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Multi-access Edge Computing (MEC) allows users to run applications on demand near their mobile access points. MEC applications will exploit 5G infrastructure, and they will have to be designed by taking into account the characteristics of 5G mobile networks. This work describes how to use a system-level simulator of 5G networks – namely Simu5G, which evolves the popular 4G network simulator SimuLTE – as a real-time 5G net-work emulator. This allows designers of networked applications – and MEC ones in particular – to use it as a testbed during the deployment. We describe the system setup of Simu5G as an emulator, and its emulation capabilities and scale. Moreover, we present a case study of a MEC testbed using Intel’s Open Network Edge Services Software (OpenNESS) toolkit, based on a recent demon-stration in 5GAA (5G Automotive Association).

Evaluating edge deployments from a user perspective requires modeling, in a unified framework, the communication part, i.e., the cellular access network, and the computation part, i.e., the Mobile-edge server. This paper presents a framework that enables this, by joining the SimuLTE 4G network simulator and the Intel CoFluent edge-computing simulator. We show how the two tools are integrated and discuss some preliminary validation of the framework.

Multi-access edge computing (MEC) brings data and computational resources near mobile users, with the ultimate goal of reducing latency, improving resource utilization, and leveraging context and radio awareness. Relocation policies for applications in the MEC environment are necessary to guarantee its effectiveness and performance, and can use a multitude of different data (user position and direction, availability of MEC services and computation resources, etc.). In this article, we advocate using deep reinforcement learning to relocate applications in MEC scenarios, by having MEC learn during the evolution of the system. We show the feasibility of this approach and highlight its benefits via simulation, also presenting an environment that can foster future research on this topic.

Multi-access Edge Computing (MEC) is a novel paradigm to enrich current 4G and future 5G cellular networks by placing cloud-computing-based capabilities at the edge of the network. This will allow operators and service providers to endow the cellular network with enriched services. In this paper we describe the modeling and development of a MEC extension for the SimuLTE framework.

5G technology promises to improve the network performance by allowing users to seamlessly access distributed services in a powerful way. In this perspective, Multi-access Edge Computing (MEC) is a relevant paradigm that push data and computational resources nearby users with the final goal to reduce latencies and improve resource utilization. Such a scenario requires strong policies in order to react to the dynamics of the environment also taking into account multiple parameter settings. In this paper, we propose a deep reinforcement learning approach that is able to manage data migration in MEC scenarios by learning during the system evolution. We set up a simulation environment based on the OMNeT++/SimuLTE simulator integrated with the Keras machine learning framework. Preliminary results showing the feasibility of the proposed approach are discussed.

In Long Term Evolution-Advanced (LTE-A), network-controlled device-to-device (D2D) communications allow User Equipments (UEs) to communicate directly, without involving the Evolved Node-B in data relaying, while the latter still retains control of resource allocation. The above paradigm allows reduced latencies for the UEs and increased resource efficiency for the network operator, and is therefore foreseen to support several services, from Machine-to-machine to vehicular communications. D2D communications introduce research challenges that might affect the performance of applications and upper-layer protocols, hence simulations represent a valuable tool for evaluating these aspects. However, simulating D2D features might pose additional computational burden to the simulation environment. To this aim, a careful modeling is required to reduce computational overhead. In this paper, we describe our modeling of network-controlled D2D communications in SimuLTE, a system-level LTE-A simulation library based on OMNeT++. We describe the core modeling choices of SimuLTE, and show how these allow an easy extension to D2D communications. Moreover, we describe in detail the modeling of specific problems arising with D2D communications, such as scheduling with frequency reuse, connection mode switching and broadcast transmission. We document the computational efficiency of our modeling choices, showing that simulation of D2D communications is not more complex than simulation of classical cellular communications of comparable scale. Results show that the heaviest computational burden of D2D communication lies in estimating the Sidelink channel quality. We show that SimuLTE allows one to evaluate the interplay between D2D communication and end-to-end performance of UDP- and TCP-based services. Moreover, we assess the accuracy of using a binary interference model for frequency reuse, and we evaluate the trade-off between speed of execution and accuracy in modeling the reception probability.

International audience; The next generation of mobile systems must provide a balancing strategy towards M2M (Machine-to-Machine) traffic, while maintaining a sustainable Quality of Service (QoS) for H2H (Human-to-Human) traffic, especially with the expected exponential growth of the number of M2M devices in the coming years carried by the advance of the IoT (Internet of Things) technology. In normal situations, it is obvious that using a 1.4 MHz bandwidth in Long Term Evolution for Machines (LTE-M), helps in improving M2M device complexity, cost and battery life effectively. However, in emergency scenarios, an expected M2M storm will lead inevitably to a fast resource depletion accompanied by an eNodeB congestion in a split second. In this manuscript, we propose a novel scheme "LTE-M Adaptive eNodeB", which gradually solves the eNodeB overload problem, while keeping the H2H traffic QoS not to be affected badly. Moreover, We adaptively manage network resources to allow both traffic to efficiently access the LTE network via SimuLTE open-source modeler. Eventually, an evaluation of the mutual impact of M2M and H2H coexistence is also presented.

International audience; The next generation of mobile systems based on LTE-A (Long Term Evolution-Advanced) networks are expected to support the new promising technology M2M (Machine-to-Machine) communications while keeping an eye on its previous H2H (Human-to-Human) communications not to be affected especially when it comes to the expected exponential growth of the number of M2M in the coming years, in particular, with the advance of IoT (Internet of Things) deployment and the expected ubiquity of such objects in the near future. In this article, we review the M2M communication technology from the LTE-A perspective and we outline the random access challenges in high dense areas where the LTE-A network is striving to fulfill the massive number of M2M devices. Moreover, we compare the most common mechanisms found in the literature that deal with the RACH (Random Access Channel) procedure issues and challenges by analyzing the existing solutions and approaches to avoid RACH overload congestion in the M2M communications. To this end, we have developed different M2M scenarios using SimuLTE Modeler to investigate the impact of M2M communications on LTE-A networks in emergency events.