GAIN-QoS: A Novel QoS Prediction Model for Edge Computing

Authors

  • Jiwon Choi Department of Software Engineering, Jeonbuk National University, Korea
  • Jaewook Lee Department of Software Engineering, Jeonbuk National University, Korea
  • Duksan Ryu Department of Software Engineering, Jeonbuk National University, Korea
  • Suntae Kim Department of Software Engineering, Jeonbuk National University, Korea
  • Jongmoon Baik School of Computing, Korea Advanced Institute of Science and Technology, Korea

DOI:

https://doi.org/10.13052/jwe1540-9589.2112

Keywords:

Edge computing, Service recommendation, QoS prediction, Cold-start problem

Abstract

With recent increases in the number of network-connected devices, the number of edge computing services that provide similar functions has increased. Therefore, it is important to recommend an optimal edge computing service, based on quality-of-service (QoS). However, in the real world, there is a cold-start problem in QoS data: highly sparse invocation. Therefore, it is difficult to recommend a suitable service to the user. Deep learning techniques were applied to address this problem, or context information was used to extract deep features between users and services. However, edge computing environment has not been considered in previous studies. Our goal is to predict the QoS values in real edge computing environments with improved accuracy. To this end, we propose a GAIN-QoS technique. It clusters services based on their location information, calculates the distance between services and users in each cluster, and brings the QoS values of users within a certain distance. We apply a Generative Adversarial Imputation Nets (GAIN) model and perform QoS prediction based on this reconstructed user service invocation matrix. When the density is low, GAIN-QoS shows superior performance to other techniques. In addition, the distance between the service and user slightly affects performance. Thus, compared to other methods, the proposed method can significantly improve the accuracy of QoS prediction for edge computing, which suffers from cold-start problem.

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Author Biographies

Jiwon Choi, Department of Software Engineering, Jeonbuk National University, Korea

Jiwon Choi received the B.S degree in Software Engineering from Jeonbuk National University in 2020. She is currently pursuing the M.S degree in Software Engineering, Jeonbuk National University. Her research areas include software defect prediction, deep learning, edge computing QoS prediction, and software reliability engineering.

Jaewook Lee, Department of Software Engineering, Jeonbuk National University, Korea

Jaewook Lee is currently pursuing the B.S degree in Software Engineering from Jeonbuk National University. His research areas include software defect prediction, deep learning, optimization, and edge computing QoS prediction.

Duksan Ryu, Department of Software Engineering, Jeonbuk National University, Korea

Duksan Ryu earned a bachelor’s degree in computer science from Hanyang University in 1999 and a Master’s dual degree in software engineering from KAIST and Carnegie Mellon University in 2012. He received his Ph.D. degree in school of computing from KAIST in 2016. His research areas include software analytics based on AI, software defect prediction, mining software repositories, and software reliability engineering. He is currently an assistant professor in software engineering department at Jeonbuk National University.

Suntae Kim, Department of Software Engineering, Jeonbuk National University, Korea

Suntae Kim is a professor of the Department of Software Engineering at Jeonbuk National University. He received Ph.D. Degree in Computer Science & Engineering from Sogang University in 2010. His research areas include Blockchain, Software Engineering and Artificial Intelligence.

Jongmoon Baik, School of Computing, Korea Advanced Institute of Science and Technology, Korea

Jongmoon Baik received his B.S. degree in computer science and statistics from Chosun University in 1993. He received his M.S. degree and Ph.D. degree in computer science from University of Southern California in 1996 and 2000 respectively. He worked as a principal research scientist at Software and Systems Engineering Research Laboratory, Motorola Labs, where he was responsible for leading many software quality improvement initiatives. His research activity and interest are focused on software six sigma, software reliability & safety, and software process improvement. Currently, he is an associate professor in school of computing at Korea Advanced Institute of Science and Technology (KAIST). He is a member of the IEEE.

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Published

2021-11-28

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