Work

A Context-Aware Edge-Cloud Collaboration Framework for QoS Prediction

Year: 2024

Type: article

Abstract: The rapid growth of online services has led to the emergence of many with similar functionalities, making it necessary to predict their non-functional attributes, namely quality of service (QoS). Traditional QoS prediction approaches require users to upload their QoS data to the cloud for centralized training, leading to high user data upload latency. With the help of edge computing, users can upload data to edge servers (ESs) adjacent to them for training, reducing the upload latency. However, shallow models like matrix factorization (MF) are still used, which cannot sufficiently extract context features, resulting in low prediction accuracy. In this paper, we propose a context-aware edge-cloud collaboration framework for QoS prediction, named CQEC. Specially, to reduce the users upload latency, a distributed model training algorithm is designed with the collaboration of ESs and cloud. Furthermore, a context-aware prediction model based on convolutional neural network (CNN) and integrating attention mechanism is proposed to improve the performance. Experiments based on real-world dataset demonstrate that COEC outperforms the baselines.

Source: Tsinghua Science & Technology

DOI: 10.26599/tst.2024.9010027

Authors: Yong Cheng, Weihao Cao, Hao Fang, Shaobo Zang

Institution: Nanjing University of Information Science and Technology

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: National Natural Science Foundation of China