A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Time-Related Optimal Service <s> Fully utilizing green energy can remarkably decrease the operational cost of cloudlet providers in provisioning green cloudlet networks (GCNs), which are powered by both green and brown energy. Owing to the spatial and temporal dynamics of energy demands and green energy generation, migrating Avatars (i.e., virtual machines) from green energy deprived cloudlets into green energy over-provisioned cloudlets can reduce the total on-grid energy consumption of GCN. However, Avatar migration itself consumes non-negligible energy consumption. In this letter, we propose the Energy driven AvataR migration (EARN) scheme to reduce the total on-grid energy consumption of GCN by considering the energy consumption of Avatar migrations. The performance of EARN is demonstrated by extensive simulations. <s> BIB001 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Time-Related Optimal Service <s> When moving cloud computing towards the edge in order to provide low latency and location-based Internet services, there are additional considerations that need to be introduced in the traditional cloud approaches to adapt them to fog computing. This paper discusses a proposal for resource allocation and migration strategies adapted specifically to fog computing. The proposal is based on the concept of virtual and physical communities and it has already shown its efficiency when applied to cloud computing. Simulation-based scenarios provide the performance of our community-based proposal following the geographical trajectory of mobile users. <s> BIB002 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Time-Related Optimal Service <s> The upcoming Cloud-Fog interplay is expected to grant service providers more degrees of freedom in the implementation and management of their service portfolios. However, recent advances in Mobile Internet have developed a growing need to support user mobility - as users move, the Fog counterparts of services that require close proximity may call for migration(s) to meet the desired quality of service (QoS). With the state-of-the-art virtualization technologies, next-generation Cloud/Fog services are being implemented in modular software (i.e., as a graph/chain of portable virtual objects (VOs)) that can be migrated around the Telco infrastructure, and yet scalability is still an open issue, especially with the inter-datacenter bulk live migration of VOs. In this perspective, a VO clustering and migration policy that jointly considers user proximity and inter-VO affinity is proposed to scalably support user mobility, while allowing service differentiation among users. Results confirm that introducing migrations improve the QoS to always meet or exceed the requirements, as compared to static service placement, and considering VO clusters as aggregate entities will initiate around 40% less migrations, on average - an improvement that increases with inter-VO affinity and could potentially simplify service management when supporting user mobility. <s> BIB003 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Time-Related Optimal Service <s> Emerging vehicular applications, such as real-time situational awareness and cooperative lane change, demand for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Fog Following Me (Folo), a novel solution for latency and quality balanced task allocation in vehicular fog computing. Folo is designed to support the mobility of vehicles, including ones generating tasks and the others serving as fog nodes. We formulate the process of task allocation across stationary and mobile fog nodes into a joint optimization problem, with constraints on service latency, quality loss, and fog capacity. As it is a NP-hard problem, we linearize it and solve it using Mixed Integer Linear Programming. To evaluate the effectiveness of Folo, we simulate the mobility of fog nodes at different times of day based on real-world taxi traces, and implement two representative tasks, including video streaming and real-time object recognition. Compared with naive and random fog node selection, the latency and quality balanced task allocation provided by Folo achieves higher performance. More specifically, Folo shortens the average service latency by up to 41\% while reducing the quality loss by up to 60\%. <s> BIB004 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Time-Related Optimal Service <s> This paper studies computation offloading for mobile users in legacy vehicles that are not equipped with powerful computing devices. While running a time-sensitive and computation-intensive application on a mobile device (MD) of these users, the MD may offload part or all the applications to a nearby smart vehicle that can serve as a cloudlet. This provides a possibility to complete the application in time while saving the battery energy of the MD. However, high mobility of the vehicles causes short-lived communication links between the vehicles. As a result, the MD may have to offload tasks to a new cloudlet each time it loses the connection to the current cloudlet. In this paper, we propose a vehicle-based cloudlet relaying (VCR) scheme for mobile computation offloading. The objective is to effectively utilize the computation resources available in surrounding smart vehicles of the MD in the highly dynamic network environment, where each vehicle-based cloudlet may only be available to help the MD execute a small portion of the application. Instead of offloading tasks directly from the MD to individual cloudlets, which may consume high power and a long time, especially when the link just becomes available or is about to disconnect, we propose computation relaying that allows the current executing cloudlet to offload tasks to the next executing cloudlet on behalf of the MD. A set of conditions are established to enable the inter-cloudlet offloading so that the computation relaying can be achieved between the vehicle cloudlets, while the results can be returned to the MD. In addition, the computation relaying is always backed up by direct offloading between the