Service migration versus service replication in Multi-access Edge Computing

Envisioned low-latency services in 5G, like automated driving, will rely mainly on Multi-access Edge Computing (MEC) to reduce the distance, and hence latency, between users and the remote applications. MEC hosts will be deployed close to mobile base stations, constituting a highly distributed computing platform. However, user mobility may raise the need to migrate a MEC application among MEC hosts to ensure always connecting users to the optimal server, in terms of geographical proximity, Quality of Service (QoS), etc. However, service migration may introduce: (i) latency for users due to the downtime duration; (ii) cost for the network operator as it consumes bandwidth to migrate services. One solution could be the use of service replication, which pro-actively replicates the service to avoid service migration and ensure low latency access. Service replication induces cost in terms of storage, though, requiring a careful study on the number of service to replicate and distribute in MEC. In this paper, we propose to compare service migration and service replication via an analytical model. The proposed model captures the relation between user mobility and service duration on service replication as well as service migration costs. The obtained results allow to propose recommendations between using service migration or service replication according to user mobility and the number of replicates to use for two types of service.This work was partially funded by the European Union’s Horizon 2020 research and innovation program under the 5GTransformer project (grant no. 761536

Exposing radio network information in a MEC-in-NFV environment: the RNISaaS concept

IEEE Conference on Network Softwarization (2019)The Radio Network Information Service (RNIS) is one of the key services provided by a Multi-access Edge Computing Platform (MEP), as specified in the relevant ETSI MEC standards. It is responsible for interacting with the Radio Access Network (RAN), collecting RAN-level information about User Equipment (UE) and exposing it to mobile edge applications, which can in turn utilize it to dynamically adjust their behavior to optimally match the RAN conditions. Putting the provision of RNIS in the context of the emerging MEC-in-NFV environment, where the components and services of the MEC architecture, including the MEP itself, are integrated in an NFV environment and are delivered on top of a virtualized infrastructure, we present our standards-compliant RNIS implementation based on OpenAirInterface and study critical performance aspects for its provision as a virtual function. Since the RNIS design and operation follows the publish-subscribe model, we provide alternative implementations using different message brokering technologies (RabbitMQ and Apache Kafka), and compare their use and performance in an effort to evaluate their suitability for providing RNIS in an as-a-service manner.This work has been partially funded by the EC H2020 5G-Transformer Project (grant no. 761536)

Latency and Availability Driven VNF Placement in a MEC-NFV Environment

Multi-access Edge Computing (MEC) is gaining momentum as it is considered as one of the enablers of 5G ultra-Reliable Low-Latency Communications (uRLLC) services. MEC deploys computation resources close to the end user, enabling to reduce drastically the end-to-end latency. ETSI has recently leveraged the MEC architecture to run all MEC entities, including MEC applications, as Virtual Network Functions (VNF) in a Network Functions Virtualization (NFV) environment. This evolution allows taking advantage of the mature architecture and the enabling tools of NFV, including the potential to apply a variety of service-tailored function placement algorithms. However, the latter need to be carefully designed in case of MEC applications such as uRLLC, where service access latency is critical. In this paper, we propose a novel placement scheme applicable to a MEC in NFV environment. In particular, we propose a formulation of the problem of VNF placement tailored to uRLLC as an optimization problem of two conflicting objectives, namely minimizing access latency and maximizing service availability. To deal with the complexity of the problem, we propose a Genetic Algorithm to solve it, which we compare with a CPLEX implementation of our model. Our numerical results show that our heuristic algorithm runs efficiently and produces solutions that approximate well the optimal, reducing latency and providing a highly-available service.This work has been partially supported by the European Union’s H2020 5G-Transformer Project (grant no. 761536

Dynamic slicing of RAN resources for heterogeneous coexisting 5G services

This paper has been presented at: IEEE Global Communications Conference, GLOBECOM 2019Network slicing is one of the key components allow-ing to support the envisioned 5G services, which are organized in three different classes: Enhanced Mobile Broadband (eMBB), massive Machine Type Communication (mMTC), and Ultra-Reliable and Low-Latency Communication (URLLC). Network Slicing relies on the concept of Network Softwarization (Software Defined Networking - SDN and Network Functions Virtualization - NFV) to share a common infrastructure and build virtual instances (slices) of the network tailored to the needs of dif-ferent 5G services. Although it is straightforward to slice and isolate computing and network resources for Core Network (CN) elements, isolating and slicing Radio Access Network (RAN) resources is still challenging. In this paper, we leverage a two-level MAC scheduling architecture and provide a resource sharing algorithm to compute and dynamically adjust the necessary radio resources to be used by each deployed network slice, covering eMBB and URLLC slices. Simulation results clearly indicate the ability of our solution to slice the RAN resources and satisfy the heterogeneous requirements of both types of network slices.This work was partially supported by the European Union’s Horizon 2020 Research and Innovation Program under the 5G!Drones (Grant No. 857031) and 5G-TRANSFORMER (Grant No. 761536) projects

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions

Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks

Cost and availability aware resource allocation and virtual function placement for CDNaaS provision

We address the fundamental tradeoff between deployment cost and service availability in the context of on-demand content delivery service provision over a telecom operator's network functions virtualization infrastructure. In particular, given a specific set of preferences and constraints with respect to deployment cost, availability and computing resource capacity, we provide polynomial-time heuristics for the problem of jointly deriving an appropriate assignment of computing resources to a set of virtual instances and the placement of the latter in a subset of the available physical hosts. We capture the conflicting criteria of service availability and deployment cost by proposing a multi-objective optimization problem formulation. Our algorithms are experimentally shown to outperform state-of-the-art solutions in terms of both execution time and optimality, while providing the system operator with the necessary flexibility to balance between conflicting objectives and reflect the relevant preferences of the customer in the produced solutions.This work was supported in part by the French FUI-18 DVD2C project and by the European Union’s Horizon 2020 research and innovation program under the 5G-Transformer project (grant no. 761536)

A Blockchain-Based Network Slice Broker for 5G Services

With advent of 5G, the classical mobile network business model is shifting from a network-operator-oriented business to a more open system with several actors. In this context, the Network Slice provider will play the role of an intermediate entity between the vertical service provider and the resource provider. To deploy a network slice, the network slice provider will require a brokering mechanism, which allows it to lease resources from different providers in a secure and private way. In this paper we propose a broker design based on Blockchain technology, providing a mechanism that secures and ensures anonymous transactions.This work was partially funded by the European Union’s Horizon 2020 research and innovation program under the 5G-Transformer project (grant no. 761536). Dr. Ksentini is corresponding author