Multi-access Edge Computing Platform Research Articles

S-LDM: Server local dynamic map for 5G-based centralized enhanced collective perception

The automotive field is undergoing significant technological advances, which includes making the next generation of autonomous vehicles smarter, greener and safer through vehicular networks, which are often referred to as Vehicle-to-Everything (V2X) communications. Together with V2X, centralized maneuver management services for autonomous vehicles are increasingly gaining importance, as, thanks to their complete view over the road, they can optimally manage even the most complex maneuvers targeting L4 driving and beyond. These services face the challenge of strictly requiring a high reliability and low latency, which are tackled with the deployment at orchestrated Multi-Access Edge Computing (MEC) platforms. In order to properly manage safety-critical maneuvers, these services need to receive a large amount of data from vehicles, even though the useful subset of data is often related to a specific context on the road (e.g., to specific road users or geographical areas). Decoding and post-processing a large amount of raw messages, which are then for the most part filtered, increases the load on safety-critical services, which should instead focus on meeting the deadlines for the actual control and management operations. On this basis, we present an innovative open-source, 5G & MEC enabled service, called Server Local Dynamic Map (S-LDM). The S-LDM is a service that collects information about vehicles and other non-connected road objects using standard-compliant messages. Its primary purpose is to create a centralized dynamic map of the road that can be shared efficiently with other services managing L4 automation, when needed. By doing so, the S-LDM enables these services to widely and precisely understand the current situation of sections of the road, offloading them from the need of quickly processing a large number of messages. After a detailed description of the service architecture, we validate it through extensive laboratory and pilot trials, involving the MEC platforms and production 5G networks of three major European network operations and two Stellantis vehicles equipped with V2X On-Board Units (OBUs). We show how it can efficiently handle high update rates and process each messages in less than few tenths of microseconds. We also provide a complete scalability analysis with details on deployment options, providing insights on where new instances should be created in practical 5G-based V2X scenarios.

Beyond Multi-Access Edge Computing: Essentials to Realize a Mobile, Constrained Edge

ETSI Multi-access Edge computing (MEC) main purpose is to improve latency and bandwidth consumption by keeping local traffic local while providing computing resources near the end-user. Despite its clear benefits, the next-generation of hyper-distributed applications (e.g., edge robotics, augmented environments, or smart agriculture) will exacerbate latency and bandwidth requirements, posing significant challenges to today's MEC deployments. In this work, we leverage on the current study item ETSI GR MEC 036, introducing a lightweight constrained version of a MEC platform that can be deployed in a mobile end terminal or in its closed locality. This work presents design options for cMEC, and how it can untangle the aforementioned gaps while being architectural compatible with a full-fledged MEC framework. Finally, key use cases and still open challenges are discussed, including recommendations to extend the current MEC standard towards constrained environments.

Hierarchical Aerial Computing for Internet of Things via Cooperation of HAPs and UAVs

With the explosive increment of computation requirements, the multiaccess edge computing (MEC) paradigm appears as an effective mechanism. Besides, as for the Internet of Things (IoT) in disasters or remote areas requiring MEC services, unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs) are available to provide aerial computing services for these IoT devices. In this article, we develop the hierarchical aerial computing framework composed of HAPs and UAVs, to provide MEC services for various IoT applications. In particular, the problem is formulated to maximize the total IoT data computed by the aerial MEC platforms, restricted by the delay requirement of IoT and multiple resource constraints of UAVs and HAPs, which is an integer programming problem and intractable to solve. Due to the prohibitive complexity of the exhaustive search, we handle the problem by presenting the matching game theory-based algorithm to deal with the offloading decisions from IoT devices to UAVs, as well as a heuristic algorithm for the offloading decisions between UAVs and HAPs. The external effect affected by the interplay of different IoT devices in the matching is tackled by the externality elimination mechanism. Besides, an adjustment algorithm is also proposed to make the best of aerial resources. The complexity of proposed algorithms is analyzed and extensive simulation results verify the efficiency of the proposed algorithms, and the system performances are also analyzed by the numerical results.

