Virtualizing vehicular node resources: Feasibility study of virtual machine migration

Baron, Benjamin; Campista, Miguel Elias M.; Spathis, Prométhée; Costa, Luís Henrique M. K.; Amorim, Marcelo Dias de; Duarte, Otto Carlos Muniz Bandeira; Pujolle, Guy; Viniotis, Yannis

doi:10.1016/j.vehcom.2016.04.001

Cited by 37 publications

(22 citation statements)

References 29 publications

(37 reference statements)

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“…In this case, a proper architecture is provided to grant access to resources depending on service needs. Even migration schemes of virtual machines between OBUs have been considered to offer services adapted to available resources [15].…”

Section: Related Workmentioning

confidence: 99%

SURROGATES: Virtual OBUs to Foster 5G Vehicular Services

et al. 2019

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Virtualization technologies are key enablers of softwarized 5G networks, and their usage in the vehicular domain can provide flexibility and reliability in real deployments, where mobility and processing needs may be an issue. Next-generation vehicular services, such as the ones in the area of urban mobility and, in general, those interconnecting on-board sensors, require continuous data gathering and processing, but current architectures are stratified in two-tier solutions in which data is collected by on-board units (OBU) and sent to cloud servers. In this line, intermediate cache and processing layers are needed in order to cover quasi-ubiquitous data-gathering needs of vehicles in scenarios of smart cities/roads considering vehicles as moving sensors. The SURROGATES solution presented in this paper proposes to virtualize vehicle OBUs and create a novel Multi-Access Edge Computing (MEC) layer with the aim of offloading processing from the vehicle and serving data-access requests. This deals with potential disconnection periods of vehicles, saves radio resources when accessing the physical OBU and improves data processing performance. A proof of concept has been implemented using OpenStack and Open Source MANO to virtualize resources and gather data from in-vehicle sensors, and a final traffic monitoring service has been implemented to validate the proposal. Performance results reveal a speedup of more than 50% in the data request resolution, with consequently great savings of network resources in the wireless segment. Thus, this work opens a novel path regarding the virtualization of end-devices in the Intelligent Transportation Systems (ITS) ecosystem.

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Section: Related Workmentioning

confidence: 99%

SURROGATES: Virtual OBUs to Foster 5G Vehicular Services

et al. 2019

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“…Li et al proposed migration procedure in fog computing–enabled LTE band that supports real time vehicular communication. Baron et al studied the feasibility of VM migrations among moving buses. Although the studies prove migration of VMs among moving vehicles is possible, the process of VM migration in vehicular environment for both V2V and V2I communication is an unexplored area.…”

Section: Related Workmentioning

confidence: 99%

“…The task of the VM service scheduler is to allocate resources to a task residing in the request vector from the VM vector optimally while maintaining the required QoS of the clients. Many resource allocation strategies are researched upon . The decision is taken based on one of these algorithms to allocate the resource. f. Migration scheduler .…”

Section: Proposed System Architecture Of Vehicular Cloud Networkmentioning

confidence: 99%

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An adaptive resource placement policy by optimizing live VM migration for ITS applications in vehicular cloud network

Midya

Roy

Majumder

et al. 2019

Trans Emerging Tel Tech

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Vehicular cloud computing (VCC) is a promising approach that uses cloud computing techniques in vehicular environment to execute smart applications. Vehicular environment is characterized by high mobility. This requires the services executed by a vehicular resource in a vehicular cloud to be migrated before it leaves a network. Migration is also required when a service‐requesting vehicle has to leave a network before its request is executed. Moreover, to balance the network load, virtual machine (VM) migration is desired as, at times, a single physical host may become overloaded. In this work, we propose an adaptive resource placement policy by optimizing live VM migration process in vehicular cloud network. The Pareto optimal mapping of migrating VMs to a physical host is carried out using a hybrid optimized algorithm, which is the combination of particle swarm optimization and genetic algorithm. The optimization is done by maximizing a fitness function that is developed by considering significant quality‐of‐service (QoS) parameters, such as network latency, migration delay, power consumption, and vehicular mobility. These QoS parameters are mathematically formulated in view of a vehicular network. The proposed algorithm reduces migration latency, transmission latency, and service delay. It also distributes the load among the available physical hosts within a cloud system, thus reducing the average wait time for each cloud user. Simulation results exhibit significant decrease in waiting time, service delay, and migration delay. It is also shown that the proposed algorithm reduces total power consumption of the system.

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“…Therefore, "pay as you go" is the elementary profit model for cloud computing providers [29]. [45], the authors propose vehicular node virtualization, which allows the service provider to allocate resources to tenants within vehicular nodes. The virtual machine migration in VANETs is also studied.…”

Section: Cloud Computing and Nfv In Vanetsmentioning

confidence: 99%

Trust Management for Security Enhancements in Ad hoc Networking Paradigms with Uncertain Reasoning

Wei¹

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Trust-based schemes are promising techniques to tackle inside attacks in distributed self-organized networks, such as mobile ad hoc networks and vehicular ad hoc networks. For the outside attackers, access control, authorization and authentication by cryptography can effectively thwart most of them. For the inside attackers, prevention based schemes such as cryptographic techniques are usually powerless. In the trust management system, trust is defined as the degree of belief that an entity can behave correctly in an observer's perspective. Compared to prevention-based schemes, detection-based schemes, such as trust management, dynamically estimate the internal nodes behavior. Based on the results of the estimation, the detection system makes the decision whether the node is a malicious attacker. These detectionbased schemes introduce a large amount of uncertainties due to the unpredictable behavior of each node in the networks. Therefore, in the trust management system, accurate trust assessment is playing a key role in the trust management. It is significantly affected by uncertainty. In order to obtain accurate trust of each entity in the network, we apply uncertain reasoning, coming from the artificial intelligence field, to trust management in the emerging networking paradigms. In this dissertation, we focus on trust management with the probability methodologies. This is because that Bayesian probability can formulate the trust in distributed self-organized networks better than rule-based schemes. Under the framework of uncertain reasoning, we adopt three approaches to evaluate trust accurately: v Table of Contents vi List of Tables xi List of Figures xii

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Virtualizing vehicular node resources: Feasibility study of virtual machine migration

Cited by 37 publications

References 29 publications

SURROGATES: Virtual OBUs to Foster 5G Vehicular Services

SURROGATES: Virtual OBUs to Foster 5G Vehicular Services

An adaptive resource placement policy by optimizing live VM migration for ITS applications in vehicular cloud network

Trust Management for Security Enhancements in Ad hoc Networking Paradigms with Uncertain Reasoning

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