A Network Fault Prediction-Based Service Migration Approach for Unstable Mobile Edge Environment

Joint edge caching and dynamic service migration in SDN based mobile edge computing

Mobile Edge Computing (MEC) is a promising paradigm to support high-quality time-sensitive applications. In this paper, we investigate the service migration (i.e., whether, when, and where to migrate the services) to seamlessly serve mobile users in small-cell MEC systems. The service migration is formulated as an optimization problem to minimize the long-term system average delay that consists of queuing, communication, and migration delays. Considering the dynamic user mobility and network conditions, the formulated problem is non-convex and difficult to solve in real time. To this end, we propose a Mobility-aware Service Migration scheme, named MSM, to make real-time decisions on service migrations by utilizing reinforcement learning (RL) approaches. Specifically, we first design a user classification mechanism based on users’ mobility patterns to reduce the complexity of decision-making. We then formulate the service migration as a Markov decision process and devise an RL-based framework to make service migration decisions in real time in the dynamic MEC environment. Extensive data-driven experiments demonstrate the efficacy of MSM in reducing the system average delay.

By enabling distributed computing at the network edge, Mobile Edge Computing (MEC) supports computingintensive, delay-sensitive tasks on resource-limited mobile devices, but also introduces service migration challenges. Existing approaches mainly rely on centralized decisions with full system information. However, because of dynamic state changes, scalability bottlenecks, and privacy risks, complete information is often infeasible, leading to partial observability in service migration. To address this, we propose State Cognition for Mobile Edge Computing (SCMDM), a user-oriented migration model that leverages only local data, removing dependency on global knowledge. We formulate the problem as a Partially Observable Markov Decision Process (POMDP) to jointly optimize service quality and migration cost. SCMDM integrates a State Cognitive Model (SCM) to infer hidden states from observations and a Migration Decision Model (MDM) to determine optimal migration strategies. Experiments on two real-world mobility traces demonstrate that SCMDM converges faster and achieves superior decision-making compared to existing alternatives.

Although mobile edge computing (MEC), as an extension of the cloud computing paradigm to edge networks, overcomes some obstacles of traditional mobile cloud computing, i.e., the reduced response time particularly, it is a nontrivial task to efficiently deploy virtual machine replica copies (VRCs) supporting multiple applications among numerous MEC servers in edge networks. To combat this issue, we are motivated to investigate in detail the optimal placement of VRCs to minimize the average response time (MART) in the MEC architecture with various requests demand among multiple applications and capacity constraints of MEC servers in edge networks. Besides optimal enumeration placement algorithm (OEPA) as benchmark, we design latency aware heuristic placement algorithm (LAHPA) with much lower computation complexity than that of OEPA. To enhance the performance of LAHPA on MART, clustering enhanced heuristic placement algorithm (CEHPA) is proposed, focusing on the optimal VRC placement in each cluster. We also develop substitution enhanced heuristic placement algorithm (SEHPA) to avoid falling into local optimal solutions. As corroborated by extensive simulation results, the performance of SEHPA on MART is very close to that of OEPA compared with LAHPA and CEHPA. Note that CEHPA also outperforms LAHPA, and both are better than a general greedy placement algorithm. Furthermore, we evaluate the normalized total cost for services provision in edge networks, where SEHPA can also get more outstanding results than other algorithms.

In the mobile edge computing (MEC) environment, tasks can be offloaded to edge servers for better service. However, the locations of users are not fixed. As users move, tasks need to be migrated to the vicinity of users to ensure quality of service (QoS). At present, most research considers the problem of service migration from the perspective of a single user. The single user migration scheme is only concerned with it’s own optimal choice, without considering the interference between multiple users, which will lead to multiple users blindly competing for resources and resulting in edge server overload. This paper proposes a service migration method for resource competition, which can eliminate the problem of edge server overload caused by blind resource competition among users. Experimental results show that, compared with the existing algorithms, proposed method has lower cost and higher availability in multi-user scenarios.