Seer: Learning-Based $360^{\circ }$ Video Streaming for MEC-Equipped Cellular Networks

The increasing popularity of virtual reality and 360-degree video streaming has created a demand for streaming high-quality video content over cellular networks. However, streaming 360-degree videos over cellular networks poses a challenge due to the need for ultra-low latency and high bandwidth. Although viewport adaptive streaming presents a promising solution to reduce the bandwidth requirement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> video, it remains challenging to predict the Field of View (FoV) due to the adverse impact of prediction errors on the user's Quality of Experience (QoE). Moreover, bitrate adaptation gets complicated due to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> videos and the dynamic nature of the cellular network. Therefore, in this paper, we propose an intelligent framework, termed Seer, to fully realize <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> video streaming over cellular networks by leveraging Multi-access Edge Computing (MEC). It incorporates an FoV prediction scheme and a bitrate adaptation policy into the MEC platform. In particular, the FoV prediction scheme uses perceived video features coupled with time-series based viewport prediction to achieve higher prediction accuracy. For an effective bitrate selection in cellular network conditions, a Reinforcement Learning (RL) based bitrate adaptation policy is further employed. The results demonstrate that Seer's FoV prediction scheme delivers higher prediction accuracy, while the streaming framework significantly reduces the bandwidth requirements and improves the user's QoE.

Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets.

The explosive growth of the Internet of Things (IoT) applications has imposed a dramatic increase of network data and placed a high computation complexity across various connected devices. The IoT devices capture valuable information, which allows the industries or individual users to make critical live dependent decisions. Most of these IoT devices have resource constraints such as low CPU, limited memory, and low energy storage. Hence, these devices are vulnerable to cyber-attacks due to the lack of capacity to run existing general-purpose security software. It creates an inherent risk in IoT networks. The multi-access edge computing (MEC) platform has emerged to mitigate these constraints by relocating complex computing tasks from the IoT devices to the edge. Most of the existing related works are focusing on finding the optimized security solutions to protect the IoT devices. We believe distributed solutions leveraging MEC should draw more attention. This paper presents a comprehensive review of state-of-the-art network intrusion detection systems (NIDS) and security practices for IoT networks. We have analyzed the approaches based on MEC platforms and utilizing machine learning (ML) techniques. The paper also performs a comparative analysis on the public available datasets, evaluation metrics, and deployment strategies employed in the NIDS design. Finally, we propose an NIDS framework for IoT networks leveraging MEC.

Privacy-Aware Access Protocols for MEC Applications in 5G

Multi-access edge computing (MEC) is one of the emerging key technologies in fifth generation (5G) mobile networks, providing reduced end-to-end latency for applications and reduced load in the transport network. This paper proposes mechanisms to enhance user privacy in MEC within 5G. We consider a basic MEC usage scenario, where the user accesses an application hosted in the MEC platform via the radio access network of the mobile network operator (MNO). First, we create a system model based on this scenario. Second, we define the adversary model and give the list of privacy requirements for this system model. We also analyze the impact on user privacy when some of the parties in our model share information that is not strictly needed for providing the service. Third, we introduce a privacy-aware access protocol for the system model and analyze this protocol against the privacy requirements.

An Accurate Model for Computation Offloading in 6G Networks and a HAPS-Based Case Study

The undeniable potential of computation offloading has been attracting attention from researchers for more than a decade. With advances in multi-access edge computing (MEC), computation offloading has become a more critical issue because of the heterogeneity in the computational power of edge devices and the elevated importance of extending their lifespan. Due to the apparent advantages, the use of MEC in 6G networks, where a vertical heterogeneous network composed of space, air, and ground networks is only natural. The non-terrestrial networking elements constitute effective computational resources. However, recent research investigating the potential of computational offloading in 6G networks has involved models that do not adequately reflect the complexity of the underlying processes. In this study, we propose a realistic computation model for 6G networks that considers crucial properties of the offloaded job, including the inter-dependency of the job tasks and the decomposability of the job. Our model is based on the mature application domain of MEC, where proven solutions are already studied. We also investigate the potential of a high altitude platform station (HAPS)-aided MEC platform using this model. The proposed model allows us to design offloading strategies to enable adaptive computational offloading. Through numerical analyses, we show that the proposed model provides sufficient insight to reduce the total processing time significantly.

