complexity during task execution.
Data Source
AI summary
A user equipment (UE) or other device performs service discovery of edge computing resources in a cellular network system and dynamic offloading of UE application tasks to discovered edge computing resources. As part of the discovery process, the device (e.g., the UE) may request edge server site capability information. When performing dynamic offloading, the UE may obtain (collect and/or receive) information regarding channel conditions, cellular network parameters or application requirements and dynamically determine whether a task of the application executing on the UE should be offloaded to an edge server or executed locally on the UE. In making decisions between offloaded or local execution, the UE may use a utility function that takes into account factors such as relative differences in application latency, energy consumption and offloading cost.