基于服务负载的时序QoS预测

网络技术的发展和多接入边缘计算的兴起使得计算和网络资源的部署逐渐靠近终端.随着服务数量的增多,为了向用户更好地推荐服务,如何在复杂、动态的边缘计算环境中实时、准确地预测服务质量(quality of service,QoS)成为一项挑战.本文提出一种基于服务负载实时预测QoS的深度神经模型(QPSL),它可以为边缘计算中的QoS预测提供缺少的负载状况感知和周期感知.首先,对服务的负载状况进行特征表示,并通过时序分解模块获取时序特征.其次,将CNN和BiLSTM结合,学习潜在的时序关系,生成不同时刻的状态向量.然后,基于Attention机制为历史时刻的状态向量分配权重,从而构造未来时刻的状态向量.最后,将上下文嵌入向量与状态向量送入感知层完成实时QoS预测.基于真实的融合数据集进行了大量的实验,结果表明QPSL在响应时间和吞吐量任务上的MAE分别平均提升了10.28%和10.87%,优于现有的时间感知QoS预测方法.

Time-series QoS Prediction Based on Service Load

The advance in network technology and the rise of multi-access edge computing have led to the deployment of computation and network resources closer to the end users. As the service numbers increase, it is a challenge to predict the quality of service (QoS) in real-time and accurately in the complex and dynamic edge computing environment to better recommend services to users. In this study, a deep neural model for real-time QoS prediction based on service load (QPSL) is proposed, which can provide missing load condition awareness and cycle awareness for QoS prediction in edge computing. Firstly, the service load condition is characterized, and the features of the time-series are obtained by the time-series decomposition module. Secondly, CNN and BiLSTM are combined to learn the potential time-series relationships and generate the state vectors at different time intervals. Then, the state vectors at future time intervals are constructed by assigning weights to the historical state vectors based on the Attention mechanism. Finally, contextual embedding vectors and state vectors are fed into the perception layer to complete the real-time QoS prediction. Extensive experiments are conducted based on a real fusion dataset, and the results show that QPSL improves MAE by 10.28% and 10.87% on average for response time and throughput tasks respectively, outperforming existing time-aware QoS prediction methods.