Application-aware resource allocation and data management for MEC-assisted IoT service providers

To support the growing demand for data-intensive and low-latency IoT applications, Multi-Access Edge Computing (MEC) is emerging as an effective edge-computing approach enabling the execution of delay-sensitive processing tasks close to end-users. However, most of the existing works on resource allocation and service placement in MEC systems overlook the unique characteristics of new IoT use cases. For instance, many IoT applications require the periodic execution of computing tasks on real-time data streams that originate from devices dispersed over a wide area. Thus, users requesting IoT services are typically distant from the data producers. To fill this gap, the contribution of this work is two-fold. Firstly, we propose a MEC-compliant architectural solution to support the operation of multiple IoT service providers over a common MEC platform deployment, which enables the steering and shaping of IoT data transport within the platform. Secondly, we model the problem of service placement and data management in the proposed MEC-based solution taking into account the dependencies at the data level between IoT services and sensing resources. Our model also considers that caches can be deployed on MEC hosts, to allow the sharing of the same data between different IoT services with overlapping geographical scope, and provides support for IoT services with heterogeneous QoS requirements, such as different frequencies of periodic task execution. Due to the complexity of the optimisation problem, a heuristic algorithm is proposed using linear relaxation and rounding techniques. Extensive simulation results demonstrate the efficiency of the proposed approach, especially when traffic demands generated by the service requests are not uniform.

Virtual IoT Service Slice Functions for Multiaccess Edge Computing Platform

With the advancement of the Internet of Things (IoT), different use cases, such as smart factories, smart cities, and smart cars are being developed. Technologies, such as multiaccess edge computing (MEC) and network slicing are being researched to support various vertical use cases. However, simply utilizing network slicing and MEC is insufficient when providing a specific IoT service. Accordingly, there is a need for a method of arranging and operating common service functions (CSFs) used in the IoT platform at the edge of the network. In addition, it is necessary not only to deliver virtual CSFs but also to implement autoscaling policies of these virtual common service resources. This study extends the idea of resource slicing technologies for the IoT service layer. The IoT service layer exposes the virtual IoT CSFs as an IoT slice service to be provided in the form of virtual network functions (VNFs) with MEC applications on top of the network function virtualization infrastructure (NFVI) at the edge of the network. We propose a framework architecture for virtual IoT slice service orchestration, and illustrate how to instantiate the virtual IoT service functions in the existing centralized cloud to the MEC platform. We also propose an elastic computing algorithm of virtual IoT slice services functions(vIoT-SSFs) resources at the NFVI so that the IoT applications and underlying IoT resources can access vIoT-SSFs at the edge of the network.

Centralized and Federated Learning for Predictive VNF Autoscaling in Multi-Domain 5G Networks and Beyond

Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) are two technologies expected to play a vital role in 5G and beyond networks. However, adequate mechanisms are required to meet the dynamically changing network service demands to utilize the network resources optimally and also to satisfy the demanding QoS requirements. Particularly in multi-domain scenarios, the additional challenge of isolation and data privacy among domains needs to be tackled. To this end, centralized and distributed Artificial Intelligence (AI)-driven resource orchestration techniques (e.g., virtual network function (VNF) autoscaling) are foreseen as the main enabler. In this work, we propose deep learning models, both centralized and federated approaches, that can perform horizontal and vertical autoscaling in multi-domain networks. The problem of autoscaling is modelled as a time series forecasting problem that predicts the future number of VNF instances based on the expected traffic demand. We evaluate the performance of various deep learning models trained over a commercial network operator dataset and investigate the pros and cons of federated learning over centralized learning approaches. Furthermore, we introduce the AI-driven Kubernetes orchestration prototype that we implemented by leveraging our MEC platform and assess the performance of the proposed deep learning models in a practical setup.

