Edge QoS Management via Proactive Resource Reservation
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Solution Overview
Problem
Existing edge computing systems face challenges in maintaining quality of service (QoS) and resource management as devices transition between different computing locations in mobile scenarios, leading to increased latency and reduced efficiency due to limitations in orchestration, coordination, and resource management.
Innovation Solution
The implementation of proactive resource reservation, deadline-driven allocation, speculative QoS-based allocation, and automatic QoS migration techniques to ensure timely and efficient allocation of edge computing resources, maintaining QoS levels as devices move between different computing locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If devices transition between different computing locations in mobile scenarios, then connectivity and accessibility are improved, but QoS degradation and latency increase due to limitations in orchestration and resource management
Solution Approach 1:
The patent applies preliminary action by proactively reserving resources at target edge computing nodes before device migration occurs. The system predicts future resource needs and pre-allocates computing, storage, and network resources at destination nodes, ensuring QoS is maintained during device transition without waiting for actual migration to happen.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor device location, resource usage patterns, and network conditions. This feedback enables dynamic adjustment of resource allocation and migration decisions, allowing the system to adapt to changing conditions and maintain optimal QoS during mobile scenarios.
2Productivity
If resources are allocated dynamically as devices move, then resource utilization is improved, but latency increases due to real-time allocation delays
Solution Approach 1:
The system performs resource allocation in advance based on predicted device trajectories and future resource demands. By pre-reserving resources at target locations before actual migration occurs, the system eliminates allocation delays during transition and maintains high productivity while reducing latency.
3Reliability
If existing cloud services are used for mobile devices, then service availability is maintained, but edge computing benefits such as reduced latency and increased responsiveness are lost
Solution Approach 1:
The patent segments computing services into distributed edge nodes deployed at multiple locations rather than relying on a single centralized cloud. This segmentation enables services to be delivered locally at edge nodes, maintaining service availability while significantly reducing latency and improving response time through distributed architecture.
Solution Approach 2:
The system implements local quality by deploying computing and storage resources at local edge nodes close to end devices. This allows data processing and service execution to occur locally rather than requiring remote cloud access, thereby reducing latency and improving responsiveness while maintaining service availability through distributed redundancy.
Data Source
AI summary
An architecture to perform resource management among multiple network nodes and associated resources is disclosed. Example resource management techniques include those relating to: proactive reservation of edge computing resources; deadline-driven resource allocation; speculative edge QOS pre-allocation; and automatic QoS migration across edge computing nodes. In a specific example, a technique for service migration includes: identifying a service operated with computing resources in an edge computing system, involving computing capabilities for a connected edge device with an identified service level; identifying a mobility condition for the service, based on a change in network connectivity with the connected edge device; and performing a migration of the service to another edge computing system based on the identified mobility condition, to enable the service to be continued at the second edge computing apparatus to provide computing capabilities for the connected edge device with the identified service level.