Multiple Attributes QoS Prediction via Deep Neural Model with Contexts

Wu, Hao; Zhang, Zhengxin; Luo, Juanjuan; Yue, Kun; Hsu, Ching‐Hsien

doi:10.1109/tsc.2018.2859986

Cited by 73 publications

(36 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since auto-encoder generally uses one hidden-layer neural network to learn the embedding feature, Zhang et al adopt the MLP to model the nonlinear characteristics of embedding features [32], they also embeds similar neighborhood in MLP to further improve prediction accuracy [33]. Wu et al proposed a deep neural network for making QoS prediction with contextual information [34], where a deep neural network is added to the end of FM in series for prediction. Note that the network structure is similar to the work in [35], and our work is a left-right network structure to model the model the high-order and non-linear feature interaction.…”

Section: Deep Learning Based Qos Predictionmentioning

confidence: 99%

“…(11) AFM is a model that an attention part is added to FM [44]. (12) NFM is an optimized version of FM, in which a deep neural network is added to the end of FM in series for prediction [34,35]. (13) MLP is a deep model, in with embedding features are fed into the model to learn the nonlinear interaction between user and Web API and generate prediction [32].…”

Section: B Performance Comparison (Rq1)mentioning

confidence: 99%

See 1 more Smart Citation

Contexts Enhance Accuracy: On Modeling Context Aware Deep Factorization Machine for Web API QoS Prediction

et al. 2020

View full text Add to dashboard Cite

Section: Deep Learning Based Qos Predictionmentioning

confidence: 99%

Section: B Performance Comparison (Rq1)mentioning

confidence: 99%

Contexts Enhance Accuracy: On Modeling Context Aware Deep Factorization Machine for Web API QoS Prediction

et al. 2020

View full text Add to dashboard Cite

“…In such settings, collaborative approaches like Matrix Factorization (MF) are used to predict missing values in the Quality of Service (QoS) vectors of various services. Some of the main contributions include [8,21,23,26,27,30]. All these works predict missing values for Response Time (RT) and THP, under various matrix densities and dimensionalities of learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…A context-sensitive MF technique is proposed in Ref. [26], whereas [30] uses a deep neural model. All these works are evaluated using standard regression metrics and most of these use an established dataset released by Zheng [22] containing the RT and THP of 339 users and 5825 services.…”

Section: Related Workmentioning

confidence: 99%

Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach

Ateeq

Ishmanov

Afzal

et al. 2019

Sensors

View full text Add to dashboard Cite

Small-to-medium scale smart buildings are an important part of the Internet of Things (IoT). Wireless Sensor Networks (WSNs) are the major enabler for smart control in such environments. Reliability is among the key performance requirements for many loss-sensitive IoT and WSN applications, while Energy Consumption (EC) remains a primary concern in WSN design. Error-prone links, traffic intense applications, and limited physical resources make it challenging to meet these service goals—not only that these performance metrics often conflict with one another, but also require solving optimization problems, which are intrinsically NP-hard. Correctly forecasting Packet Delivery Ratio (PDR) and EC can play a significant role in different loss-sensitive application environments. With the ever-increasing availability of performance data, data-driven techniques are becoming popular in such settings. It is observed that a number of communication parameters like transmission power, packet size, etc., influence metrics like PDR and EC in diverse ways. In this work, different regression models including linear, gradient boosting, random forest, and deep learning are used for the purpose of predicting both PDR and EC based on such communication parameters. To evaluate the performance, a public dataset of the IEEE 802.15.4 network, containing measurements against more than 48,000 combinations of parameter configurations, is used. Results are evaluated using root mean square error and it turns out that deep learning achieves up to 98% accuracy for both PDR and EC predictions. These prediction results can help configure communication parameters taking into account the performance goals.

show abstract

“…This history is employed as the basis for service quality predictions. In recent years, there have been a large number of such prediction methods [3,7,8,9] but, when applying this method to predict service quality, an important premise is to ensure that the evaluation information for each QoS value submitted by users is true and reliable. Indeed, in actual environments, due to the influence of various factors, this cannot be effectively guaranteed.…”

Section: Introductionmentioning

confidence: 99%

A Personalized QoS Prediction Method for Web Services via Blockchain-Based Matrix Factorization

Cai

2019

Sensors

View full text Add to dashboard Cite

Personalized quality of service (QoS) prediction plays an important role in helping users build high-quality service-oriented systems. To obtain accurate prediction results, many approaches have been investigated in recent years. However, these approaches do not fully address untrustworthy QoS values submitted by unreliable users, leading to inaccurate predictions. To address this issue, inspired by blockchain with distributed ledger technology, distributed consensus mechanisms, encryption algorithms, etc., we propose a personalized QoS prediction method for web services that we call blockchain-based matrix factorization (BMF). We develop a user verification approach based on homomorphic hash, and use the Byzantine agreement to remove unreliable users. Then, matrix factorization is employed to improve the accuracy of predictions and we evaluate the proposed BMF on a real-world web services dataset. Experimental results show that the proposed method significantly outperforms existing approaches, making it much more effective than traditional techniques.

show abstract

Multiple Attributes QoS Prediction via Deep Neural Model with Contexts

Cited by 73 publications

References 38 publications

Contexts Enhance Accuracy: On Modeling Context Aware Deep Factorization Machine for Web API QoS Prediction

Contexts Enhance Accuracy: On Modeling Context Aware Deep Factorization Machine for Web API QoS Prediction

Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach

A Personalized QoS Prediction Method for Web Services via Blockchain-Based Matrix Factorization

Contact Info

Product

Resources

About