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

How to perform efficient service migration in a mobile edge environment has become one of the research hotspots in the field of service computing. Most service migration approaches assume that the mobile edge network on which the migration depends is stable. However, in practice, these networks often fluctuate greatly due to the fault of edge devices, resulting in unexpected service interruptions during the migration process. Besides, most of the existing solutions do not consider the migration cost and path selection in the event of edge network fault. Aiming at the above problems, we propose a service migration approach based on network fault prediction (SMNFP) for mobile edge environment. The SMNFP method first introduces the software-defined network as a global controller, which is used to monitor and collect the changing of the edge network and schedule the migration tasks. Second, a network fault prediction model based on Wide&Deep model is proposed to predict the upcoming faults in the network according to the alarm information of network equipment. Finally, the service migration problem is constructed as a Markov decision process, and a fault penalty function is introduced to avoid faulty nodes, together with the deep Q-learning method to solve the migration strategy. Simulation experiments are conducted on the public metro network fault dataset, and results show the proposed method can effectively predict network faults and complete service migration.

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.

Virtual machine (VM) is an important mechanism to multiplex computer resources. The increasing popularity of network computing has renewed research interests in the adaptive and distributed virtual machines. Service migration is a vital technique to construct reconfigurable VMs. By incorporating mobile agent technology, VM systems can improve their resource utilization, load-balancing and fault-tolerance significantly. This work focuses on the decision problem of hybrid mobility for load-balancing in reconfigurable distributed VMs. We tackle this problem from three aspects: migration candidate determination, migration timing and destination server selection. The service migration timing and destination server selection are formulated as two optimization models. We derive the optimal migration policy for distributed and heterogeneous systems based on stochastic optimization theories. Renewal processes are applied to model the dynamics of migration. We solve the agent migration problem by dynamic programming and extend the optimal service migration decision by considering the interplay of the hybrid mobility. Our decision policy is complementary to the existing service and agent migration techniques. Its accuracy is verified by simulations.

The advent of the Internet-of-Things (IoT), which streams a wide range of computation-intensive applications with strict Quality of Service (QoS) requirements, has caused a paradigm shift from cloud computing to edge computing. Edge computing can drastically reduce latency and improve QoS. However, various dynamic changes can affect service continuity, thus requiring service migration. The dynamic computation load is one of the changes that are typically overlooked in service migration. In this paper, we propose the Dynamic Load-based Proactive Migration (DLPM) scheme. DLPM adopts a finite-state machine (FSM) that models the dynamic computation load, and proactively migrates computation tasks based on the associated transition probabilities. We formulate the service migration problem as an integer linear programming (ILP) optimization problem that aims to minimize the delay. We provide an analytical solution to the optimization problem using the KKT conditions and Lagrangian analysis. Performance evaluation shows that DLPM yields significant improvements in terms of delay and number of migrations compared to the reactive migration approach.

Mobile edge computing (MEC) can supplement cloud computing by helping to overcome the limitations of long physical transmission distances and accelerating the responsiveness of edge computing servers. In 5G (fifth generation) cellular networks, adopting MEC can guarantee ultralow latency. To enhance the MEC quality, optimization of the user service profile migration according to the user mobility is essential. However, this optimization establishes an NP-hard problem. Moreover, high-speed 5G base stations with MEC servers often experience high energy consumption. As conventional service migration algorithms such as those based on profile tracking and game theory tend to fall in local optima and neglect energy consumption constraints, we propose a memetic algorithm based on community detection local search (MA-CDLS) to continuously optimize the service migration in 5G MEC scenarios. During busy periods or in crowded areas, MA-CDLS adopts a single-objective optimization of user-perceived latency to achieve high-performance 5G services. During light-load periods or in uncrowded areas, MA-CDLS uses two measures, namely the user-perceived latency and energy consumption, to realize energy-efficient 5G services. MA-CDLS effectively reduces the search space and speeds up the elite selection in the meme operator. Experiments in simulated scenarios show that MA-CDLS achieves a lower user-perceived latency and energy consumption, than the traditional profile tracking and game theory methods, especially during congestion.

<p>The next generation of wireless networks, 6G is being designed with data-intensive applications. One of the key technologies that will enable 6G is blockchain technology. The emergence of blockchain technology and 6G networks has revolutionized service migration. Service migration in 6G networks is a complex process that requires the integration of new technologies, such as artificial intelligence (AI), edge computing, and network slicing. Motivated by these facts, this comprehensive review includes an overview of blockchain and service migration integration in 6G. First, state of art, development frame work and related works were introduced. Then, we used content analysis by WordStat software and bibliographic analysis by VOSviewer to analysis the current status of service migration and blockchain integration in 6G networks. Next, patterns and characteristics, benefits and challenges and potential cases were reviewed. Then, we proposed an architectural blockchain-based model including decentralized architecture, edge computing, network slicing, software-defined networking, and 5G-6G interworking in 6G. Finally, we described potential application service migration-based in 6G networks including digital twin (DT), holograms, robot avatar, high density internet of things (IoT), AR and VR in 6G and collected open research and future directions of service migration and blockchain.</p>

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.

