MEC Edge Storage Segmentation for Data Retrieval Latency
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Solution Overview
Problem
Current Multi-Access Edge Computing (MEC) systems face challenges in retrieving real-time device and network information due to the large volume of data stored in centralized repositories, leading to latency issues that hinder dynamic optimization and scaling of MEC applications.
Innovation Solution
Devices are enabled to send diagnostic information and device data directly to the MEC platform, allowing for real-time storage and processing locally, bypassing the need for data retrieval from cloud or provider networks, thus enabling immediate access and optimization of MEC services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If device information is stored in centralized cloud repositories, then data storage capacity is sufficient, but data retrieval latency increases
Solution Approach 1:
The patent segments the centralized cloud storage system into distributed edge storage nodes deployed at multiple network edges. Each edge node stores local device information, enabling regional data access without querying the central cloud repository. This segmentation resolves the contradiction by providing sufficient total storage capacity across distributed nodes while reducing retrieval latency through localized access.
Solution Approach 2:
The patent introduces a spatial dimension to data storage by distributing repositories across multiple geographic locations at network edges rather than concentrating them in a single cloud center. This dimensional transformation allows devices to access nearby edge repositories with lower latency while the collective distributed storage maintains sufficient total capacity.
2Loss of information
If device information is constantly retrieved from centralized repositories, then information availability is maintained, but network bandwidth consumption increases
Solution Approach 1:
The patent implements preliminary action by pre-populating distributed edge repositories with device information before it is needed for MEC optimization. Edge nodes proactively collect and store device data locally, so when MEC applications require this information, it is already available at the edge without requiring real-time queries to the central cloud, thus maintaining information availability while reducing bandwidth consumption.
Solution Approach 2:
The patent introduces distributed edge repositories as intermediary layers between devices and the central cloud. These intermediaries cache device information locally, allowing MEC platforms to query edge nodes instead of directly accessing centralized cloud repositories. This intermediary approach maintains information availability for MEC optimization while significantly reducing network bandwidth consumption by avoiding repeated cloud queries.
3Measurement precision
If real-time device data is collected from all devices, then service optimization accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies local quality by enabling each distributed edge repository to independently collect, store, and manage device information relevant to its local region. Each edge node tailors its data collection and storage capabilities to local MEC optimization needs rather than implementing a uniform complex centralized system. This localizes the complexity management while maintaining high measurement precision for service optimization through relevant local device data.
Data Source
AI summary
A multi-access edge computing (MEC) platform may receive an indication that a user device has downloaded a MEC application client associated with a MEC application and may send, to the user device, instructions to install a device client. The device client may transmit device information associated with the user device to the MEC platform. The MEC platform may receive the device information associated with the user device and determine, based on the received device information, performance information associated with the MEC application.