scholarly journals Network Slicing on 5G Vehicular Cloud Computing Systems

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1474
Author(s):  
Emmanouil Skondras ◽  
Angelos Michalas ◽  
Dimitrios J. Vergados ◽  
Emmanouel T. Michailidis ◽  
Nikolaos I. Miridakis ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Multiple Input Multiple Output ◽  
Access Network ◽  
Network Access ◽  
Network Slicing ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud ◽  
Sdn Controller

Fifth generation Vehicular Cloud Computing (5G-VCC) systems support various services with strict Quality of Service (QoS) constraints. Network access technologies such as Long-Term Evolution Advanced Pro with Full Dimensional Multiple-Input Multiple-Output (LTE-A Pro FD-MIMO) and LTE Vehicle to Everything (LTE-V2X) undertake the service of an increasing number of vehicular users, since each vehicle could serve multiple passenger with multiple services. Therefore, the design of efficient resource allocation schemes for 5G-VCC infrastructures is needed. This paper describes a network slicing scheme for 5G-VCC systems that aims to improve the performance of modern vehicular services. The QoS that each user perceives for his services as well as the energy consumption that each access network causes to user equipment are considered. Subsequently, the satisfactory grade of the user services is estimated by taking into consideration both the perceived QoS and the energy consumption. If the estimated satisfactory grade is above a predefined service threshold, then the necessary Resource Blocks (RBs) from the current Point of Access (PoA) are allocated to support the user’s services. On the contrary, if the estimated satisfactory grade is lower than the aforementioned threshold, additional RBs from a Virtual Resource Pool (VRP) located at the Software Defined Network (SDN) controller are committed by the PoA in order to satisfy the required services. The proposed scheme uses a Management and Orchestration (MANO) entity implemented by a SDN controller, orchestrating the entire procedure avoiding situations of interference from RBs of neighboring PoAs. Performance evaluation shows that the suggested method improves the resource allocation and enhances the performance of the offered services in terms of packet transfer delay, jitter, throughput and packet loss ratio.

scholarly journals A Safety Resource Allocation Mechanism against Connection Fault for Vehicular Cloud Computing

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Tianpeng Ye ◽  
Zhou Su ◽  
Jun Wu ◽  
Longhua Guo ◽  
Jianhua Li ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Real Time ◽  
Traffic Control ◽  
Intelligent Transportation System ◽  
Probability Model ◽  
Allocation Mechanism ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud ◽  
Resource Allocation Mechanism

The Intelligent Transportation System (ITS) becomes an important component of the smart city toward safer roads, better traffic control, and on-demand service by utilizing and processing the information collected from sensors of vehicles and road side infrastructure. In ITS, Vehicular Cloud Computing (VCC) is a novel technology balancing the requirement of complex services and the limited capability of on-board computers. However, the behaviors of the vehicles in VCC are dynamic, random, and complex. Thus, one of the key safety issues is the frequent disconnections between the vehicle and the Vehicular Cloud (VC) when this vehicle is computing for a service. More important, the connection fault will disturb seriously the normal services of VCC and impact the safety works of the transportation. In this paper, a safety resource allocation mechanism is proposed against connection fault in VCC by using a modified workflow with prediction capability. We firstly propose the probability model for the vehicle movement which satisfies the high dynamics and real-time requirements of VCC. And then we propose a Prediction-based Reliability Maximization Algorithm (PRMA) to realize the safety resource allocation for VCC. The evaluation shows that our mechanism can improve the reliability and guarantee the real-time performance of the VCC.

An SMDP-Based Resource Allocation in Vehicular Cloud Computing Systems

2015 ◽  
Vol 62 (12) ◽  
pp. 7920-7928 ◽  
Author(s):  
Kan Zheng ◽  
Hanlin Meng ◽  
Periklis Chatzimisios ◽  
Lei Lei ◽  
Xuemin Shen
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Computing Systems ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

Reply to comment on ‘SFVCC: Chaotic map-based security framework for vehicular cloud computing’

2020 ◽  
Vol 14 (12) ◽  
pp. 1724-1724
Author(s):  
Dheerendra Mishra ◽  
Vinod Kumar ◽  
Dharminder Dhaminder ◽  
Saurabh Rana
Keyword(s):  
Cloud Computing ◽  
Chaotic Map ◽  
Security Framework ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud

scholarly journals An energy-efficient failure detector for vehicular cloud computing

PLoS ONE ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. e0191577 ◽  
Author(s):  
Jiaxi Liu ◽  
Zhibo Wu ◽  
Jian Dong ◽  
Jin Wu ◽  
Dongxin Wen
Keyword(s):  
Cloud Computing ◽  
Energy Efficient ◽  
Failure Detector ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud

Vehicular Cloud Computing

2014 ◽  
pp. 262-274 ◽  
Author(s):  
Kayhan Zrar Ghafoor ◽  
Marwan Aziz Mohammed ◽  
Kamalrulnizam Abu Bakar ◽  
Ali Safa Sadiq ◽  
Jaime Lloret
Keyword(s):  
Cloud Computing ◽  
Vehicular Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud ◽  
Performance Improvements ◽  
Potential Applications ◽  
Research Outcome ◽  
Hoc Networks

Recently, Vehicular Ad Hoc Networks (VANET) have attracted the attention of research communities, leading car manufacturers, and governments due to their potential applications and specific characteristics. Their research outcome was started with awareness between vehicles for collision avoidance and Internet access and then expanded to vehicular multimedia communications. Moreover, vehicles’ high computation, communication, and storage resources set a ground for vehicular networks to deploy these applications in the near future. Nevertheless, on-board resources in vehicles are mostly underutilized. Vehicular Cloud Computing (VCC) is developed to utilize the VANET resources efficiently and provide subscribers safe infotainment services. In this chapter, the authors perform a survey of state-of-the-art vehicular cloud computing as well as the existing techniques that utilize cloud computing for performance improvements in VANET. The authors then classify the VCC based on the applications, service types, and vehicular cloud organization. They present the detail for each VCC application and formation. Lastly, the authors discuss the open issues and research directions related to VANET cloud computing.

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