Design and Performance Evaluation of a Hybrid Task Scheduling Strategy for E-Commerce Logistics in Cloud-Edge Collaborative Environments
DOI:
https://doi.org/10.4108/eetsis.11343Keywords:
cloud-edge collaboration, task scheduling, e-commerce logistics, reinforcement learning, resource allocationAbstract
INTRODUCTION: The rapid growth of e-commerce has led to highly concurrent and diverse logistics operations, posing significant challenges for task scheduling under dynamic network conditions and heterogeneous computing resources. Conventional cloud-centric scheduling lacks real-time responsiveness, while purely edge-based decisions suffer from limited global visibility, resulting in suboptimal performance.
OBJECTIVES: This study addresses these limitations by designing a cloud-edge collaborative hybrid scheduling method tailored for e-commerce logistics, aiming to simultaneously minimize latency, reduce energy consumption, and maximize task completion rates under fluctuating workloads.
METHODS: The proposed framework integrates three core components: task encoding to capture heterogeneity, node state prediction using lightweight temporal models, and multi-objective scheduling driven by reinforcement learning. This enables the system to adapt dynamically to changes in bandwidth, node load, and task urgency.
RESULTS: Evaluated under simulated peak and off-peak e-commerce scenarios, the method outperforms baseline approaches by reducing average task latency by 18.7%, increasing completion rate by 9.4%, and cutting system-wide energy consumption by 12.3%.
CONCLUSION: By effectively coordinating cloud and edge resources, the approach provides a robust foundation for building low-latency, energy-efficient, and reliable scheduling systems, with practical implications for warehouse automation, instant delivery networks, and other time-sensitive logistics applications.
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