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.

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) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics.KeywordsMobile edge computingService migrationFault-tolerantResubmissionReplication

Envisioned low-latency services in 5G, like automated\n\t\t\t\t driving, will rely mainly on Multi-access Edge Computing\n\t\t\t\t (MEC) to reduce the distance, and hence latency, between users\n\t\t\t\t and the remote applications. MEC hosts will be deployed close to\n\t\t\t\t mobile base stations, constituting a highly distributed computing\n\t\t\t\t platform. However, user mobility may raise the need to migrate a\n\t\t\t\t MEC application among MEC hosts to ensure always connecting\n\t\t\t\t users to the optimal server, in terms of geographical proximity,\n\t\t\t\t Quality of Service (QoS), etc. However, service migration may\n\t\t\t\t introduce: (i) latency for users due to the downtime duration;\n\t\t\t\t (ii) cost for the network operator as it consumes bandwidth\n\t\t\t\t to migrate services. One solution could be the use of service\n\t\t\t\t replication, which pro-actively replicates the service to avoid service\n\t\t\t\t migration and ensure low latency access. Service replication\n\t\t\t\t induces cost in terms of storage, though, requiring a careful\n\t\t\t\t study on the number of service to replicate and distribute in\n\t\t\t\t MEC. In this paper, we propose to compare service migration\n\t\t\t\t and service replication via an analytical model. The proposed\n\t\t\t\t model captures the relation between user mobility and service\n\t\t\t\t duration on service replication as well as service migration costs.\n\t\t\t\t The obtained results allow to propose recommendations between\n\t\t\t\t using service migration or service replication according to user\n\t\t\t\t mobility and the number of replicates to use for two types of\n\t\t\t\t service.

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.

Service migration with edge collaboration: Multi-agent deep reinforcement learning approach combined with user preference adaptation

In recent years, the Edge Computing (EC) paradigm has emerged as an enabling factor for developing technologies like the Internet of Things (IoT) and 5G networks, bridging the gap between Cloud Computing services and end-users, supporting low latency, mobility, and location awareness to delay-sensitive applications. An increasing number of solutions in EC have employed machine learning (ML) methods to perform data classification and other information processing tasks on continuous and evolving data streams. Usually, such solutions have to cope with vast amounts of data that come as data streams while balancing energy consumption, latency, and the predictive performance of the algorithms. Ensemble methods achieve remarkable predictive performance when applied to evolving data streams due to several models and the possibility of selective resets. This work investigates a strategy that introduces short intervals to defer the processing of mini-batches. Well balanced, our strategy can improve the performance (i.e., delay, throughput) and reduce the energy consumption of bagging ensembles to classify data streams. The experimental evaluation involved six state-of-art ensemble algorithms (OzaBag, OzaBag Adaptive Size Hoeffding Tree, Online Bagging ADWIN, Leveraging Bagging, Adaptive RandomForest, and Streaming Random Patches) applying five widely used machine learning benchmark datasets with varied characteristics on three computer platforms. As a result, our strategy can significantly reduce energy consumption in 96% of the experimental scenarios evaluated. Despite the trade-offs, it is possible to balance them to avoid significant loss in predictive performance.

In the Internet of Things (IoT), a large number of smart mobile devices, sensors, and actuators are deployed at the edge of the network. Processing of this large volume of data is a key factor of future Internet and communication systems such as Fifth Generation (5G) cellular networks, which provide dramatic enhancement in data rates, reliability, and energy consumption as compared to existing mobile communication infrastructure. In the context of service migration (inter-Public Land Mobile Network (PLMN) roaming) between different mobile network operators, end-users experience longer service interruption due to network re-connection specifically in cross-border scenarios. In this paper, we propose Mobile-access edge computing (MEC)-based intelligent service migration architecture to improve the service continuity (SC) in multi-domain Long Term Evolution (LTE) and 5G cellular networks. We introduce the concept of virtual roaming requests processed on edge node networks and predict in advance the development of service in visiting networks. As result, we can reduce the service interruption time during service migration in a multi-domain cellular network. Moreover, this MEC-based prediction also helps to improve message communication delay and to use cellular core network resource efficiently.