TY - GEN
T1 - Context-aware multi-QoS prediction for services in mobile edge computing
AU - Liu, Zhizhong
AU - Sheng, Quan Z.
AU - Zhang, Wei Emma
AU - Chu, Dianhui
AU - Xu, Xiaofei
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Mobile edge computing (MEC) allows the use of services with low latency, location awareness and mobility support to overcome the disadvantages of cloud computing, and has gained a considerable momentum recently. However, Quality of Services (QoS) of MEC services are changing frequently, resulting in failures in QoS-aware service applications such as composition and recommendation. Therefore, it becomes critical to develop novel techniques that can accurately predict the QoS of MEC services to avoid such failures. In this paper, we leverage the QoS attributes and three important contextual factors to perform the prediction, as they are highly influential to the QoS of MEC services. Specifically, we propose a context-aware multi-QoS prediction method for services in MEC. We first propose an improved artificial bee colony algorithm (ABC) to optimize the support vector machine (SVM), then we apply the optimized support vector machine to predict the workload of MEC services. Finally, according to the predicted workload and other task-related contextual factors, we predict the multi-QoS of services based on the improved Case-Based Reasoning (CBR). Extensive experiments are conducted to show the effectiveness of our proposed approach.
AB - Mobile edge computing (MEC) allows the use of services with low latency, location awareness and mobility support to overcome the disadvantages of cloud computing, and has gained a considerable momentum recently. However, Quality of Services (QoS) of MEC services are changing frequently, resulting in failures in QoS-aware service applications such as composition and recommendation. Therefore, it becomes critical to develop novel techniques that can accurately predict the QoS of MEC services to avoid such failures. In this paper, we leverage the QoS attributes and three important contextual factors to perform the prediction, as they are highly influential to the QoS of MEC services. Specifically, we propose a context-aware multi-QoS prediction method for services in MEC. We first propose an improved artificial bee colony algorithm (ABC) to optimize the support vector machine (SVM), then we apply the optimized support vector machine to predict the workload of MEC services. Finally, according to the predicted workload and other task-related contextual factors, we predict the multi-QoS of services based on the improved Case-Based Reasoning (CBR). Extensive experiments are conducted to show the effectiveness of our proposed approach.
KW - Case based Reasoning
KW - Context aware
KW - Mobile Edge Computing
KW - Multi QoS Prediction
KW - Quality of Service
KW - Support Vector Machine
UR - https://www.scopus.com/pages/publications/85072568165
U2 - 10.1109/SCC.2019.00024
DO - 10.1109/SCC.2019.00024
M3 - 会议稿件
AN - SCOPUS:85072568165
T3 - Proceedings - 2019 IEEE International Conference on Services Computing, SCC 2019 - Part of the 2019 IEEE World Congress on Services
SP - 72
EP - 79
BT - Proceedings - 2019 IEEE International Conference on Services Computing, SCC 2019 - Part of the 2019 IEEE World Congress on Services
A2 - Bertino, Elisa
A2 - Chang, Carl K.
A2 - Chen, Peter
A2 - Damiani, Ernesto
A2 - Damiani, Ernesto
A2 - Goul, Michael
A2 - Oyama, Katsunori
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Services Computing, SCC 2019
Y2 - 8 July 2019 through 13 July 2019
ER -