MEC Orchestration Platform for Edge Resource Sharing
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Solution Overview
Problem
In multi-access edge computing (MEC) environments, user equipment often faces challenges with computationally intensive workloads due to limited resources, leading to increased energy consumption and latency issues, especially for applications requiring real-time processing like autonomous vehicles and augmented reality, as offloading data to remote servers can be impractical due to distance and bandwidth constraints.
Innovation Solution
A MEC orchestration platform that aggregates resources from network operators and third-party providers to share computing resources in edge regions, enabling efficient allocation and utilization of computing capacity, reducing latency and bandwidth usage by processing data closer to the user equipment, and utilizing distributed ledgers for resource management and smart contracts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If computing resources are centralized in remote servers, then infrastructure cost is reduced, but latency increases and real-time processing capability deteriorates
Solution Approach 1:
The patent segments computing resources from centralized remote servers into distributed edge computing nodes deployed at network edges. This segmentation allows computing functions to be physically closer to user equipment, reducing latency while maintaining cost efficiency through shared infrastructure across multiple edge locations.
Solution Approach 2:
The patent transitions from a single-dimensional centralized computing model to a multi-dimensional distributed edge computing architecture. By adding the spatial dimension of distribution across multiple edge locations, the system achieves both low latency (local processing) and cost efficiency (shared infrastructure).
2Loss of time
If computing resources are distributed at network edges, then latency is reduced and real-time processing is improved, but infrastructure cost and device complexity increase
Solution Approach 1:
The patent creates universal edge computing platforms that can serve multiple functions and multiple user equipment devices. Each edge computing node provides general-purpose computing resources that can be dynamically allocated to different applications and users, spreading infrastructure costs across diverse workloads while maintaining low latency for all clients.
Solution Approach 2:
The patent merges multiple computing functions and resources into integrated edge computing nodes. By combining CPU, GPU, storage, and networking capabilities in unified edge platforms, the system reduces overall infrastructure requirements compared to having separate specialized systems, while still providing distributed low-latency processing.
3Loss of time
If user equipment processes computationally intensive workloads locally, then real-time processing capability is maintained, but energy consumption increases and battery life decreases
Solution Approach 1:
The patent introduces edge computing nodes as intermediary processing platforms between user equipment and remote servers. These intermediaries handle computationally intensive workloads that would otherwise consume excessive battery power on mobile devices, while still providing low-latency processing close to the user equipment.
Solution Approach 2:
The patent creates virtual copies of computing resources at edge locations that mirror the functionality of remote servers. Instead of running all computations locally on battery-powered devices, the system copies necessary computing capabilities to edge nodes, allowing local processing without the full energy cost of running equivalent workloads on user equipment.
4Ease of operation
If data is offloaded to remote servers for processing, then user equipment resources are conserved, but bandwidth usage increases and network congestion worsens
Solution Approach 1:
The patent segments data processing operations between user equipment, edge computing nodes, and remote servers based on computational requirements. By segmenting workloads and processing data locally at edge nodes whenever possible, the system reduces the volume of data that must traverse the network, decreasing bandwidth consumption and network congestion while maintaining ease of operation.
Data Source
AI summary
An orchestration device may receive a request to register available multi-access edge computing (MEC) resources to be shared with a network operator. For example, the available MEC resources may be provided by a provider operating a MEC host located in an edge region of a radio access network (RAN) associated with the network operator. The orchestration device may receive information related to a requested MEC session to support an application workload for a user equipment in communication with a base station located in the edge region of the RAN and assign at least a portion of the application workload to the MEC host based on a profile for the MEC host and a service level agreement specifying one or more performance requirements associated with the application workload. Accordingly, the orchestration device may cause the portion of the application workload to be transmitted to the MEC host.