Reinforcement learning for resource provisioning in the vehicular cloud

Salahuddin, Mohammad A.; Al-Fuqaha, Ala; Guizani, Mohsen

doi:10.1109/mwc.2016.7553036

Cited by 93 publications

(36 citation statements)

References 15 publications

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“…The proposed scheme is divided into two stages, which adapt to large time scale factors, such as the traffic density, and small timescale factors, such as channel and queue states, respectively. In [76], the resource allocation problem in vehicular clouds has been modeled as an MDP and reinforcement learning is leveraged to solve the problem such that the resources are dynamically provisioned to maximize long-term benefits for the network and avoid myopic decision making. Joint management of networking, caching, and computing resources in virtualized vehicular networks has been further considered in [77], where a novel deep reinforcement learning approach has been proposed to deal with the highly complex joint resource optimization problem and shown to achieve good performance in terms of total revenues for the virtual network operators.…”

Section: ) Virtual Resource Allocationmentioning

confidence: 99%

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Liang

Hao

2019

IEEE Internet Things J.

224

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As wireless networks evolve towards high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional wireless design methodologies. Future intelligent vehicles, which are at the heart of high mobility networks, are increasingly equipped with multiple advanced onboard sensors and keep generating large volumes of data. Machine learning, as an effective approach to artificial intelligence, can provide a rich set of tools to exploit such data for the benefit of the networks. In this article, we first identify the distinctive characteristics of high mobility vehicular networks and motivate the use of machine learning to address the resulting challenges. After a brief introduction of the major concepts of machine learning, we discuss its applications to learn the dynamics of vehicular networks and make informed decisions to optimize network performance. In particular, we discuss in greater detail the application of reinforcement learning in managing network resources as an alternative to the prevalent optimization approach. Finally, some open issues worth further investigation are highlighted.

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Section: ) Virtual Resource Allocationmentioning

confidence: 99%

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Liang

Hao

2019

IEEE Internet Things J.

224

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“…Applying RL in SDN One main challenge SDN faces arises from highly-dynamic traffic patterns, which motivate a requirement for the network to be reconfigured frequently. It has been demonstrated that RL is an ideal tool to accomplish such a task [69,48,46,38,77,68,51,27,40,20]. For example, Salahuddin et al [69] propose a roadside unit (RSU) cloud to enhance traffic flow and road safety.…”

Section: Software-defined Networkingmentioning

confidence: 99%

Reinforcement Learning for Autonomous Defence in Software-Defined Networking

Han

Rubinstein

Abraham

et al. 2018

Lecture Notes in Computer Science

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Despite the successful application of machine learning (ML) in a wide range of domains, adaptabilitythe very property that makes machine learning desirable-can be exploited by adversaries to contaminate training and evade classification. In this paper, we investigate the feasibility of applying a specific class of machine learning algorithms, namely, reinforcement learning (RL) algorithms, for autonomous cyber defence in software-defined networking (SDN). In particular, we focus on how an RL agent reacts towards different forms of causative attacks that poison its training process, including indiscriminate and targeted, white-box and black-box attacks. In addition, we also study the impact of the attack timing, and explore potential countermeasures such as adversarial training.

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“…This on-board computing facility in today's vehicles is spurring emerging applications such as infotainment and comfort applications (e.g. on-board Internet access, vehicle conditions for maintenance update, traffic congestion live information) [41,48].…”

Section: Introductionmentioning

confidence: 99%

“…With the technological advancements of today's OBU installed in vehicles, many researchers envision that vehicles may form a Fog computing facility for supporting different applications in the network access segment [8,19,41]. Implementation of vehicles as a fog node has developed a novel approach of Fog computing called Vehicular Fog-Computing (VFC).…”

Section: Introductionmentioning

confidence: 99%

Evaluating TCP performance of routing protocols for traffic exchange in street-parked vehicles based fog computing infrastructure

et al. 2020

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As most vehicles remain parked 95% of its time, this suggests that leveraging the use of On-board Units (OBUs) in parked vehicles would provide communication and computation services to other mobile and fixed nodes for delivery of services such as multimedia streaming, data storage and data processing. The nearby vehicles can form an infrastructure using IEEE 802.11p communication interface, facilitating communication, computation and storage services to the end users. We refer to this as a Vehicular Fog Computing (VFC) infrastructure. In this study, using NS-2 simulator, we investigate how six routing protocols consisting of two proactive routing protocols, Destination Sequence Destination Vector (DSDV) and Fisheye State Routing (FSR); two reactive routing protocols, Ad Hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR); and two geographic routing protocols, Distance Routing Effect Algorithm for Mobility (DREAM) and Location Aided Routing (LAR) perform when forwarding TCP traffic among the parked vehicles that form a VFC infrastructure in an urban street parking scenario. In order to reflect an urban street parking scenario, we consider a traffic mobility traces that are generated using SUMO in our simulation. To the best of our knowledge, this work is the first effort to understand how vehicle density, vehicle speed and parking duration can influence TCP in an urban street parking scenario when packet forwarding decision is made using proactive, reactive and geographic routing protocols. In our performance evaluation, positive results are observed on the influence of parking duration in parked vehicles as TCP performance in all routing protocols increases with longer parking duration. However, variable speed in parked vehicles and moving vehicles in an urban street parking scenario may not have significant influence on TCP performance, especially in case of reactive and proactive routing protocols. Further, our findings reveal that vehicle density in a VFC infrastructure can noticeably influence TCP performance. Towards the end of the paper, we delineate some important future research issues in order to improve routing performance in a street-parked vehicle based VFC infrastructure.

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Reinforcement learning for resource provisioning in the vehicular cloud

Cited by 93 publications

References 15 publications

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Reinforcement Learning for Autonomous Defence in Software-Defined Networking

Evaluating TCP performance of routing protocols for traffic exchange in street-parked vehicles based fog computing infrastructure

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