Edge computing (EC) extends the ability of cloud computing to the network edge to support diverse resource-sensitive and performance-sensitive IoT applications. However, due to the limited capacity of edge servers (ESs) and the dynamic computing requirements, the system needs to dynamically update the task allocation policy according to real-time system states. Service migration is essential to ensure service continuity when implementing dynamic task allocation. Therefore, this article investigates the long-term dynamic task allocation and service migration (DTASM) problem in edge-cloud IoT systems where users’ computing requirements and mobility change over time. The DTASM problem is formulated to achieve the long-term performance of minimizing the load forwarded to the cloud while fulfilling the seamless migration constraint and the latency constraint at each time of implementing the DTASM decision. First, the DTASM problem is divided into two subproblems: 1) the user selection problem on each ES and 2) the system task allocation problem. Then, the DTASM problem is formulated as a Markov decision process (MDP) and an approach based on deep reinforcement learning (DRL) is proposed. To tackle the challenge of vast discrete action spaces for DTASM task allocation in the system with a mass of IoT users, a training architecture based on the twin-delayed deep deterministic policy gradient (DDPG) is employed. Meanwhile, each action is divided into a differentiable action for policy training and one mapped action for implementation in the IoT system. Simulation results demonstrate that the proposed DRL-based approach obtains the long-term optimal system performance compared to other benchmarks while satisfying seamless service migration.

Edge caching and computation offloading are potential technologies that can greatly reduce the pressure on the backhaul link and improve user response speed. However, a large amount of data exchange and resource transactions between users have security problems, and it is difficult to encourage users to participate. In this article, we propose a blockchain-based consensus protocol, as well as detailed formula steps and theoretical analysis in a mobile edge network D2D environment, where the blockchain acts as a trusted third party to maintain transactions between users. In order to improve the throughput of the blockchain, we jointly consider the trust feature of the blockchain nodes and controllers and the computing resource of the blockchain system, and by defining the state space, the problem is transformed into a Markov decision process. However, the traditional method is difficult to solve the joint problem, so we propose a method based on hierarchical reinforcement learning. The simulation result shows the effectiveness of the proposed scheme.

Mobile Edge Computing (MEC) is the concept of placing a cloud computing server to run at the network edge near the user. The faster the user moves, the higher the probability of leaving the coverage of an MEC server. Seamless service migration should be implemented to assure service continuity. However, the time required for service migration is not negligible; thus, the frequent service migration should be avoided. In this study, we define the service consumption plan optimization problem and prove its NP-completeness. To solve the problem, we proposed a genetic algorithm-based method and conducted vast experiments to evaluate the algorithm with respect to other baselines based on real-world data sources. The results showed that the proposed method generates improved service consumption plans than other baselines in all the scenarios we set out.

Mobile edge computing (MEC) is a key enabler for ultra-low latency in heterogeneous dense cellular networks in the 5G era and beyond, by deploying services at the network edge. Due to high user mobility, the services are usually migrated to follow the users by predicting the user trajectory to achieve a balance between energy consumption and service latency. However, service migration for multi-user heterogeneous dense cellular networks is challenging because (1) the user trajectory prediction, which is crucial for service migration, becomes intractable with a large number of users, and (2) making service migration decisions for each user independently is subjected to interference among the users. Therefore, in this study, we formulated the service migration of all the users in MEC-enabled heterogeneous dense cellular networks as an optimization problem, with the objective of minimizing the average energy consumption while satisfying the service latency requirements, taking into account the interference among different users. Next, we developed an efficient energy-efficient online algorithm based on the Lyapunov and particle swarm optimizations, called EGO, to resolve the original problem without predicting the trajectories of the users. Finally, a series of simulations based on real-world mobility traces of vehicles in Bologna were conducted to establish the superiority of the EGO algorithm over state-of-the-art solutions.

Mobile edge computing (MEC) is a promising approach to support latency-sensitive and computation-intensive applications by sinking the computational resources to network edges. Due to high-speed and ubiquitous mobility, mobile devices such as autonomous vehicles may encounter service interruptions or increased communication latency when transitioning across network gateways. However, the existing mechanisms fall short of simultaneously achieving both continuity and efficient performance. To address these issues, we investigate a service migration mechanism for MEC, namely SeMiMEC, to support service continuity, while achieving low-latency requirements. In SeMiMEC, both handover management and application migration procedures are novelly devised to collaboratively realize service migration. For service migration, a comprehensive set of signaling operations is designed to provide efficient support for ongoing sessions in each handover scenario. For data communication, the SeMiMEC binds the session information with the identifier to address the issue of certain applications not accommodating changes in communication addresses during an active session. Experimental results show that the proposed SeMiMEC reduces the interruption latency and packet forwarding latency while reducing the data delivery cost as well.

