TY - GEN
T1 - QoS Prediction via Multi-scale Feature Fusion Based on Convolutional Neural Network
AU - Xu, Hanzhi
AU - Shu, Yanjun
AU - Zhang, Zhan
AU - Zuo, Decheng
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Quality of Service (QoS) prediction is a crucial aspect in service management. However, the existing QoS prediction methods face several limitations, such as loss of information during encoding, incomplete feature extraction and neglect of the interaction between features. To this end, this paper proposes a new QoS PRediction method based on a Multi-Scale convolutional neural Network, i.e., QPRMSN. For each service invocation, we build a feature matrix that encodes invocation context and QoS characteristics by using status codes with degrees of membership. Then, a multi-scale convolutional neural network is employed to extract features that keep detailed information during deep global features mining. Moreover, we introduce attention mechanism to learn the intrinsic relationships between features to strengthen key features. Finally, QPRMSN completes the QoS prediction based on a multi-level feature matrix. Extensive experiments are conducted on a real-world dataset to evaluate the performance of QPRMSN. The experimental results demonstrate that QPRMSN outperforms the state-of-the-art QoS prediction models and is better at QoS context encoding.
AB - Quality of Service (QoS) prediction is a crucial aspect in service management. However, the existing QoS prediction methods face several limitations, such as loss of information during encoding, incomplete feature extraction and neglect of the interaction between features. To this end, this paper proposes a new QoS PRediction method based on a Multi-Scale convolutional neural Network, i.e., QPRMSN. For each service invocation, we build a feature matrix that encodes invocation context and QoS characteristics by using status codes with degrees of membership. Then, a multi-scale convolutional neural network is employed to extract features that keep detailed information during deep global features mining. Moreover, we introduce attention mechanism to learn the intrinsic relationships between features to strengthen key features. Finally, QPRMSN completes the QoS prediction based on a multi-level feature matrix. Extensive experiments are conducted on a real-world dataset to evaluate the performance of QPRMSN. The experimental results demonstrate that QPRMSN outperforms the state-of-the-art QoS prediction models and is better at QoS context encoding.
KW - Convolutional neural network
KW - QoS prediction
KW - multi-scale
UR - https://www.scopus.com/pages/publications/85178204338
U2 - 10.1007/978-3-031-48421-6_9
DO - 10.1007/978-3-031-48421-6_9
M3 - 会议稿件
AN - SCOPUS:85178204338
SN - 9783031484209
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 119
EP - 134
BT - Service-Oriented Computing - 21st International Conference, ICSOC 2023, Proceedings
A2 - Monti, Flavia
A2 - Mecella, Massimo
A2 - Rinderle-Ma, Stefanie
A2 - Ruiz Cortés, Antonio
A2 - Zheng, Zibin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st International Conference on Service-Oriented Computing, ICSOC 2023
Y2 - 28 November 2023 through 1 December 2023
ER -