A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Mixed-Integer Quadratic Programming (MIQP). <s> Vehicle Ad-Hoc Networks VANET enable all components in intelligent transportation systems to be connected so as to improve transport safety, relieve traffic congestion, reduce air pollution, and enhance driving comfort. The vision of all vehicles connected poses a significant challenge to the collection, storage, and analysis of big traffic-related data. Vehicular cloud computing, which incorporates cloud computing into vehicular networks, emerges as a promising solution. Different from conventional cloud computing platform, the vehicle mobility poses new challenges to the allocation and management of cloud resources in roadside cloudlet. In this paper, we study a virtual machine VM migration problem in roadside cloudlet-based vehicular network and unfold that 1 whether a VM shall be migrated or not along with the vehicle moving and 2 where a VM shall be migrated, in order to minimize the overall network cost for both VM migration and normal data traffic. We first treat the problem as a static off-line VM placement problem and formulate it into a mixed-integer quadratic programming problem. A heuristic algorithm with polynomial time is then proposed to tackle the complexity of solving mixed-integer quadratic programming. Extensive simulation results show that it produces near-optimal performance and outperforms other related algorithms significantly. Copyright © 2015 John Wiley & Sons, Ltd. <s> BIB001 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Mixed-Integer Quadratic Programming (MIQP). <s> With the shortcoming of long network delay and long response time, traditional mobile cloud computing can't meet the need of delay sensitive business, such as Virtual Reality and Augmented Reality. Mobile edge computing has emerged and provided a way to solve the problems. During the movement of the user, if an optimal data center is detected, the virtual machine migrates from the current data center to the optimal one. It is important to note that virtual machine migration increases costs while enhancing the user's experience. How to balance costs and benefits is what we need to focus on. The core issue of the migration strategy is whether, when, and where to migrate. In this paper, we propose an edge computing migration strategy based on multiple attribute decision making to deal with the issue. Furthermore, we demonstrate the effectiveness of the migration strategy by simulation. <s> BIB002 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> Mixed-Integer Quadratic Programming (MIQP). <s> Mobile edge computing is a new computing paradigm in which cloud computing capabilities are pushed from the network core to the network edge to serve the end-user in proximity. However, with the sinking of computing capabilities, the new challenge incurred by user mobility arises: since end-users typically move erratically, the services should be dynamically migrated among multiple edges to maintain the service performance, i.e., user-perceived latency. Tackling this problem is non-trivial since frequent service migration would greatly increase the operational cost. To address this challenge in terms performance-cost trade-off, in this paper we study the mobile edge service performance optimization problem under long-term cost budget constraint. To address user mobility which is typically unpredictable, we first apply Lyapunov optimization to decompose the long-term optimization problem into a series of real-time optimization problems which do not require a priori knowledge such as user mobility. As the decomposed problem is NP-hard, we further propose an efficient heuristic based on the Markov approximation technique. Rigorous theoretical analysis and extensive evaluations demonstrate the efficacy of the proposed solution. <s> BIB003
In BIB001 , the problem of VM migration in a "roadside cloudlet-based vehicular network" is formulated as a mixed-integer quadratic programming problem. The aim is to minimize the network cost while considering the migration-transmission cost tradeoff. In order to solve the formulated problem, the authors propose a heuristic that finds, in polynomial time, the optimal network path between a client vehicle and its corresponding VM. Simulations show that the proposed heuristic obtains the lowest network cost compared to stateof-the-art alternatives, while approaching the cost obtained by the optimal solution to the MIQP. Additionally, the obtained results emphasize the need for efficiently balancing both migration and transmission costs. Stochastic Optimization. The authors in BIB003 study the performance-cost tradeoff that may result from frequent migrations in MEC. A stochastic optimization problem is formulated to minimize the long-term average latency given a long-term cost budget. Due to the lack of knowledge regarding the user mobility path, the original problem is transformed into a set of real-time optimization problems, using Lyapunov optimization. To solve the resulting problems, the Markov approximation technique is used to derive an efficient heuristic. Simulations show that the proposed heuristic achieves the lowest user perceived latency while meeting the defined cost budget. Multi-attribute Decision Making. In BIB002 , the authors focus on the and benefits related to service migration in MEC by leveraging the "multiple attribute decision-making" approach. Simulations show that using this approach significantly reduces the user's response time.