A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> GAP ANALYSIS AND RESEARCH OPPORTUNITIES <s> Contemporary mobile devices generate heavy loads of computationally intensive tasks, which cannot be executed locally due to the limited processing and energy capabilities of each device. Cloud facilities enable mobile devices-clients to offload their tasks to remote cloud servers, giving birth to Mobile Cloud Computing (MCC). The challenge for the cloud is to minimize the task execution and data transfer time to the user, whose location changes due to mobility. However, providing quality of service guarantees is particularly challenging in the dynamic MCC environment, due to the time-varying bandwidth of the access links, the ever changing available processing capacity at each server and the timevarying data volume of each virtual machine. In this article, we advocate the need for novel cloud architectures and migration mechanisms that effectively bring the computing power of the cloud closer to the mobile user. We consider a cloud computing architecture that consists of a back-end cloud and a local cloud, which is attached to wireless access infrastructure (e.g. LTE base stations). We outline different classes of task migration policies, spanning fully uncoordinated ones, in which each user or server autonomously makes its migration decisions, up to the cloud-wide migration strategy of a cloud provider. We conclude with a discussion of open research problems in the area. <s> BIB001 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> GAP ANALYSIS AND RESEARCH OPPORTUNITIES <s> We study the dynamic service migration problem in mobile edge-clouds that host cloud-based services at the network edge. This offers the benefits of reduction in network overhead and latency but requires service migrations as user locations change over time. It is challenging to make these decisions in an optimal manner because of the uncertainty in node mobility as well as possible non-linearity of the migration and transmission costs. In this paper, we formulate a sequential decision making problem for service migration using the framework of Markov Decision Process (MDP). Our formulation captures general cost models and provides a mathematical framework to design optimal service migration policies. In order to overcome the complexity associated with computing the optimal policy, we approximate the underlying state space by the distance between the user and service locations. We show that the resulting MDP is exact for uniform one-dimensional mobility while it provides a close approximation for uniform two-dimensional mobility with a constant additive error term. We also propose a new algorithm and a numerical technique for computing the optimal solution which is significantly faster in computation than traditional methods based on value or policy iteration. We illustrate the effectiveness of our approach by simulation using real-world mobility traces of taxis in San Francisco. <s> BIB002 </s> A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms <s> GAP ANALYSIS AND RESEARCH OPPORTUNITIES <s> Fog Computing (FC) is a flexible architecture to support distributed domain-specific applications with cloud-like quality of service. However, current FC still lacks the mobility support mechanism when facing many mobile users with diversified application quality requirements. Such mobility support mechanism can be critical such as in the industrial internet where human, products, and devices are moveable. To fill in such gaps, in this paper we propose novel container migration algorithms and architecture to support mobility tasks with various application requirements. Our algorithms are realized from three aspects: 1) We consider mobile application tasks can be hosted in a container of a corresponding fog node that can be migrated, taking the communication delay and computational power consumption into consideration; 2) We further model such container migration strategy as multiple dimensional Markov Decision Process (MDP) spaces. To effectively reduce the large MDP spaces, efficient deep reinforcement learning algorithms are devised to achieve fast decision-making and 3) We implement the model and algorithms as a container migration prototype system and test its feasibility and performance. Extensive experiments show that our strategy outperforms the existing baseline approaches 2.9, 48.5 and 58.4 percent on average in terms of delay, power consumption, and migration cost, respectively. <s> BIB003
As shown in the previous sections, the surveyed literature was revealed to be rich on many levels. However, some gaps could be noticed, turning into potential research opportunities that we summarize next: • Decision-making placement: Apart from a few exceptions, most of the reviewed contributions dealing with the migration decision making do not explicitly state where the decision-making logic is placed, and most importantly do not discuss its impact on the migration performance. Three potential decision-making placement levels could be envisioned as suggested in BIB001 for a mobile cloud computing context. These include centralized decisions, server-level decisions, and task-level decisions. As we go from the former to the latter, the autonomy level increases, whereas the complexity decreases. However, apart from autonomy and complexity, other considerations such as the impact of the placement on the timeliness of the decision and the availability of the required decision-making information at the considered level also matter. We find that only the authors in BIB003 reiterate the need to address such a problem. • Migration-related synchronization traffic: Timing and overhead: To enable a more informed migration decision making, migration-related information such as the mobility path and load status information needs to be exchanged periodically among fog nodes. However, the impact of the periodicity timing and the resulting overhead in terms of generated network traffic has not been studied much. For instance, the authors in BIB002 state that this information exchange does not occur frequently, thus leading to a low overhead. However, this has not been evaluated in a real-world context. Yet, this aspect is of extreme importance in the context of fog computing, which was envisioned to alleviate the burden on the network and to guarantee timely service provisioning to end-users. • User mobility: To predict or not to predict: While there are several studies that push toward predicting future user positions to premigrate the services to the nodes placed in locations that the user is likely to visit, other studies consider that users' mobility is generally difficult to predict and is characterized with inherent uncertainty. To this end, they rely on online learning mechanisms leveraging recent advances in reinforcement learning. Certainly, each approach can have a set of advantages and disadvantages. For instance, when predictions are obtained with high accuracy, considerable time savings can be achieved, which is beneficial for latency-sensitive edge applications. However, when prediction errors occur, this approach can result in non-negligible losses that may affect the applications' performance. On the other hand, in online learning, learning can be performed through trial and error following consecutive interactions with the considered environment. However, converging to optimal decisions may require a lot of iterations, which may not be tolerated in time-constrained applications, which are typical in fog/edge computing environments. An appropriate combination of both approaches may therefore be needed.