Toward Field-Level Device Orchestration in Industrial Multiaccess Edge Computing Deployments: A Unified IT–OT Framework

Latency and energy aware rate maximization in MC-NOMA-based multi-access edge computing: A two-stage deep reinforcement learning approach

Multi-access edge computing (MEC) is considered a promising technology to facilitate mission-critical vehicular applications, such as automatic driving, path-planning, and navigation. By offloading delay-sensitive or computation-intensive tasks from vehicles to MEC servers (MECSs), edge computing significantly enhances the computing capacity of vehicles with limited computing resources. However, MECSs may have uneven loads as vehicles are not evenly distributed across MEC systems and vehicles do not offload their tasks evenly. As a result, those offloaded tasks have high latency or be blocked. In addition, service interruption would happen frequently due to task migration caused by the high mobility. Due to the high mobility of vehicles and load dynamics at MECSs, computation tasks can migrate simultaneously to a particular MECS or migrate to a heavily congested MECS. Therefore, it is challenging to determine the migration decision, i.e., whether/where to migrate, among MECSs. In conventional methods, computation tasks are fully migrated to the MECS corresponding to the vehicle’s trajectory. By contrast, in this study, tasks are migrated partially or fully to other MECSs in the collaborative edge computing system. To reduce the task execution latency and improve the system throughput, the proposed method selects a MECS that optimizes load balancing among MECSs and partitions the task to migrate for the MECS. Through simulations, compared with the conventional methods, the proposed method can increase the satisfaction of quality of service (QoS) requirements and MEC system throughput by optimizing the load balancing and task partitioning.

The Multi-access Edge Computing (MEC) paradigm and the Cellular Vehicle-to-Everything (C-V2X) technology prove to be good candidates to address the vehicular applications' demands of high-performing connectivity and low-latency access to computing and storage resources. MEC provides cloud-like resources at the network edge, i.e., at the Multi-access Edge (ME) host. While vehicles move around, a service application instance running on a ME host may be triggered to move to another ME host to better support the application's demands. As a side effect, this migration may undermine service continuity. In this paper, we refer to the latest available ETSI MEC and 3GPP C-V2X specifications to investigate the issue of service migration between ME hosts in the context of vehicular communication. Early experimental results provide measures of the service migration latencies when ME applications run as Docker containers.

Multi‐access edge computing (MEC) is an evolving paradigm of the Internet of things (IoT) applications. The MEC is a complement to traditional cloud computing where services are extended closer to the network and so to the end users. As mobile users can use MEC services in an inter‐domain, security is one of the challenging questions, how to protect IoT applications in MEC environments from abuses? In addition, considering real‐world MEC supported IoT applications (e.g. airport) where a user is always on the move from one network to another network. This scenario also poses many security challenges. To mitigate this, an authentication mechanism can play an important role to defend MEC from unauthorized access. Thus, an authentication mechanism is needed that can support mobility for MEC users. Moreover, establishing a session key is also highly desirable between the MEC users and foreign‐edge servers to enable secure communication in MEC environments. In addition, how to maintain users' anonymity is another important security requirement, as MEC users do not want to disclose their private information. To solve these issues, this chapter proposes a new efficient and anonymous mobility supported mutual authentication scheme in MEC environments. The scheme utilizes the password and smartcard as two‐factor authentications and facilitates many services to the users such as user anonymity, mutual authentication, and secure session key establishment in mobility supported environments. In addition, it allows users to choose/update their password regularly, whenever needed. Security and performance evaluation show the practicality of the proposed scheme.

Edge computing has emerged as a hot topic in recent years, coinciding with the advancement of 5G implementation. Utilizing multi-access edge computing (MEC), the transmission of mobile data will occur in real time and ultra low latency. Edge computing is required to deliver 5G service. MEC is a cloud computing evolution that moves applications from centralized data centers to the network edge, bringing technology resources closer to end users and their devices. Mitratel, the largest tower provider in one of the most attractive market in the world - Indonesia. It provides nationwide coverage in highly attractive across the country. Moreover, it has aspiration to become the leading operator of mission critical infrastructure to enhance Indonesia's digital future. Now it is expanding to provide full suite of digital infrastructure solution. In the tower ecosystem, Mitratel also offers various support services, such as edge infra solution, tower fiberization, small cell, power-to-tower, and other technologies to help accelerating 5G development and provide access and convenience to cellular service providers throughout Indonesia. Telkomsel is the largest mobile operator in Indonesia with varieties of mobile services and more than 170 million subscribers. Both of them are subsidiaries of Telkom, an Indonesian multinational telecommunication holding group. Mitratel, Telkomsel, and Telkom works together to conduct proof of concept (PoC) of MEC on tower ecosystem Indonesia. Mitratel utilize its BTS Room which functions as a micro edge data center to provide edge infra solution which consist of conditioned room, power, connectivity, Network Monitoring System (NMS) and others supported infrastructures. While, Telkomsel brings mobile virtual networks, edge servers and use cases. Mitratel, Telkomsel, and Telkom have successfully completed the MEC PoC at the Mitratel BTS Room in the GBK (Gelora Bung Karno), Jakarta. This PoC shows that tower ecosystem of Mitratel is reliable to deliver MEC service proven by the result of URRLC measurement. In comparison, with MEC and without MEC, the URLLC rates are 5 ms and 15 ms, respectively. In addition, use cases such as Trash Detection, Mask Detection and Virtual Reality are successfully implemented. From the Comparison between micro edge data center of Mitratel and the American Tower, several differences were identified. This will be subject of the further development of this PoC.