Mobile micro-cloud is an emerging technology in distributed computing, which\nis aimed at providing seamless computing/data access to the edge of the network\nwhen a centralized service may suffer from poor connectivity and long latency.\nDifferent from the traditional cloud, a mobile micro-cloud is smaller and\ndeployed closer to users, typically attached to a cellular basestation or\nwireless network access point. Due to the relatively small coverage area of\neach basestation or access point, when a user moves across areas covered by\ndifferent basestations or access points which are attached to different\nmicro-clouds, issues of service performance and service migration become\nimportant. In this paper, we consider such migration issues. We model the\ngeneral problem as a Markov decision process (MDP), and show that, in the\nspecial case where the mobile user follows a one-dimensional asymmetric random\nwalk mobility model, the optimal policy for service migration is a threshold\npolicy. We obtain the analytical solution for the cost resulting from arbitrary\nthresholds, and then propose an algorithm for finding the optimal thresholds.\nThe proposed algorithm is more efficient than standard mechanisms for solving\nMDPs.\n

Due to the development of 5G networks, computation intensive applications on mobile devices have emerged, such as augmented reality and video stream analysis. Mobile edge computing is put forward as a new computing paradigm, to meet the low-latency requirements of applications, by moving services from the cloud to the network edge like base stations. Due to the limited storage space and computing capacity of an edge server, service placement is an important issue, determining which services are deployed at edge to serve corresponding tasks. The problem becomes particularly complicated, with considering the stochastic arrivals of tasks, the additional latency incurred by service migration, and the time spent for waiting in queues for processing at edge. Benefiting from reinforcement learning, we propose a deep Q network based approach, by formulating service placement as a Markov decision process. Real-time service placement strategies are output, to minimize the total latency of arrived tasks in a long term. Extensive simulation results demonstrate that our approach works effectively.

Content caching in mobile edge networks has stirred up tremendous research attention. However, most existing studies focus on predicting content popularity in mobile edge servers (MESs). In addition, they overlook how the content is cached, especially how to cache the content with user devices. In this paper, we propose CMU, a three-layer (Cloud-MES-Users) content caching framework and investigate the performance of different caching strategies under this framework. A user device who has cached the content can offer the content sharing service to other user devices through device-to-device communication. In addition, we prove that optimizing the transmission performance of CMU is an NP-hard problem. We provide a solution to solve this problem and describe how to calculate the number of distributed caching nodes under different parameters, including time, energy and storage. Finally, we evaluate CMU through a numerical analysis. Experiment results show that content caching with user devices could reduce the requests to Cloud and MESs, and decrease the content delivery time as well.

Service migration is crucial in mobile edge computing (MEC) to ensure seamless service provision as users move. Although some migration schemes have been proposed, they fail to efficiently support the migration of microservices in a directed acyclic graph (DAG)-based service across different edge servers, resulting in high service latency. This paper focuses on the DAG-based service migration problem and proposes a collaborative microservice migration framework for MEC, aiming to minimize the service migration latency while efficiently distributing the migration workload across edge servers. We divide edge servers into clusters and formulate the DAG-based service migration problem as a two-stage optimization problem. In the first stage, a deep reinforcement learning-based service pre-migration algorithm is developed to identify the optimal cluster of edge servers for hosting the migrated service. In the second stage, a microservice migration algorithm is devised, utilizing topological sorting and network flow techniques to further determine the target edge server for each microservice. Our design addresses the inherent dependencies among microservices within a DAG task and adapts well to dynamic network environments. Experimental results on real-world datasets demonstrate that our approach significantly reduces service migration latency.

Mobile edge computing (MEC) provides a promising approach to significantly reduce network operational cost and improve quality of service (QoS) of mobile users by pushing computation resources to the network edges, and enables a scalable Internet of Things (IoT) architecture for time-sensitive applications (e-healthcare, real-time monitoring, and so on.). However, the mobility of mobile users and the limited coverage of edge servers can result in significant network performance degradation, dramatic drop in QoS, and even interruption of ongoing edge services; therefore, it is difficult to ensure service continuity. Service migration has great potential to address the issues, which decides when or where these services are migrated following user mobility and the changes of demand. In this paper, two conceptions similar to service migration, i.e., live migration for data centers and handover in cellular networks, are first discussed. Next, the cutting-edge research efforts on service migration in MEC are reviewed, and a devisal of taxonomy based on various research directions for efficient service migration is presented. Subsequently, a summary of three technologies for hosting services on edge servers, i.e., virtual machine, container, and agent, is provided. At last, open research challenges in service migration are identified and discussed.