Edge Node Computing Load Adjustment for Seamless User Device Handover
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Solution Overview
Problem
Mobile edge computing technologies face challenges in managing computing loads, leading to delays and traffic fluctuations, especially for high-priority users, due to insufficient edge resources and the passive nature of existing task transfer solutions.
Innovation Solution
A method that proactively adjusts computing loads by predicting incoming resource demands, preemptively unloading low-priority applications to other computing entities, such as clouds or edge stations, to ensure sufficient resources for high-priority users, thereby preventing service interruptions and maintaining user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If task transfer solutions are used to move computing tasks from overloaded servers to idle servers, then resource utilization is improved, but service delays and traffic fluctuations still occur affecting user experience
Solution Approach 1:
The system performs preliminary actions by proactively adjusting computing loads before user devices actually switch to new edge stations. It predicts upcoming switching events based on user device movement patterns and preemptively transfers computing tasks, ensuring target stations have adequate resources ready before arrival, thus eliminating service delays.
Solution Approach 2:
The system applies preliminary anti-action by anticipating and counteracting potential service interruptions before they occur. It identifies user devices that will switch stations and preemptively adjusts loads to prevent the harmful effect of resource insufficiency, ensuring continuous seamless service for VIP users.
2Adaptability or versatility
If computing loads are adjusted reactively after user devices switch to new nodes, then resource allocation flexibility is improved, but service interruptions occur affecting user experience
Solution Approach 1:
The system performs preliminary load adjustment actions before user devices switch to new edge stations. By predicting switching events based on movement patterns and proactively transferring computing tasks, it ensures resource allocation is completed in advance, maintaining both flexibility and service continuity without interruptions.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user device locations, movement patterns, and computing resource status. This real-time feedback enables the system to dynamically adjust loads proactively, maintaining service reliability while adapting to changing conditions.
3Productivity
If edge computing resources are increased to meet growing computing demands, then service capacity is improved, but resource insufficiency still occurs during peak loads
Solution Approach 1:
The system applies dynamics by enabling flexible, dynamic redistribution of computing loads across edge stations based on real-time and predicted resource demands. Instead of statically increasing resources at each station, it dynamically transfers tasks to stations with available capacity, optimizing overall system resource utilization and preventing insufficiency during peak loads.
Solution Approach 2:
The system merges computing resources across multiple edge stations into a unified pool. By coordinating load transfers between stations, it effectively combines their capacities, allowing the network to handle peak demands that exceed individual station resources while maintaining efficient utilization.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for adjusting a computing load. The method in an illustrative embodiment includes: determining a total computing power demand of at least one user device that will be switched, due to movement, to being provided a computing service by a computing node; determining an available computing power of the computing node; and if the available computing power is unable to meet the total computing power demand, by adjusting a computing load of the computing node, adjusting the available computing power before the at least one user device is switched to being provided the computing service by the computing node, so as to meet the total computing power demand.