The term edge computing is continuously becoming more eminent across the globe as its distributed computing architype which is bringing the computation as well as diverse kind of the data storage nearer to a place wherein this is demanded for enhancing the response times as well as preserve the total available bandwidth. The term MAEC (Multi-Access Edge Computing) providing the cloud computing (CC) competences as well as information technology (IT) services located on edges of diverse networks. The MAEC acts through the effective processing of the diverse kinds of the data sets among the cloud as well as multifarious users worldwide. The multifarious variants, dispersed MAEC, executes over the public 4G as well as 5G networks for linking diverse moving assets as well as things. Although, multifarious investigations are done in prior art for efficient association of the cellular phone subscribers to MAEC in recent decade but existing strategies have numerous limitation namely pragmatic tasks offloading as well as the moving support and many others because of technological alteration. In order to, resolve these existing threats, a novel prototypical architecture is explored in the present research article that more effectively acquire faster response along with uninterrupted service distribution. The outcomes of this proposed prototypical is pragmatic and improved in comparison to the existing approaches explored by multifarious researchers in past decades. Although, considerable research are done in past in this arena for resolving the encountered threats but still there is mammoth scope to deep dive in this domain for pragmatic outcomes.

Multi‐Access Edge Computing (MEC) provides a lot of computing with low latency within the nearby portion of the network. Nevertheless, current MEC frameworks have considerable resource management and offloading issues when there are dynamic and uncertain tasks. These are poor flexibility to live with time changes, heavy backhaul traffic during data communication, the use of a static approach of encryption, and a lack of integration with chaotic models on resource achievement. Due to the complexity of encrypting all resources, the developed framework employs a novel MEC paradigm of integrating the chaos modeling of systems to both adaptively distribute resources and dynamically change the task offloading as well as lightweight chaos‐based encryption. In this study, Multi‐Access Edge Computing (MEC) is used for fast task handling near the end user. The present MEC models still face resource and offloading problems when tasks are dynamic or uncertain. In this paper, a new chaotic model‐based method is developed for resource allocation and adaptive task offloading with lightweight encryption. The system applies chaotic sequence features such as initial sensitivity and randomness to decide task distribution among different servers. This helps in balancing the load, reducing delay, and lowering energy use. A secondary chaotic key generator is used to secure data transfer without heavy computation. Simulation results show that the proposed model achieves an average latency of 0.37 s and task processing time of 0.21 s, which are better than the standard MEC methods. Overall, the chaotic idea gives flexibility and safety for real‐time MEC operation.

Multi-access edge computing (MEC) is emerging as a solution to serve offloaded tasks from mobile devices that are computing intensive and have very low latency and high bandwidth requirements. Since compute resources are limited at the MEC hosts, collaboration among hosts could enhance the capabilities of sharing limited resources while minimizing the cost of such hosts. However, the selection of optimal hosts to instantiate the user applications is a major challenge when considering the total service provisioning cost. In this paper, we formulate the MEC hosts selection problem as a binary integer problem with the objective to minimize the total cost of providing the offloading services. We extend the Balas-Geoffrion algorithm to solve the special case of binary programming problems similar to MEC host selection problem. The time complexity of the MEC host selection problem is therefore minimized. We show that our modified algorithm outperforms Balas-Geoffrion algorithm in the number of iterations required to reach the optimal solution. Then we conduct an extensive simulation to show that the overall quality-of-service of the MEC system is improved by the MEC hosts collaborations in a limited bandwidth scenario by up to 13%. However, the tradeoff is an increase in the cost of provisioning the services.

A survey of mobility-aware Multi-access Edge Computing: Challenges, use cases and future directions

Multi-access edge computing (MEC) is viewed as an integral part of future wireless networks to support new applications with stringent service reliability and latency requirements. However, guaranteeing ultra-reliable and low-latency MEC (URLL MEC) is very challenging due to uncertainties of wireless links, limited communications and computing resources, as well as dynamic network traffic. Enabling URLL MEC man-dates taking into account the statistics of the end-to-end (E2E) latency and reliability across the wireless and edge computing systems. In this paper, a novel framework is proposed to optimize the reliability of MEC networks by considering the distribution of E2E service delay, encompassing over-the-air transmission and edge computing latency. The proposed framework builds on correlated variational autoencoders (VAEs) to estimate the full distribution of the E2E service delay. Using this result, a new optimization problem based on risk theory is formulated to maximize the network reliability by minimizing the Conditional Value at Risk (CVaR) as a risk measure of the E2E service delay. To solve this problem, a new algorithm is developed to efficiently allocate users’ processing tasks to edge computing servers across the MEC network, while considering the statistics of the E2E service delay learned by VAEs. The simulation results show that the proposed scheme outperforms several baselines that do not account for the risk analyses or statistics of the E2E service delay.

