A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Migration Success Rate Optimization <s> In the era of the Internet of Things, all components in intelligent transportation systems will be connected to improve transport safety, relieve traffic congestion, reduce air pollution, and enhance the comfort of driving. The vision of all vehicles connected poses a significant challenge to the collection and storage of large amounts of traffic-related data. In this article, we propose to integrate cloud computing into vehicular networks such that the vehicles can share computation resources, storage resources, and bandwidth resources. The proposed architecture includes a vehicular cloud, a roadside cloud, and a central cloud. Then we study cloud resource allocation and virtual machine migration for effective resource management in this cloud-based vehicular network. A game-theoretical approach is presented to optimally allocate cloud resources. Virtual machine migration due to vehicle mobility is solved based on a resource reservation scheme. <s> BIB001 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Migration Success Rate Optimization <s> Vehicular Cloud Computing is a growing research field which consolidates the benefit of cloud computing into vehicular ad hoc networks. However, few studies address vehicles as potential Virtual Machine hosts. Due to the rapidly changing environment of a vehicular cloud, a host can easily change or leave coverage. As such, Virtual Machine Management and Migration schemes are necessary to ensure cloud subscribers have a satisfactory level of access to the resources. This paper proposes several Vehicular Virtual Machine Migration (VVMM) schemes: VVMM-U (Uniform), VVMM-LW (Least Workload), VVMM-MA (Mobility Aware) and MDWLAM (Mobility and Destination Workload Aware Migration). Their performance is evaluated with respect to a set of metrics through simulations with varying levels of vehicular traffic congestion, Virtual Machine sizes and levels of load restriction. The most advanced scheme (MDWLAM), takes into account, the workload and mobility of the original host as well as those of the potential destinations. By doing so a valid destination will both have time to receive the workload and migrate the new load when necessary. The behavior of various algorithms is compared and the MDWLAM has been shown to demonstrate best performance, exhibiting migration drop rates that are negligibly small. <s> BIB002 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Migration Success Rate Optimization <s> With emerging geo-distributed services, there is a need to coordinate the use of resources offered by field-area networks. In the case of vehicular networks, such resources include the processing, sensing, and storage capabilities offered to service providers for urban sensing or intelligent transportation. In this paper, we propose to virtualize the resources embedded on the vehicular nodes to allow multiple tenants to coexist and deploy their services on the same underlying mobile substrate. Virtualization is the task of an infrastructure provider that controls the mobile substrate and allocates sliced resources to the tenants. A service results from a collection of virtual machines hosted on the mobile nodes allocated by the infrastructure provider. Efficient utilization of the node resources may trigger virtual machine migrations. We study the problem of virtual machine migrations through V2V communications between mobile nodes. To evaluate the impact of such migrations on the resource allocation process, we use the real traces of a bus transit system to simulate a vehicular network where virtual machines migrate via V2V communications. Our results show that virtual machines of several hundreds of Megabytes can migrate between moving buses. We then discuss design principles and research issues toward the full virtualization of opportunistic networks. <s> BIB003 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Migration Success Rate Optimization <s> Vehicular Cloud Computing (VCC) has becomea significant research area recently, due to its potentialadvantages and applications, especially in the field ofIntelligent Transportation Systems (ITS). However, thehigh mobility of vehicular environment poses crucial challengesto resources allocation and management in VCC, which makesits implementation more complex than conventional clouds. Many works have been introduced to address various issuesand aspects of VCC, including resources management andVirtual Machine Migration in vehicular clouds. However, usingmobility prediction in VCC has not been studied previously. Inthis paper, we introduce a novel solution to reduce the effect ofresources mobility on the performance of vehicular cloud, usingan efficient resources management scheme based on vehiclesmobility prediction. This approach enables the vehicular cloudto take pre-planned procedures, based on the output of anArtificial Neural Network (ANN) mobility prediction model. The aim is to reduce the negative impact of sudden changes invehicles locations on vehicular cloud performance. A simulationscenario is introduced to compare between the performanceof our resources management scheme and other resourcesmanagement approaches introduced in the literature. Thesimulation environment is based on Nagel-Shreckenberg cellularautomata (CA) discrete model for traffic simulation. Simulationresults show that our proposed approach has leveraged theperformance of vehicular cloud effectively without overusingavailable vehicular cloud resources. <s> BIB004
Contributions falling under this category originate exclusively from the vehicular cloud research area. This is mainly due to the dynamicity characterizing such a scenario, thus potentially negatively affecting the migration success rates. More specifically, the authors in BIB001 address the problem of unsuccessful migrations that may arise when the target cloud is unable to accommodate the VM because it is excessively loaded. A resource reservation scheme is proposed to address this issue and an optimization problem is formulated to derive the optimal number of reserved resources. The proposed reservation scheme was shown to be promising, as a reduction of the service dropping rate was observed in the simulation results. To cope with the problem of vehicles hosting services leaving their area of operation before the end of the service execution, several "Vehicular Virtual Machine Migration (VVMM)" schemes have been proposed in BIB002 . Such schemes ensure that the VM is migrated from the exiting vehicle to other vehicles remaining in the RSU area. The most promising scheme is the "Mobility and Destination Workload Aware Migration (MDWLAM)" scheme, which obtained very low migration drop rates, as shown in the conducted simulations. In line with this approach, the authors in BIB004 leverage mobility prediction to avoid workload losses when a vehicle moves to another RSU area or a non-covered area. The vehicle's lifetime within the current RSU area is predicted based on an Artificial Neural Network (ANN) model, and when a predefined portion of its lifetime elapses, its current workload (i.e., its corresponding VM) would be premigrated to another vehicle that has a long lifetime in that same RSU area. The authors in BIB003 study the feasibility of performing VM migrations in a VANET scenario using V2V communications instead of using cellular links. However, since V2V communications are intermittent, the authors propose to opportunistically perform migrations in hotspot areas, defined as areas "where vehicles come in contact more often and for [a] longer period." Doing so improves the ratio of successful migrations, as shown in trace-based simulations. Table 10 summarizes the different characteristics of the aforementioned contributions. 