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Xia H, Dong Q, Zheng J, Chen Y, Gao C, Wang Z. A QoS Prediction Approach Based on Truncated Nuclear Norm Low-Rank Tensor Completion. SENSORS (BASEL, SWITZERLAND) 2022; 22:6266. [PMID: 36016026 PMCID: PMC9414826 DOI: 10.3390/s22166266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/05/2022] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
Abstract
With the rise of mobile edge computing (MEC), mobile services with the same or similar functions are gradually increasing. Usually, Quality of Service (QoS) has become an indicator to measure high-quality services. In the real MEC service invocation environment, due to time and network instability factors, users' QoS data feedback results are limited. Therefore, effectively predicting the Qos value to provide users with high-quality services has become a key issue. In this paper, we propose a truncated nuclear norm Low-rank Tensor Completion method for the QoS data prediction. This method represents complex multivariate QoS data by constructing tensors. Furthermore, the truncated nuclear norm is introduced in the QoS data tensor completion in order to mine the correlation between QoS data and improve the prediction accuracy. At the same time, the general rate parameter is introduced to control the truncation degree of tensor mode. Finally, the prediction approximate tensor is obtained by the Alternating Direction Multiplier Method iterative optimization algorithm. Numerical experiments are conducted based on the public QoS dataset WS-Dream. The results indicate that our QoS prediction method has better prediction accuracy than other methods under different missing density QoS data.
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Affiliation(s)
- Hong Xia
- School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an 710121, China
- Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an 710121, China
| | - Qingyi Dong
- School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
| | - Jiahao Zheng
- School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
| | - Yanping Chen
- School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an 710121, China
- Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an 710121, China
| | - Cong Gao
- School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an 710121, China
- Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an 710121, China
| | - Zhongmin Wang
- School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an 710121, China
- Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an 710121, China
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A hybrid tensor factorization approach for QoS prediction in time-aware mobile edge computing. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02851-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Luo X, Wu H, Wang Z, Wang J, Meng D. A Novel Approach to Large-Scale Dynamically Weighted Directed Network Representation. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021; PP:9756-9773. [PMID: 34898429 DOI: 10.1109/tpami.2021.3132503] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A dynamically weighted directed network (DWDN) is frequently encountered in various big data-related applications like a terminal interaction pattern analysis system (TIPAS) concerned in this study. It consists of large-scale dynamic interactions among numerous nodes. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant DWDN High Dimensional and Incomplete (HDI). An HDI DWDN, in spite of its incompleteness, contains rich knowledge regarding involved nodes various behavior patterns. To extract such knowledge from an HDI DWDN, this paper proposes a novel Alternating direction method of multipliers (ADMM)-based Nonnegative Latent-factorization of Tensors (ANLT) model. It adopts three-fold ideas: a) building a data density-oriented augmented Lagrangian function for efficiently handling an HDI tensors incompleteness and nonnegativity; b) splitting the optimization task in each iteration into an elaborately designed subtask series where each one is solved based on the previously solved ones following the ADMM principle to achieve fast convergence; and c) theoretically proving that its convergence is guaranteed with its efficient learning scheme. Experimental results on six DWDNs from real applications demonstrate that the proposed ANLT outperforms state-of-the-art models significantly in both computational efficiency and prediction accuracy.
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Jing P, Li Y, Li X, Wu Y, Su Y. Joint nuclear- and ℓ2,1-norm regularized heterogeneous tensor decomposition for robust classification. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.08.112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Chen H, Deng L, Qu Z, Liang L, Yan T, Xie Y, Li G. Tensor train decomposition for solving large-scale linear equations. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.08.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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