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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2020.9
- 수록면
- 393 - 396 (4page)
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초록· 키워드
오류제보하기
Many mission critical devices are increasing with upcoming 5G network to fulfill a low latency for a real time network service on smart factory, autonomous vehicle, etc. Distributed cloud computing system also has a key role to execute the various mobile devices, because, an edge computing is the nearest from the mobile devices to provide low latency and computation energy consumption. In this paper, we consider the autonomous vehicles with video live streaming services. Especially, the vehicles require a low transmission delay as within 10 ms. To reduce a latency with low energy consumption, we propose a service chaining offloading decision with a deep reinforcement learning. We split tasks of the vehicle per service function blocks which have their own role. So it can do partial offloading and user association in a On-Device Edge of the vehicle and in the SBS at the same time. We can get results that service chaining offloading decision gives more optimal energy consumption with low-latency to autonomous vehicle users.
목차
Abstract
I. INTRODUCTION
II. SYSTEM MODEL
III. PROBLEM FORMULATION
IV. DEEP REINFORCEMENT LEARNING BASED SERVICE CHAINING OFFLOADING DECISION
V. EVALUATION
VI. CONCLUSION
REFERENCES
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