Jointly optimized request dispatching and service placement for MEC in LEO network

Integrating Multi-access Edge Computing (MEC) in Low Earth Orbit (LEO) network is an important way to provide globally seamless low-delay service. In this paper, we consider the scenario that MEC platforms with computation and storage resource are deployed on LEO satellites, which is called LEO-MEC. Service request dispatching decision is very important for resource utilization of the whole LEO-MEC system and QoE of MEC users. Another important problem is service placement that is closely coupled with request dispatching. This paper models the joint service request dispatching and service placement problem as an optimization problem, which is a Mixed Integer Linear Programming (MILP). Our proposed mechanism solves this problem and uses the solved decision variables to dispatch requests and place services. Simulation results show that our proposed mechanism can achieve better performance in terms of ratio of served users and average hop count compared with baseline mechanism.

Network Service and Resource Orchestration: A Feature and Performance Analysis within the MEC-Enhanced Vehicular Network Context.

By providing storage and computational resources at the network edge, which enables hosting applications closer to the mobile users, Multi-Access Edge Computing (MEC) uses the mobile backhaul, and the network core more efficiently, thereby reducing the overall latency. Fostering the synergy between 5G and MEC brings ultra-reliable low-latency in data transmission, and paves the way towards numerous latency-sensitive automotive use cases, with the ultimate goal of enabling autonomous driving. Despite the benefits of significant latency reduction, bringing MEC platforms into 5G-based vehicular networks imposes severe challenges towards poorly scalable network management, as MEC platforms usually represent a highly heterogeneous environment. Therefore, there is a strong need to perform network management and orchestration in an automated way, which, being supported by Software Defined Networking (SDN) and Network Function Virtualization (NFV), will further decrease the latency. With recent advances in SDN, along with NFV, which aim to facilitate management automation for tackling delay issues in vehicular communications, we study the closed-loop life-cycle management of network services, and map such cycle to the Management and Orchestration (MANO) systems, such as ETSI NFV MANO. In this paper, we provide a comprehensive overview of existing MANO solutions, studying their most important features to enable network service and resource orchestration in MEC-enhanced vehicular networks. Finally, using a real testbed setup, we conduct and present an extensive performance analysis of Open Baton and Open Source MANO that are, due to their lightweight resource footprint, and compliance to ETSI standards, suitable solutions for resource and service management and orchestration within the network edge.

Orchestrating heterogeneous MEC-based applications for connected vehicles

In the near future, 5G-connected vehicles will be able to exchange messages with each other, with the roadside infrastructure, with back-end servers, and with the Internet. They will do so with reduced latency, increased reliability, and large throughput under high mobility and user density. Different services with different requirements, such as Advanced Driving Assistance (ADA) and High Definition (HD) Video Streaming, will share the same physical resources, such as the wireless channel. Thus, a rigid orchestration among them becomes necessary to prioritize network resource allocation. This study proposes a Connected Vehicle Service Orchestrator (CVSO) which optimizes the Quality of Experience (QoE) of an in-vehicle infotainment video delivery service, while taking into account the required bandwidth for coexisting high priority services, such as ADA. To this end, we provide an Integer Linear Programming (ILP) formulation for the problem of optimally assigning a video streaming bitrate/quality per user to maximize the overall QoE, considering information from the video service and the Radio Access Network (RAN) levels. Our system takes advantage of recent developments in the area of Multi-access Edge Computing (MEC). In particular, we have implemented the CVSO and other service-level components and have deployed them on top of a standards-compliant MEC platform that we have developed. We exploit MEC-native services such as the Radio Network Information Service (RNIS) to offer the CVSO the necessary level of RAN awareness. Experiments on a full LTE network testbed featuring our MEC platform demonstrate the performance improvements our system brings in terms of video QoE. Furthermore, we propose and evaluate different algorithms to solve the ILP, which exhibit different trade-offs between solution quality and execution time.