With the concept of Mobile Edge Computing or Multi-access Edge Computing (MEC), computation resources can be located closer to the users in proximity. MEC can operate specific services for the user requests to reduce overhead in the main cloud. The MEC infrastructure also facilitates service migration and relocation to follow the user movement in order to maintain service connection and therefore reduce the service downtime. However, since most MEC components are meant to be deployed on constrained IoT devices, they inherit many of the limitations that come with IoT, especially in terms of limited resources and security. In this paper, we focus on the capacity aspect of the MEC infrastructure to ensure the services have enough resources during the migration process. We note that the distributed and decentralized nature of MEC networks requires the additional endorsement of the capacity information reported by the network nodes, which is especially critical since MEC nodes can be hacked or tampered with, leading to failures during the migration process. To address this issue, we propose a machine learning-based solution that applies historical resources metric data to predict potential node behavior under attack scenarios. Our experiments showed that artificial neural network (ANN) is the best model for the production version that will be built as an extension in Kubernetes.

With the explosive growth of intelligent and mobile devices, the current centralized cloud computing paradigm is encountering difficult challenges. Since the primary requirements have shifted towards implementing real-time response and supporting context awareness and mobility, there is an urgent need to bring resources and functions of centralized clouds to the edge of networks, which has led to the emergence of the edge computing paradigm. Edge computing increases the responsibilities of network edges by hosting computation and services, therefore enhancing performances and improving quality of experience (QoE). Fog computing, multi-access edge computing (MEC), and cloudlet are three typical and promising implementations of edge computing. Fog computing aims to build a system that enables cloud-to-thing service connectivity and works in concert with clouds, MEC is seen as a key technology of the fifth generation (5G) system, and Cloudlet is a micro-data center deployed in close proximity. In terms of deployment scenarios, Fog computing focuses on the Internet of Things (IoT), MEC mainly provides mobile RAN application solutions for 5G systems, and cloudlet offloads computing power at the network edge. In this paper, we present a comprehensive case study on these three edge computing implementations, including their architectures, differences, and their respective application scenario in IoT, 5G wireless systems, and smart edge. We discuss the requirements, benefits, and mechanisms of typical co-deployment cases for each paradigm and identify challenges and future directions in edge computing.

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.

Edge computing in 5G and Beyond 5G (B5G) networks is an essential infrastructure enabler for future technology and business developments, also known as Industry 4.0, which requires high computational power and fast service deliveries. There are many proprietary Multi-access Edge Computing (MEC) architectures defined by the service providers and the standard bodies. However, all of them require changes not only in the existing end devices, but also in the existing applications to connect with the MEC platform for edge server discovery. Furthermore, these client applications are unaware of the list of server applications running on the edge servers. It also requires installation of device application in mobile devices to subscribe and authorize with the MEC Platform, to communicate with edge servers and to find the available MEC services, which cause additional overhead to the device. In this paper, we propose a novel solution named Computational and Location-Aware Middleware (CLM), to compute and locate the nearest server for service discovery and computational offload for the mobile device. The proposed solution does not require any changes in existing applications for edge server discovery, and it has been successfully prototyped in Samsung devices with Android Pie for evaluation. The results show a reduction in edge server discovery overheads to one Round Trip Time and remarkable performance gain of up to 85% for service discovery.

With the rapid development of Internet-of-Things (IoT) devices and mobile communication technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm to extend cloud computing and storage capabilities to the edge of cellular networks, near to IoT devices. MEC enables IoT devices with limited battery capacity and computation/storage capabilities to execute their computation-intensive and latency-sensitive applications at the edge of the networks. However, to efficiently execute these applications in MEC systems, each task must be properly offloaded and scheduled onto the MEC servers. Additionally, the MEC servers may intelligently balance and share their computing resources to satisfy the application QoS and QoE. Therefore, effective resource allocation (RA) mechanisms in MEC are vital for ensuring its foreseen advantages. Recently, Machine Learning (ML) and Deep Learning (DL) have emerged as key methods for many challenging aspects of MEC. Particularly, ML and DL play a crucial role in addressing the challenges of RA in MEC. This paper presents a comprehensive survey of ML/DL-based RA mechanisms in MEC. We first present tutorials that demonstrate the advantages of applying ML and DL in MEC. Then, we present enabling technologies for quickly running ML/DL training and inference in MEC. Afterward, we provide an in-depth survey of recent works that used ML/DL methods for RA in MEC from three aspects: (1) ML/DL-based methods for task offloading; (2) ML/DL-based methods for task scheduling; and (3) ML/DL-based methods for joint resource allocation. Finally, we discuss key challenges and future research directions of applying ML/DL for resource allocation in MEC networks.