四川大学学报（自然科学版）2024，Vol.61Issue(4)：134-144,11.DOI:10.19907/j.0490-6756.2024.042005
云服务推荐中基于多源特征和多任务学习的时序QoS预测
Temporal QoS prediction based on multi-source features and multitask learning in cloud service recommendation
摘要
Abstract
With the popularization of cloud computing technology,the number of cloud services is increasing exponentially,and users are no longer satisfied with functional requirements.Quality of Service(QoS)has be-come a key performance indicator for comparing the services.How to predict QoS in a dynamic and complex cloud environment in real-time and accurately,and recommend high-quality services to users has become a hot issue.Considering that the load of cloud servers,network status,and user preferences for accessing the cloud environment vary over time,this paper proposes a Temporal aware model based on Multi-Source and Multi-Task(T-MST),which can synchronously and accurately predict multiple QoS attributes.Firstly,T-MST performs feature representation on users and services,characterizes temporal features through Time2Vec,and generates multi-source feature representations by combining historical records of multiple QoS attributes.Sec-ondly,based on the sliding window,LSTM is used to perceive the temporal relationships within the window,and attention mechanism is used to refine the criticality of different time slots within the window,thereby con-structing a hidden state for the predicted time.Finally,T-MST uses a multitask prediction layer to achieve si-multaneous prediction of multiple QoS attributes,sharing upstream models and only using different perception modules in the prediction layer to improve model robustness and computational efficiency.This paper con-ducts comprehensive experimental verification based on real-world datasets,and the results show that T-MST has an average improvement of 37.53%and 20.37%in MAE in throughput and response time temporal pre-diction tasks,respectively,which is superior to existing temporal QoS prediction methods.Moreover,T-MST has higher computational efficiency and can effectively meet the demand for real-time QoS prediction.关键词
云服务/QoS预测/多源特征/多任务学习/深度学习Key words
Cloud service/QoS prediction/Multi-source features/Multitask learning/Deep learning分类
计算机与自动化引用本文复制引用
陈熳熳,王俊峰,李晓慧,余坚..云服务推荐中基于多源特征和多任务学习的时序QoS预测[J].四川大学学报（自然科学版）,2024,61(4):134-144,11.基金项目
国家自然科学基金(62101368,U2133208) （62101368,U2133208）
四川大学-泸州市人民政府战略合作项目(2022CDLZ-5) （2022CDLZ-5）
四川省重点研发项目(2022YFG0168) （2022YFG0168）