Multi-Access Edge Computing: A Comparative Analysis of 5G System Deployments and Service Consumption Locality Variants

Multi-access edge computing (MEC) is a key technology for 5G systems and offers cloud computing capabilities to application developers and content providers along with an information technology service environment at the edge of the network. In terms of system deployment, MEC infrastructure may, among other options, exist either at the edge of the radio access network (RAN), or at a central office (CO). Moreover, various MEC service consumption variants can be considered, depending on the degree of proximity between service consumers (i.e., MEC applications) and service producers (i.e., running on MEC platforms or exposed by service-producing MEC applications). Consequently, different system scenarios are expected to achieve different performance, e.g., in terms of the round-trip time (RTT) experienced at the user side. In this article, we present a first MEC performance case study to focus on different system deployments and service consumption localities for a number of workloads. It is observed that in all cases, the RTT is highly impacted by the MEC application workload; higher RTT is observed when a MEC application consumes remote MEC services; whereas low RTT gain is observed when moving from smart CO to RAN edge deployments, regardless of the considered workload.

Multiaccess Edge Computing-Based Simulation as a Service for 5G Mobile Applications: A Case Study of Tollgate Selection for Autonomous Vehicles

As the data rate and area capacity are enormously increased with the advent of 5G wireless communication, the network latency becomes a severe issue in a 5G network. Since there are various types of terminals in a 5G network such as vehicles, medical devices, robots, drones, and various sensors which perform complex tasks interacting with other devices dynamically, there is a need to handle heavy computing resource intensive operations. Placing a multiaccess edge computing (MEC) server at the base station, which is located at the edge, can be one of the solutions to it. The application running on the MEC platform needs a specific simulation technique to analyze complex systems inside the MEC network. We proposed and implemented a simulation as a service (SIMaaS) for the MEC platform, which is to offload the simulation using a Cloud infrastructure based on the concept of computation offloading. In the case study, the Monte-Carlo simulations are conducted using the proposed SIMaaS to select the optimal highway tollgate where vehicles are allowed to enter. It shows how clients of the MEC platform use SIMaaS to obtain certain goals.

A Fleet of MEC UAVs to Extend a 5G Network Slice for Video Monitoring with Low-Latency Constraints

In the last decade, video surveillance systems have become more and more popular. Thanks to a decrease in price of video camera devices and the diffusion of cheap small unmanned aerial vehicles (UAVs), video monitoring is today adopted in a wide range of application cases, from road traffic control to precision agriculture. This leads to capture a great amount of visual material to be monitored and screened for event detection. However, information that is gathered from a platform of video monitoring UAVs may produce high-volume data, whose processing is unfeasible to be done locally by the same UAVs that perform monitoring. Moreover, because of the limited bandwidth of wireless links connecting UAVs to computing infrastructures that are installed on ground, offloading these data to edge clouds renders these platforms infeasible for video analysis applications with low-latency requirements. The target of this paper is to extend a 5G network slice for video monitoring with a Flying Ad-hoc NETwork (FANET) constituted by UAVs with multi-access edge computing (MEC) facilities (MEC UAVs), flying very close to the layer of UAVs monitoring the area of interest. A policy for mutual help among MEC UAVS is defined in order to increase the performance of the whole aerial MEC platform, so further reducing end-to-end latency between sources and actuators, and increasing system reliability. A use case is considered for a numerical analysis of the proposed platform.

Reliable Resource Provisioning Using Bankers’ Deadlock Avoidance Algorithm in MEC for Industrial IoT

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.

On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration

Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges.