Service migration versus service replication in Multi-access Edge Computing

Multi-access edge computing (MEC) is an emerging computing paradigm that brings services from the centralized cloud to nearby network edge to improve users' Quality of Experience (QoE). As massive services with dynamic Quality of Service (QoS) are available in MEC, it becomes challenging for users to find reliable services that satisfy their needs. Therefore, service recommendation technology is urgently needed in MEC. Although existing service recommendation methods work well on recommending popular services that users might be interested in, they fail to recommend services with reliable QoS in the MEC environment. To tackle this issue, an accurate and reliable service recommendation (ARSR) approach based on bilateral perception is proposed, which aims to proactively recommend reliable services by perceiving both users' service demands and multi-QoS of candidate services. ARSR consists of three main steps. First, a user's service demand is estimated by a context-aware service demand prediction method based on an improved online deep learning model. Then, multiple QoS attributes of candidate services are forecasted by a multidimensional contexts-aware QoS prediction method based on an improved multi-task deep neural network. Finally, the optimal service is recommended to the user based on the predicted QoS. Extensive experiments have been carried out to verify the proposed approach and to prove its performance superiority.

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.

Multi-access edge computing (MEC) promises to deliver localized computing power and storage. Coupled with low-latency 5G radio access, this enables the creation of high added-value services for mobile users, such as in-vehicle infotainment or remote driving. The performance of these services as well as their scalability will however depend on how MEC will be deployed in 5G systems. This paper evaluates different MEC deployment options, coherent with the respective 5G migration phases, using an accurate and comprehensive end-to-end (E2E) system simulation model (exploiting Simu5G for radio access and Intel CoFluent for core network and MEC), taking into account user-related metrics, such as response time or MEC latency. Our results show that 4G radio access is going to be a bottleneck, preventing MEC services from scaling up. On the other hand, the introduction of 5G will allow a considerable higher penetration of MEC services.

Multi-access edge computing (MEC) extends cloud computing capabilities to the edge of the network which enables real-time and low-latency services for mobile users. To ensure that the delay requirement of the services is met even in the event of user mobility, service migration across different edge clusters is needed. Trusted migration and proper destination node selection across domains are two key ingredients for cross-edge migration. To this end, we propose a blockchain-based service migration framework to establish trust among different edge clusters. We use Kubernetes as our cluster orchestrator platform for containerized services. In addition, we model the problem as a Markov Decision Process (MDP) and employ the asynchronous advantage actor-critic (A3C) algorithm to select suitable destinations. The proposed approach is corroborated by a simulation study.

Mobility-aware task offloading in MEC with task migration and result caching

ABSTRACT6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

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

Multi-access edge computing (MEC) is a new 5G enabling technology proposed to reduce latency by bringing cloud computing capability closer to IoT and mobile device users. MEC may be prone to unreliable communication as a result of deadlock during resource provisioning. Deadlock may occur due to a huge number of devices contending for a limited amount of resources if adequate measures are not put in place. It is crucial to eradicate deadlock while scheduling and provisioning of resources on MEC to achieve highly reliable and available system. In this paper, a deadlock avoidance resource provisioning algorithm is proposed for Industrial IoT devices using MEC platforms to ensure the higher reliability of network interactions. The proposed scheme incorporates a banker’s resource-request algorithm using software-defined networking to reduce communication overhead. Simulation Results have shown that system deadlock can be prevented by applying the proposed algorithm which ultimately leads to a more reliable network interaction between mobile stations and MEC platforms.

In multi-access edge computing (MEC) networks, parallelized service function chains (P-SFCs) can provide low-delay network services for mobile users by deploying virtualized network functions (VNFs) to process user requests in parallel. These VNFs are unreliable due to software faults and server failures. A practical way to address this is to deploy idle backup VNFs (BVNFs) near these active VNFs and activate them when active VNFs fail. However, deploying BVNFs preempts server resources and decreases the number of accepted user requests. Thus, this paper proposes a reliability enhancement approach that uses BVNFs satisfying the delay requirement as active VNFs to form P-SFCs, which contributes to the delay reduction and reliability enhancement. Since the resource capacities of edge servers can only deploy a certain number of P-SFCs and BVNFs, establishing how to deploy the minimum number of P-SFCs and BVNFs to satisfy the delay and reliability requirements of mobile users and maximize the number of accepted user requests is a challenging problem. In this paper, we first model the dynamics of delay and reliability caused by VNF parallelization and BVNFs deployment, then formulate the joint deployment problem of P-SFCs and BVNFs. Next, we design an approximation algorithm to deploy critical VNFs and BVNFs on a target edge server and schedule the data traffic of user requests processed by P-SFCs. Experimental results based on real-world datasets show that our proposed algorithm outperforms two benchmark algorithms in terms of throughput, delay, reliability, and resource utilization.

5G ecosystems will benefit significantly from Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV). While NFV allows for dynamical deployment of virtualized network functions, MEC allows applications to be deployed close to mobile users, thus reducing latency. When used together, NFV and MEC bring flexibility and enhanced performance to meet the user’s demand. However, to increase service availability and maximize the convenience of MEC users, an MEC framework has to interface with multiple NFV orchestrators to utilize running network functions in an efficient manner. We introduce in this demonstration APMEC, a framework addressing the above two challenges. Via a combination of an interactive GUI, KPI views and a live-demo setup, we will showcase the advanced features of the framework.

The rapid development of 5G technology and edge computing prompts emerging edge applications with heterogeneous requirements for network services with respect to latency, reliability and throughput. Although content distribution networks (CDN) and edge computing proposals such as multiaccess edge computing (MEC) improve quality of service by deploying services close to end users at the edge, the session efficiency of service still is constrained by host name resolution which also leads to quality degradation of service in the case of user mobility. In this article, we proposed a novel network architecture, Named Service Edge Network (NSEN), on the basis of DNS protocol. We enabled NSEN functionality in the MEC reference architecture and presented high-efficiency session models to satisfy the requirements of various edge applications and enhance the mobility support in distributed MEC environment. The simulation and experiment results demonstrate the NSENenabled MEC architecture can improve session efficiency of MEC services in 5G cellular networks in terms of latency and throughput.

Multi-access Edge Computing (MEC) technology has emerged to overcome traditional cloud computing limitations, challenged by the new 5G services with heavy and heterogeneous requirements on both latency and bandwidth. In this work, we tackle the problem of clustering access points in MEC environments, introducing a set of clustering models to be deployed at the pre-provisioning phase. We go through extensive simulations on real-world traffic demands to evaluate the performance of the proposed solutions. In addition, we show how MEC hosts capacity violation can be decreased when integrating access points clustering into the orchestration model, by investigating on solution accuracy when applied on held-out users traffic demands. The obtained results show that our approach outperforms two state-of-the-art algorithms, reducing both memory usage and execution time, by 46% and 50%, respectively, in comparison to a baseline algorithm. It surpasses the two methods in gaining control over MEC hosts capacity usage for different maximum achieved occupancy levels on MEC hosts.

TinyLedger: A Lightweight Blockchain Ledger Protocol for the MEC Network

Multi-Access Edge Computing (MEC) is an emerging computing paradigm that brings services from centralized cloud to nearby network edge to improve users' Quality of Experience (QoE). With massive services from different domains being emerging in MEC, various powerful composite services can be created with simple services to satisfy users' complex needs. However, existing service composition methods follow a passive service model and cannot proactively recommend optimal composite services to users in MEC, which seriously affect users' service experience. To tackle this issue, we propose an approach for proactive recommendation of composite services based on demand prediction. Our approach consists of three steps. First, we predict a user's service demand based on an attention enhanced deep interaction network (AEDIN) model trained with clustered data. Then, we create the optimal composite service to satisfy the predicted demand with a mobility-aware services composition method, and finally, we proactively recommend the optimal composite service to the user. The extensive experiments have been carried out to verify our proposed approach and prove its performance superiority.

Joint optimization of phase shift and task offloading for RIS-assisted multi-access edge computing in beyond 6G communication