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
- 발행연도
- 2024.8
- 수록면
- 1,170 - 1,182 (13page)
- DOI
- 10.7840/kics.2024.49.8.1170
이용수
초록· 키워드
오류제보하기
In In edge-cloud environments, mobile nodes face significant challenges due to their mobility and the distributed nature of the environment. The unstable communication links between mobile nodes and the cloud often lead to frequent disruptions in connectivity, posing obstacles to seamless operation and service delivery. Effective energy management strategies are crucial to address these challenges and ensure the long-term viability of mobile nodes. In this paper, we propose an architecture for energy-aware resilience in edge-cloud environments for a standalone mobile node in an edge-cloud environment that can operate seamlessly in the connection disruption from the cloud. Our architecture leverages machine learning-based energy consumption prediction techniques to forecast energy consumption patterns while considering dynamic network conditions. In addition, we propose a threshold-based control policy for autonomous node resilience, enabling mobile nodes to adaptively adjust their operations in response to fluctuating energy levels and network conditions of edge environments. Through proactive energy management strategies, such as workload autoscaling with energy awareness, we aim to minimize energy consumption and maximize node survival time, particularly under constrained conditions. Experimental evaluations demonstrate the efficiency of our proposed approach in extending node longevity and ensuring reliable operation in dynamic and resource-constrained edge-cloud environments.
목차
ABSTRACT
Ⅰ. Introduction
Ⅱ. Design of Energy-Aware Resilience for Mobile Node in Edge-Cloud Environment
Ⅲ. Experimental Evaluation
Ⅳ. Conclusion and Future Works
References
참고문헌 (25)
참고문헌 신청
G. Carvalho, B. Cabral, V. Pereira, and J. Bernardino, “Edge computing: Current trends, research challenges, and future directions,” Comput., vol. 103, pp. 993-1023, 2021.
Y. Mansouri and M. A. Babar, “A review of edge computing: Features and resource virtualization,” J. Parallel and Distrib. Comput., vol. 150, pp. 155-183, 2021.
W. Z. Khan, et al., “Edge computing: A survey,” Future Generation Comput. Syst., vol. 97, pp. 219-235, 2019.
V. Prokhorenko and M. Ali Babar, “Architectural resilience in cloud, fog and edge systems: A survey,” in IEEE Access, vol. 8, pp. 28078-28095, 2020. (https://doi.org/10.1109/ACCESS.2020.2971007)
S. Li, C. Li, Y. Huang, B. A. Jalaian, Y. T. Hou, and W. Lou, “Enhancing resilience in mobile edge computing under processing uncertainty,” in IEEE J. Sel. Areas in Commun., vol. 41, no. 3, pp. 659-674, Mar. 2023. (https://doi.org/10.1109/JSAC.2023.3234706)
Y. Mansouri and M. A. Babar, “A review of edge computing: Features and resource virtualization,” J. Parallel and Distrib. Comput., vol. 150, pp. 155-183, 2021.
W. Z. Khan, et al., “Edge computing: A survey,” Future Generation Comput. Syst., vol. 97, pp. 219-235, 2019.
V. Prokhorenko and M. Ali Babar, “Architectural resilience in cloud, fog and edge systems: A survey,” in IEEE Access, vol. 8, pp. 28078-28095, 2020. (https://doi.org/10.1109/ACCESS.2020.2971007)
S. Li, C. Li, Y. Huang, B. A. Jalaian, Y. T. Hou, and W. Lou, “Enhancing resilience in mobile edge computing under processing uncertainty,” in IEEE J. Sel. Areas in Commun., vol. 41, no. 3, pp. 659-674, Mar. 2023. (https://doi.org/10.1109/JSAC.2023.3234706)
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