Deep Learning to Hash for Time-Aware QoS Prediction Based on VQ-VAE

Lingzhen Kong, Xiyuan Hu, Lianyong Qi, Xiaolong Xu, Yiwen Zhang, Lina Yao, Xuyun Zhang

Abstract: In mobile edge computing (MEC) environments, with the increase in the number of web services possessing the same or similar functions, the prediction of nonfunctional quality of service (QoS) indicators turns increasingly vital in satisfying the diverse needs of different users. Despite the significant achievements of QoS prediction approaches such as collaborative filtering (CF), they often fail to obtain informative representations due to complex user-service-time QoS data. Moreover, changing network conditions and overlooked temporal factors lead to reduced accuracy. In response to these challenges, this paper proposes a novel deep learning to hash method for time-aware QoS prediction built on a VQ-VAE (named Pred$_{QoS}$QoS). In the proposed Pred$_{QoS}$QoS, inspired by advanced CF approaches, we make QoS predictions at a specific moment for the target user through its similar timeslots. Specifically, we train a codebook to discretize the continuous latent representation obtained by the encoder based on vector quantization (VQ), which is motivated by the generative vector-quantized variational autoencoder (VQ-VAE) model, allowing us to derive compact binary codes representing the QoS data. Then, the similarities between QoS data are determined, helping to make effective and efficient predictions. Furthermore, the time-aware Pred$_{QoS}$QoS approach incorporates a temporal factor by training on multiple QoS data (including the user, service, and time dimensions). As a result, the discrete hash codes (i.e., QoS data representations) derived from the encoder can fully uncover the time factor’s dynamic impact on the QoS data, thereby yielding significantly improved prediction performance. In summary, the Pred$_{QoS}$QoS approach learns compact hash codes for original QoS data while taking time into account, enabling accurate predictions to be produced through similar timeslots for the target users. Finally, comprehensive experiments carried out on the real-world WS-DREAM dataset affirm the exceptional performance of the Pred$_{QoS}$QoS.

External IDs:dblp:journals/tsc/KongHQXZYZ25