Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/127217
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Type: Conference paper
Title: Context-aware multi-QoS prediction for services in mobile edge computing
Author: Liu, Z.
Sheng, Q.
Zhang, W.
Chu, D.
Xu, X.
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
Publisher: IEEE
Publisher Place: online
Issue Date: 2019
Series/Report no.: Proceedings of the IEEE International Conference on Services Computing SCC
ISBN: 9781728127200
ISSN: 2474-8137
2474-2473
Conference Name: IEEE International Conference on Services Computing (SCC) (08 Jul 2019 - 13 Jul 2019 : Milan, Italy)
Statement of
Responsibility: 
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
Rights: ©2019 IEEE
RMID: 1000001295
DOI: 10.1109/SCC.2019.00024
Grant ID: http://purl.org/au-research/grants/arc/FT140101247
Appears in Collections:Computer Science publications

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