Dynamic Virtual Machine Migration in a vehicular cloud

Vehicular clouds are formed by incorporating cloud-based services into vehicular ad hoc networks. Amongst the several challenges in a vehicular cloud network, virtual machine migration (VMM) may be one of the most crucial issues that need addressing. In this paper, a novel solution for VMM in a vehicular cloud is presented. The vehicular cloud is modeled as a small corporate data center with mobile hosts, equipped with limited computational and storage capacities. The proposed scheme is called Vehicular Virtual Machine Migration (VVMM). The VVMM aims to achieve efficient handling of frequent changes in the data center topology, host heterogeneity, all while doing so with minimum Roadside Unit (RU) intervention. Three modes of VVMM are studied. The first mode, VVMM-U uniformly selects the destinations for VM migrations, which will take place shortly prior to a vehicle's departure from the coverage of the RU. The second mode, VVMM-LW aims at migrating the VM to the vehicle with the least workload, and the third mode, VVMM-MA incorporates mobility awareness by migrating the VM to the vehicle with the least workload and forecasted to be within the geographic boundaries of the vehicular cloud. We evaluate the performance of our proposed framework through simulations. Simulation results show that VVMM-MA introduces significant reduction in unsuccessful migration attempts and results in an increased fairness in vehicle capacity utilization across the vehicular cloud system.

[1]  Gongjun Yan,et al.  Datacenter at the Airport: Reasoning about Time-Dependent Parking Lot Occupancy , 2012, IEEE Transactions on Parallel and Distributed Systems.

[2]  Qi Zhang,et al.  Virtual machine migration in cloud computing environments: benefits, challenges, and approaches , 2014 .

[3]  Stephan Olariu,et al.  Taking VANET to the clouds , 2011, Int. J. Pervasive Comput. Commun..

[4]  H. Mouftah,et al.  Virtual Machine Migration in Cloud Computing Environments : Benefits , Challenges , and Approaches , 2013 .

[5]  Rong Yu,et al.  Toward cloud-based vehicular networks with efficient resource management , 2013, IEEE Network.

[6]  Silvia Giordano,et al.  The Next Paradigm Shift: From Vehicular Networks to Vehicular Clouds , 2013 .

[7]  Chonho Lee,et al.  A survey of mobile cloud computing: architecture, applications, and approaches , 2013, Wirel. Commun. Mob. Comput..

[8]  Hajar Mousannif,et al.  Cooperation as a Service in VANETs , 2011, J. Univers. Comput. Sci..

[9]  Rajkumar Buyya,et al.  A survey on vehicular cloud computing , 2014, J. Netw. Comput. Appl..

[10]  Stephan Olariu,et al.  Towards Autonomous Vehicular Clouds - A Position Paper (Invited Paper) , 2011, ADHOCNETS.

[11]  Junggab Son,et al.  TIaaS: Secure Cloud-assisted Traffic Information Dissemination in Vehicular Ad Hoc Networks , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[12]  Raj Jain,et al.  A Quantitative Measure Of Fairness And Discrimination For Resource Allocation In Shared Computer Systems , 1998, ArXiv.

[13]  Sangjin Kim,et al.  Rethinking Vehicular Communications: Merging VANET with cloud computing , 2012, 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings.