Context-aware multi-QoS prediction for services in mobile edge computing

Zhizhong Liu; Quan Z. Sheng; Wei Emma Zhang; Dianhui Chu; Xiaofei Xu

doi:10.1109/SCC.2019.00024

Context-aware multi-QoS prediction for services in mobile edge computing

Zhizhong Liu, Quan Z. Sheng, Wei Emma Zhang, Dianhui Chu, Xiaofei Xu

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

4 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Services Computing, SCC 2019
Subtitle of host publication	Part of the 2019 IEEE World Congress on Services
Editors	Elisa Bertino, Carl K. Chang, Peter Chen, Ernesto Damiani, Michael Goul, Katsunori Oyama
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	72-79
Number of pages	8
ISBN (Electronic)	9781728127200
ISBN (Print)	9781728127217
DOIs	https://doi.org/10.1109/SCC.2019.00024
Publication status	Published - 1 Jul 2019
Event	2019 IEEE International Conference on Services Computing, SCC 2019 - Milan, Italy Duration: 8 Jul 2019 → 13 Jul 2019

Publication series

Name	Proceedings of the IEEE International Conference on Services Computing SCC
Publisher	IEEE COMPUTER SOC
ISSN (Print)	2474-8137

Conference

Conference	2019 IEEE International Conference on Services Computing, SCC 2019
Country/Territory	Italy
City	Milan
Period	8/07/19 → 13/07/19

Keywords

Case based Reasoning
Context aware
Mobile Edge Computing
Multi QoS Prediction
Quality of Service
Support Vector Machine

Access to Document

10.1109/SCC.2019.00024

Cite this

Liu, Z., Sheng, Q. Z., Zhang, W. E., Chu, D., & Xu, X. (2019). Context-aware multi-QoS prediction for services in mobile edge computing. In E. Bertino, C. K. Chang, P. Chen, E. Damiani, M. Goul, & K. Oyama (Eds.), Proceedings - 2019 IEEE International Conference on Services Computing, SCC 2019: Part of the 2019 IEEE World Congress on Services (pp. 72-79). [8814132] (Proceedings of the IEEE International Conference on Services Computing SCC). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/SCC.2019.00024

Liu, Zhizhong ; Sheng, Quan Z. ; Zhang, Wei Emma ; Chu, Dianhui ; Xu, Xiaofei. / Context-aware multi-QoS prediction for services in mobile edge computing. Proceedings - 2019 IEEE International Conference on Services Computing, SCC 2019: Part of the 2019 IEEE World Congress on Services. editor / Elisa Bertino ; Carl K. Chang ; Peter Chen ; Ernesto Damiani ; Michael Goul ; Katsunori Oyama. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2019. pp. 72-79 (Proceedings of the IEEE International Conference on Services Computing SCC).

@inproceedings{0808f6bad7e24493a4fc22c6060ffd7d,

title = "Context-aware multi-QoS prediction for services in mobile edge computing",

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 = "Case based Reasoning, Context aware, Mobile Edge Computing, Multi QoS Prediction, Quality of Service, Support Vector Machine",

author = "Zhizhong Liu and Sheng, {Quan Z.} and Zhang, {Wei Emma} and Dianhui Chu and Xiaofei Xu",

year = "2019",

month = jul,

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doi = "10.1109/SCC.2019.00024",

language = "English",

isbn = "9781728127217",

series = "Proceedings of the IEEE International Conference on Services Computing SCC",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "72--79",

editor = "Elisa Bertino and Chang, {Carl K.} and Peter Chen and Ernesto Damiani and Michael Goul and Katsunori Oyama",

booktitle = "Proceedings - 2019 IEEE International Conference on Services Computing, SCC 2019",

address = "United States",

note = "2019 IEEE International Conference on Services Computing, SCC 2019 ; Conference date: 08-07-2019 Through 13-07-2019",

}

Liu, Z, Sheng, QZ , Zhang, WE, Chu, D & Xu, X 2019, Context-aware multi-QoS prediction for services in mobile edge computing. in E Bertino, CK Chang, P Chen, E Damiani, M Goul & K Oyama (eds), Proceedings - 2019 IEEE International Conference on Services Computing, SCC 2019: Part of the 2019 IEEE World Congress on Services., 8814132, Proceedings of the IEEE International Conference on Services Computing SCC, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 72-79, 2019 IEEE International Conference on Services Computing, SCC 2019, Milan, Italy, 8/07/19. https://doi.org/10.1109/SCC.2019.00024

Context-aware multi-QoS prediction for services in mobile edge computing. / Liu, Zhizhong; Sheng, Quan Z.; Zhang, Wei Emma; Chu, Dianhui; Xu, Xiaofei.

Proceedings - 2019 IEEE International Conference on Services Computing, SCC 2019: Part of the 2019 IEEE World Congress on Services. ed. / Elisa Bertino; Carl K. Chang; Peter Chen; Ernesto Damiani; Michael Goul; Katsunori Oyama. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 72-79 8814132 (Proceedings of the IEEE International Conference on Services Computing SCC).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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

PY - 2019/7/1

Y1 - 2019/7/1

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

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U2 - 10.1109/SCC.2019.00024

DO - 10.1109/SCC.2019.00024

M3 - Conference proceeding contribution

SN - 9781728127217

T3 - Proceedings of the IEEE International Conference on Services Computing SCC

SP - 72

EP - 79

BT - Proceedings - 2019 IEEE International Conference on Services Computing, SCC 2019

A2 - Bertino, Elisa

A2 - Chang, Carl K.

A2 - Chen, Peter

A2 - Damiani, Ernesto

A2 - Goul, Michael

A2 - Oyama, Katsunori

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 2019 IEEE International Conference on Services Computing, SCC 2019

Y2 - 8 July 2019 through 13 July 2019

ER -

Liu Z, Sheng QZ , Zhang WE, Chu D, Xu X. Context-aware multi-QoS prediction for services in mobile edge computing. In Bertino E, Chang CK, Chen P, Damiani E, Goul M, Oyama K, editors, Proceedings - 2019 IEEE International Conference on Services Computing, SCC 2019: Part of the 2019 IEEE World Congress on Services. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2019. p. 72-79. 8814132. (Proceedings of the IEEE International Conference on Services Computing SCC). https://doi.org/10.1109/SCC.2019.00024

Context-aware multi-QoS prediction for services in mobile edge computing

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Keywords

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