지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.21 No.2
- 발행연도
- 2021.6
- 수록면
- 101 - 122 (22page)
- DOI
- 10.5391/IJFIS.2021.21.2.101
이용수
초록· 키워드
오류제보하기
This paper reports on the use of online data stream classification algorithms to support workload orchestration in vehicular edge computing environments. These algorithms can be used to predict the ability of available computational nodes to successfully handle computational tasks generated from vehicular applications. Several online data stream classification algorithms have been evaluated based on synthetic datasets generated from simulated vehicular edge computing environments. In addition, a multi-criteria decision analysis technique was utilized to rank the different algorithms based on their performance metrics. The evaluation results demonstrate that the considered algorithms can handle online classification operations with various trade-offs and dominance relations with respect to their obtained performance. In addition, the utilized multi-criteria decision analysis technique can efficiently rank various algorithms and identify the most appropriate algorithms to augment workload orchestration. Furthermore, the evaluation results show that the leveraging bagging algorithm, with an extremely fast decision tree base estimator, is able to maintain marked online classification performance and persistent competitive ranking among its counterparts for all datasets. Hence, it can be considered a promising choice to reinforce workload orchestration in vehicular edge computing environments.
목차
Abstract
1. Introduction
2. Data Stream Classification
3. Research Tools and Methodology
4. Results and Analysis
5. Conclusion
References
참고문헌 (58)
참고문헌 신청
Hongzhi Guo / 2020 / Intelligent Task Offloading in Vehicular Edge Computing Networks / IEEE Wireless Communications 27(4):126~132
Ekim Yurtsever / 2020 / A survey of autonomous driving: common practices and emerging technologies / IEEE Access 8:58443~58469
Joseph Neil / 2020 / Precise Timing for Vehicle Navigation in the Smart City: An Overview / IEEE Communications Magazine 58(4):54~59
D. Sabella / 2020 / MEC-based infotainment services for smart roads in 5G environments / Proceedings of 2020IEEE 91st Vehicular Technology Conference (VTC2020-Spring) :1~6
Ribal F. Atallah / 2019 / Scheduling the Operation of a Connected Vehicular Network Using Deep Reinforcement Learning / IEEE Transactions on Intelligent Transportation Systems 20(5):1669~1682
Ekim Yurtsever / 2020 / A survey of autonomous driving: common practices and emerging technologies / IEEE Access 8:58443~58469
Joseph Neil / 2020 / Precise Timing for Vehicle Navigation in the Smart City: An Overview / IEEE Communications Magazine 58(4):54~59
D. Sabella / 2020 / MEC-based infotainment services for smart roads in 5G environments / Proceedings of 2020IEEE 91st Vehicular Technology Conference (VTC2020-Spring) :1~6
Ribal F. Atallah / 2019 / Scheduling the Operation of a Connected Vehicular Network Using Deep Reinforcement Learning / IEEE Transactions on Intelligent Transportation Systems 20(5):1669~1682
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