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

Limin Shen,Linlin Liu,Zhen Chen,Maosheng Pan,Dianlong You,Feng Li

doi:10.1109/access.2020.3022891

Limin Shen, Linlin Liu + Show 4 more

Open Access

https://doi.org/10.1109/access.2020.3022891

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Abstract

Service-oriented computing (SOC) promises a world of cooperating services loosely connected, constructing agile Web applications in heterogeneous environments conveniently. Web application interface (API) as an emerging technique attracts more and more enterprises and organizations to publish their deep computing functionalities and big data on the Internet, Web API has become the backbone to promote the development of SOC, thus forming the prosperous Web API economy. However, the number of available Web APIs on the Internet is massive and growing constantly, which causes the Web API overload problem. Quality of service (QoS) as an indicator is able to well differentiate the quality of Web APIs and has been widely applied for high quality Web API selection. Since testing QoS for massive Web APIs is resource-consuming, and the QoS performance depends on contextual information such as network and location, hence accurate QoS prediction has become very crucial for personalized Web API recommendation and high quality Web application construction. To address the above issue, this paper presents a context aware deep factorization machine model (CADFM for short) for accurate Web API QoS prediction. Specifically, we first carry out detailed data analysis using real-world QoS dataset and discover a positive relationship between QoS and contextual information, which motivates us to incorporate beneficial contexts for enhancing QoS prediction accuracy. Then, we treat QoS prediction as a regression problem and propose a context aware CADFM framework that integrates the contextual information via embedding technique. Particularly, we adopt MF and MLP for high-order and nonlinear interaction modeling, so as to learn the complex interaction between users and Web APIs accurately. Finally, the experimental results on real-world QoS dataset demonstrate that CADFM outperforms the classic and the state-of-the-art baselines, thereby generating the most accurate QoS predictions and increasing the revenue of Web APIs recommendation.

Highlights

Service oriented computing (SOC) is an extensively accepted design paradigm for agile application development through the Internet in the form of service invocation orThe associate editor coordinating the review of this manuscript and approving it for publication was Fabrizio Messina .service composition, aiming to make application flexible and respond to business requirements quickly [1], [2]
Shen et al.: Contexts Enhance Accuracy: On Modeling CADFM for Web application interface (API) Quality of service (QoS) Prediction is evolved from the source code into a mixture of source code and third-party services, the development paradigm of software is developed from application integration within an organization to function integration and data sharing of cross-border heterogeneous systems connected by services, and the operation way of software is transformed from isolated systems to open collaborative systems composed of services
Prior efforts have demonstrated that this r(u, s) model can evaluate user-perceived QoS well, we find it only use the explicit user-Web API QoS data to make prediction, the implicit beneficial contextual information are neglected in this model

Summary

INTRODUCTION

Service oriented computing (SOC) is an extensively accepted design paradigm for agile application development through the Internet in the form of service invocation or. The main contributions of this work are: (1) We discover a positive relationship between QoS data and contextual information through qualitative analysis on real-world QoS dataset and demonstrate that users and Web APIs having the same context such as country, network condition tend to have more similar QoS performance, and vice versa, which provides the reliable evidence for us to enhance the accuracy of QoS prediction under data sparsity case by exploiting contextual information. (3) We compare our CADFM model with the state-of-theart methods and demonstrate the superiority of our model through extensive experiments on real-world QoS dataset, showing that our approach is a ble to leverage the contextual information more effectively and capture the complex interaction of users and Web APIs more accurately.

COLLABORATIVE FILTERING BASED QoS PREDICTION

FRAMEWORK AND METHODOLOGY

METHODOLOGY

EXPERIMENTS

Findings

CONCLUSION AND FUTURE WORK