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학술지 QoS-Aware Workload Distribution in Hierarchical Edge Clouds: A Reinforcement Learning Approach
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저자
조충래, 신승재, 전홍석, 윤승현
발행일
202010
출처
IEEE Access, v.8, pp.193297-193313
ISSN
2169-3536
출판사
IEEE
DOI
https://dx.doi.org/10.1109/ACCESS.2020.3033421
협약과제
20HH1600, 초연결 지능 인프라 원천기술 연구개발, 김선미
초록
Recently, edge computing is getting attention as a new computing paradigm that is expected to achieve short-delay and high-throughput task offloading for large scale Internet-of-Things (IoT) applications. In edge computing, workload distribution is one of the most critical issues that largely influences the delay and throughput performance of edge clouds, especially in distributed Function-as-a-Service (FaaS) over networked edge nodes. In this paper, we propose the Resource Allocation Control Engine with Reinforcement learning (RACER), which provides an efficient workload distribution strategy to reduce the task response slowdown with per-task response time Quality-of-Service (QoS). First, we present a novel problem formulation with the per-task QoS constraint derived from the well-known token bucket mechanism. Second, we employ a problem relaxation to reduce the overall computation complexity by compromising just a bit of optimality. Lastly, we take the deep reinforcement learning approach as an alternative solution to the workload distribution problem to cope with the uncertainty and dynamicity of underlying environments. Evaluation results show that RACER achieves a significant improvement in terms of per-task QoS violation ratio, average slowdown, and control efficiency, compared to AREA, a state-of-the-art workload distribution method.
키워드
Deep reinforcement learning, edge computing, resource allocation, workload distribution
KSP 제안 키워드
Control efficiency, Critical issues, Deep reinforcement learning, Distribution problem, Distribution strategy, Edge cloud, Function as a service, High throughput(HTP), Internet of thing(IoT), Large scale Internet, Learning approach
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