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Design of a Low-latency Multi-source Data Scheduling Algorithm for a 5G Environment.

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Title: Design of a Low-latency Multi-source Data Scheduling Algorithm for a 5G Environment.
Authors: Zhou, JiaLi, Liu, Yuecen
Source: Journal of ICT Standardization; 2025, Vol. 13 Issue 2, p111-138, 28p
Subject Terms: 5G networks, ALGORITHMS, COMMUNICATION, COMPUTER scheduling, COMPUTER science
Abstract: Aimed at the problems of high delay and low resource allocation efficiency of multi-source heterogeneous data task scheduling in 5G edge computing environment, this paper designs a multi-source data scheduling algorithm framework for low-latency optimization. An end-edge-cloud cooperative system model is constructed, and a set of dynamic priority scheduling strategies is proposed based on the task's directed acyclic graph (DAG) graph to express the inter-task data dependency relationships, and the task scheduling order is adjusted in real time by fusing the task tightness urgency, the resource pressure and the network state changes. In order to improve the stability of the system under high load, a multi-dimensional load evaluation mechanism and a granularity-adaptive task partitioning and merging method are introduced, and a cache hit-aware resource allocation function and an edge node cache replacement strategy are designed. In addition, a QoS guarantee mechanism and a network state-aware feedback module are constructed to realize dynamic correction of task scheduling accuracy under delay constraints. Multiple rounds of comparison experiments are carried out in the simulation platform, and the results show that this paper's algorithm can control the average task completion delay within 45 ms under medium-high load conditions, significantly reducing the critical path delay, stabilizing the QoS compliance rate to more than 94%, increasing the resource utilization rate to 87.5%, and achieving a scheduling hit rate of 92.4%. The above results verify the algorithm's low latency control capability and system resource synergy in dynamic task environments, with good engineering adaptability, suitable for edge intelligent application deployment with high real-time requirements in 5G scenarios. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Aimed at the problems of high delay and low resource allocation efficiency of multi-source heterogeneous data task scheduling in 5G edge computing environment, this paper designs a multi-source data scheduling algorithm framework for low-latency optimization. An end-edge-cloud cooperative system model is constructed, and a set of dynamic priority scheduling strategies is proposed based on the task's directed acyclic graph (DAG) graph to express the inter-task data dependency relationships, and the task scheduling order is adjusted in real time by fusing the task tightness urgency, the resource pressure and the network state changes. In order to improve the stability of the system under high load, a multi-dimensional load evaluation mechanism and a granularity-adaptive task partitioning and merging method are introduced, and a cache hit-aware resource allocation function and an edge node cache replacement strategy are designed. In addition, a QoS guarantee mechanism and a network state-aware feedback module are constructed to realize dynamic correction of task scheduling accuracy under delay constraints. Multiple rounds of comparison experiments are carried out in the simulation platform, and the results show that this paper's algorithm can control the average task completion delay within 45 ms under medium-high load conditions, significantly reducing the critical path delay, stabilizing the QoS compliance rate to more than 94%, increasing the resource utilization rate to 87.5%, and achieving a scheduling hit rate of 92.4%. The above results verify the algorithm's low latency control capability and system resource synergy in dynamic task environments, with good engineering adaptability, suitable for edge intelligent application deployment with high real-time requirements in 5G scenarios. [ABSTRACT FROM AUTHOR]
ISSN:2245800X
DOI:10.13052/jicts2245-800X.1322