Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning

In this paper, we investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple logically-isolated slices are constructed on a common roadside network infrastructure. A dynamic RAN slicing fram...

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Published in:	IEEE journal on selected areas in communications Vol. 39; no. 7; pp. 2076 - 2089
Main Authors:	Wu, Wen, Chen, Nan, Zhou, Conghao, Li, Mushu, Shen, Xuemin, Zhuang, Weihua, Li, Xu
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms constrained reinforcement learning Constraints coupled constraint Delays Dynamic scheduling Heuristic algorithms Internet of Vehicles Machine learning Optimization Quality of service Radio spectra RAN slicing Resource allocation Resource management Roadsides Slicing Task analysis Traffic volume Vehicle dynamics Vehicles vehicular networks Workload workload distribution Workloads
ISSN:	0733-8716, 1558-0008
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Abstract	In this paper, we investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple logically-isolated slices are constructed on a common roadside network infrastructure. A dynamic RAN slicing framework is presented to dynamically allocate radio spectrum and computing resource, and distribute computation workloads for the slices. To obtain an optimal RAN slicing policy for accommodating the spatial-temporal dynamics of vehicle traffic density, we first formulate a constrained RAN slicing problem with the objective to minimize long-term system cost. This problem cannot be directly solved by traditional reinforcement learning (RL) algorithms due to complicated coupled constraints among decisions. Therefore, we decouple the problem into a resource allocation subproblem and a workload distribution subproblem, and propose a two-layer constrained RL algorithm, named R esource A llocation and W orkload di S tribution (RAWS) to solve them. Specifically, an outer layer first makes the resource allocation decision via an RL algorithm, and then an inner layer makes the workload distribution decision via an optimization subroutine. Extensive trace-driven simulations show that the RAWS effectively reduces the system cost while satisfying QoS requirements with a high probability, as compared with benchmarks.
AbstractList	In this paper, we investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple logically-isolated slices are constructed on a common roadside network infrastructure. A dynamic RAN slicing framework is presented to dynamically allocate radio spectrum and computing resource, and distribute computation workloads for the slices. To obtain an optimal RAN slicing policy for accommodating the spatial-temporal dynamics of vehicle traffic density, we first formulate a constrained RAN slicing problem with the objective to minimize long-term system cost. This problem cannot be directly solved by traditional reinforcement learning (RL) algorithms due to complicated coupled constraints among decisions. Therefore, we decouple the problem into a resource allocation subproblem and a workload distribution subproblem, and propose a two-layer constrained RL algorithm, named R esource A llocation and W orkload di S tribution (RAWS) to solve them. Specifically, an outer layer first makes the resource allocation decision via an RL algorithm, and then an inner layer makes the workload distribution decision via an optimization subroutine. Extensive trace-driven simulations show that the RAWS effectively reduces the system cost while satisfying QoS requirements with a high probability, as compared with benchmarks.
Author	Chen, Nan Zhou, Conghao Li, Xu Shen, Xuemin Zhuang, Weihua Li, Mushu Wu, Wen
Author_xml	– sequence: 1 givenname: Wen orcidid: 0000-0002-0458-1282 surname: Wu fullname: Wu, Wen email: w77wu@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 2 givenname: Nan surname: Chen fullname: Chen, Nan email: n37chen@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 3 givenname: Conghao orcidid: 0000-0002-5727-2432 surname: Zhou fullname: Zhou, Conghao email: c89zhou@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 4 givenname: Mushu orcidid: 0000-0002-9694-3294 surname: Li fullname: Li, Mushu email: m475li@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 5 givenname: Xuemin orcidid: 0000-0002-4140-287X surname: Shen fullname: Shen, Xuemin email: sshen@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 6 givenname: Weihua orcidid: 0000-0003-0488-511X surname: Zhuang fullname: Zhuang, Weihua email: wzhuang@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 7 givenname: Xu surname: Li fullname: Li, Xu email: xu.lica@huawei.com organization: Huawei Technologies Canada Inc., Ottawa, ON, Canada
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SubjectTerms	Algorithms constrained reinforcement learning Constraints coupled constraint Delays Dynamic scheduling Heuristic algorithms Internet of Vehicles Machine learning Optimization Quality of service Radio spectra RAN slicing Resource allocation Resource management Roadsides Slicing Task analysis Traffic volume Vehicle dynamics Vehicles vehicular networks Workload workload distribution Workloads
Title	Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning
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