Edge Computing Resource Allocation via Topology and Congestion
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Solution Overview
Problem
Existing edge computing technologies face challenges such as high latency, network bandwidth limitations, and exposure of personal information due to centralized cloud computing, necessitating improved resource allocation methods for intelligent edge services with AI modules in edge-computing environments.
Innovation Solution
A method for resource allocation in edge-computing environments that selects a worker server based on input/output congestion levels and allocates resources based on topology information, ensuring high bandwidth and efficient service execution by configuring virtual environments and utilizing specialized connection relationships like NVLink.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If centralized cloud computing is used, then computing power is centralized, but end-to-end latency increases due to long distance between device and server
Solution Approach 1:
The patent segments the centralized cloud computing system into distributed edge computing nodes deployed at multiple locations closer to end devices. This segmentation allows computing tasks to be executed locally at edge nodes rather than requiring all data to travel to a centralized remote server, thereby reducing latency while maintaining distributed computing capability.
Solution Approach 2:
The patent introduces edge computing nodes as intermediary components between end devices and centralized cloud servers. These edge nodes act as mediators that perform local processing and caching functions, reducing the distance data must travel and minimizing latency while still enabling connectivity to centralized resources when needed.
2Quantity of substance
If data amount increases rapidly, then more data can be processed, but network bandwidth is limited
Solution Approach 1:
The patent implements preliminary data processing and filtering at edge computing nodes before data is transmitted to centralized servers. Edge nodes perform local analytics, aggregation, and preprocessing operations that reduce the volume of data requiring network transmission, thereby managing bandwidth constraints while still processing large quantities of data through distributed computation.
Solution Approach 2:
The patent applies local quality optimization by enabling different processing capabilities at different locations. Edge nodes closer to data sources handle data-intensive operations locally, while centralized servers provide sophisticated computing resources for tasks requiring greater computational power. This differentiated approach optimizes bandwidth utilization by processing data where it is most efficient.
3Loss of time
If existing edge computing is used, then latency is reduced, but service execution performance is insufficient for computing-intensive and I/O-intensive workloads
Solution Approach 1:
The patent changes key parameters of resource allocation by considering multiple factors including I/O congestion levels, network bandwidth availability, storage performance characteristics, and computational resource capacity. The system dynamically adjusts resource distribution parameters to match workload requirements, ensuring optimal performance for both computing-intensive and I/O-intensive tasks while maintaining low latency.
Solution Approach 2:
The patent implements dynamic resource allocation and task scheduling that adapts to changing system conditions in real-time. The system continuously monitors I/O congestion, network bandwidth availability, and workload characteristics, then dynamically adjusts resource assignment to optimize service execution performance. This dynamic approach allows the system to maintain high performance across varying operational conditions.
Data Source
AI summary
Disclosed herein is a method for resource allocation in an edge-computing environment. The method includes receiving a request for an intelligent edge service, selecting the worker server to execute the service based on an input/output congestion level, allocating resources based on topology information of the worker server, and configuring a virtual environment based on the allocated resources.