Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Context-aware multi-QoS prediction for services in mobile edge computing|
|Citation:||Proceedings: 2019 IEEE International Conference on Services Computing - IEEE SCC 2019 - Part of the 2019 IEEE World Congress on Services, 2019 / pp.72-79|
|Series/Report no.:||Proceedings of the IEEE International Conference on Services Computing SCC|
|Conference Name:||IEEE International Conference on Services Computing (SCC) (08 Jul 2019 - 13 Jul 2019 : Milan, Italy)|
|Zhizhong Liu, Quan Z. Sheng, Wei Emma Zhang, Dianhui Chu, and Xiaofei Xu|
|Abstract:||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.|
|Keywords:||Context-aware; quality of service; mulit-QoS prediction; mobile edge computing; support victor machine; case-based reasoning|
|Appears in Collections:||Computer Science publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.