A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Mobility Pattern-Based Migration <s> A recent trend in communication networks --- sometimes referred to as fog computing --- offers to execute computational tasks close to the access points of the networks. This enables real-time applications, like mobile Complex Event Processing (CEP), to significantly reduce end-to-end latencies and bandwidth usage. Most work studying the placement of operators in such an environment completely disregards the migration costs. However, the mobility of users requires frequent migration of operators, together with possibly large state information, to meet latency restrictions and save bandwidth in the infrastructure. This paper presents a placement and migration method for providers of infrastructures that incorporate cloud and fog resources. It ensures application-defined end-to-end latency restrictions and reduces the network utilization by planning the migration ahead of time. Furthermore, we present how the application knowledge of the CEP system can be used to improve current live migration techniques for Virtual Machines to reduce the required bandwidth during the migration. Our evaluations show that we safe up to 49% of the network utilization with perfect knowledge about a users mobility pattern and up to 27% of the network utilization when considering the uncertainty of those patterns. <s> BIB001 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Mobility Pattern-Based Migration <s> Low latency in IT applications is an important aspect of improving the quality of the user's experience. Frequently, applications are run in a virtual machine in the cloud. Because cloud providers are datacentre facilities that are often distant from users, unacceptably high latencies are experienced in some applications. Fog computing can be seen as a cloud computing extension, namely cloudlets, located in access points at the edge of the network and hence able to provide lower latencies than the cloud. However, as mobile devices and applications become more popular, users' computing and data capacities should be maintained close to the user to keep latencies as low as possible. This paper discusses resource allocation in fog computing in the face of users' mobility and introduces MyiFogSim, an extension of iFogSim to support mobility through migration of virtual machines between cloudlets. Moreover, a migration policy is proposed, and MyiFogSim is used to analyze the policy impact on application quality of service. Results suggest that the policy can promote lower latencies when compared to a scenario without the migration policy. <s> BIB002 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Mobility Pattern-Based Migration <s> Fog computing provides a low latency access to resources at the edge of the network for resource-constrained devices. The high mobility of some of these devices, such as vehicles, brings great challenges related to resource allocation and management. In order to improve the management of computing resources utilized by mobile users connected to the Fog infrastructure, this paper proposes a virtual machine placement and migration decision model based on mobility prediction. Simulations have shown that moving the virtual machine to a Fog node ahead of the user’s route using the proposed approach can decrease by almost 50% the number of migrations needed by the user. The Fog architecture provides an average latency of about 15 milliseconds for the users’ applications and the proposed approach presents a lower latency compared to a greedy approach for the VM placement problem. <s> BIB003 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Mobility Pattern-Based Migration <s> One of the key targets of the upcoming 5G system is to build a mobile network architecture that supports not only classical mobile broadband applications (i.e., Internet and IMS), but also vertical industry services, such as those of automotive systems, e-health, public safety, and smart grid. Vertical industry is known to have specific needs that cannot be sustained by the current cellular networks. More notably, automotive systems require strict quality of service in terms of ultrashort latency for vehicle-to-infrastructure/network (V2I/N) communications. In this paper, we introduce the Follow Me edge-Cloud (FMeC) concept, leveraging the mobile edge computing (MEC) architecture to sustain requirements of the 5G automotive systems. Assuming that automotive services are deployed on MEC entities, FMeC ensures low-latency access to these services by guaranteeing that vehicles (i.e., as well as user equipment on board vehicles) always connect to nearest automotive service. Besides the FMeC architecture, our contribution in this paper consists in presenting a projection of the FMeC solution on an automated driving use case that integrates automotive and Telco infrastructures, to realize the vision of future 5G automotive systems. We introduce the envisioned software defined networking/OpenFlow-based architecture and our mobility-aware framework based on a set of building blocks that permit achieving the automated driving requirements within 5G network. The evaluation results, obtained conjointly through theoretical analysis and computer simulation, show that our proposed solution outperforms baseline approaches in meeting the automated driving latency requirement and minimizing the incurred global cost. <s> BIB004
Avoidance. In order to avoid excessive migration costs, a number of contributions, summarized in Table 6 , rely on predicting future migration targets based on users' mobility patterns. For instance, to deal with the issue of high migration costs due to frequent state propagation among elements of a fog computing infrastructure, the authors in BIB001 propose to use a time graph structure to model the expected costs. Then, the most suitable migration targets are determined ahead of time, taking users' mobility and dependencies among migrations into account. Evaluation results show substantial savings in network utilization, even in the presence of uncertainties characterizing users' mobility patterns. In BIB003 , the authors provide an Integer Linear Programming (ILP) model for VM placement in fog computing. The use of future user position is adopted to improve the VM placement and decrease the number of migrations. Both Myifogsim BIB002 and SUMO  are used to simulate the VM migrations and users' mobility, respectively. The authors find that using information about the following 5 minutes of the user's position significantly reduces the number of migrations. However, in this work, this information is obtained with 100% accuracy, which may not apply in real-world scenarios. Finally, in order to meet strict quality requirements of 5G automotive systems, the authors in BIB004 introduce the "Follow Me Edge-Cloud (FMEC)" concept. Within FMEC, a mobility-aware migration is performed to reduce both migration and data transmission costs. More specifically, an algorithm is provided to determine the vehicle's mobility pattern. As a result, if the vehicle is moving fast, the service is migrated to farther locations, thus avoiding unnecessary migrations along the way. If, on the other hand, the vehicle is decelerating, the migration is performed to the nearest MEC to ensure that delay constraints are met. Both theoretical and simulation-based results show that mobility-aware migration obtains fewer migrations as well as less global cost.