MD and individual cloudlets. An algorithm is proposed to find the time to switch to a new cloudlet and select the best cloudlet as the next execution site or return the execution back to the MD, in order to minimize the energy consumption of the MD while satisfying the delay constraint of the application. Extensive simulation results are shown to demonstrate the effectiveness of the proposed VCR scheme. <s> BIB005 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Time-Related Optimal Service <s> Low task execution time and low energy consumption of collaborating mobile human users and robots are important requirements of emerging human-agent-robot teamwork (HART)-centric Tactile Internet applications. In particular, task migration among mobile HART members has emerged as an important research topic, taking different task types, task deadlines, collaborative node capabilities, and mobility patterns into account. We propose a context-aware task migration scheme for efficiently orchestrating the real-time collaboration among human mobile users, central and decentralized computational agents (cloud/cloudlets), and collaborative robots (cobots) across converged fiber-wireless (FiWi) communications infrastructures. We investigate the problem of whether and, if so, when and where a HART-centric task should be best migrated to. For resource-efficient task execution, the migration decision is made according to given task processing capabilities of cloud/cloudlet agents and cobots, task execution deadline, energy consumption of involved cobots and mobile devices, and task migration latency. We evaluate the performance of our context-aware HART-centric task migration scheme and compare it to conventional task execution without migration. Towards this end, we develop an analytical framework for quantifying its performance in terms of a variety of task migration key performance metrics, including task migration gain-overhead ratio, deadline-miss ratio, task response time, and energy consumption efficiency. <s> BIB006
Reassignment. This section reports on the contributions that address the problem of finding the optimal node to migrate the service to, with an objective or a constraint related to the time axis. These contributions are described next and summarized in Table 9 . As an example, the authors in  developed a mobility-and load-aware virtual machine migration (VMM) for a mobile cloud computing context. This is achieved by using a genetic algorithm (GA) to select the optimal cloudlet to migrate the VM to such that the total number of VM migrations is minimized, resulting in a reduced task execution time. Simulation results confirm that the GA-VMM approach achieves the lowest time for task execution and the lowest number of VM migrations, compared to the no-migration case, load-centric migration, and greedy migration. Focusing on a fog computing scenario instead, the authors in BIB002 propose a community-based approach to address the problem of service placement and migration. In this work, where the concept of "community" originates from the field of cloud resource management, the network of fog nodes is partitioned into a hierarchical structure, where communities are combined to cover wider geographical regions as we move higher into the hierarchy. For evaluation purposes, the notion of communities has been integrated into the CloudSim simulator  . The obtained results show the ability of the community-based proposal to follow the mobile user's movement, thus resulting in a close-to-optimal average service delay. The authors in BIB003 propose a clustering and migration policy for virtual objects (VOs) that constitute cloud and fog services. This policy not only considers the need for meeting user proximity requirements but also takes inter-VO affinity (dependencies) into account. Simulation results show the QoS improvement when migrations are performed and the potential of considering VO clustering in reducing the number of migrations. A vehicular fog computing environment is considered in BIB004 , where both clients and fog nodes are mobile. An MILP-based optimization is performed to select a new fog node for the task execution such that the E2E latency and the quality loss are balanced. A reduction in the service latency is observed in the trace-based simulations results. Also considering a vehicular scenario, the authors in BIB005 provide an algorithm to determine the time at which an application should be switched to a new cloudlet by considering link durations among bus-hosted cloudlets. Then, the best cloudlet is selected as the next application host such that the energy consumption of the requesting mobile device is minimized while ensuring the application delay constraint is met. Simulation results confirm that the proposed scheme efficiently meets this objective. Focusing on the context of green cloudlet networks (GCNs), that is, cloudlet networks "powered by both green and brown energy," the authors in BIB001 propose the "Energy-driven AvataR migration (EARN)" scheme. The problem is formulated as an MILP problem that considers the energy consumption cost caused by the avatar migration and aims to minimize the total "brown" energy consumption such that the UE's SLAs in terms of delay are met. The suboptimal solution is obtained using the branch-and-cut algorithm, which ensures that fewer avatar migrations are performed when the green energy generation at the cloudlet is low. Finally, the authors in BIB006 consider an environment where the cloud, the cloudlets, human users, and collaborative robots (cobots) on the move collaborate in real time to execute a set of tasks. Within this environment, a context-aware task migration scheme is proposed. Several factors are taken into account in the migration decision, including the "processing capabilities of cloud/cloudlet agents and cobots, the task execution deadline, the energy consumption of the involved cobots and the mobile devices, and the task migration latency" BIB006 . Multiple performance metrics are provided to confirm the benefits of the proposed task migration compared to a "no migration" approach.