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

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

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

Mobile Edge Computing Potential in Making Cities Smarter

This paper proposes an approach to enhance users’ expe-rience of video streaming in the context of smart cities. The proposed approach relies on the concept of mobile edge computing (MEC) as a key factor in enhancing the Quality of Service (QoS). It sustains QoS by ensuring that applications/services follow the mobility of users, realizing the “Follow-me-Edge” concept. The proposed scheme en-forces an autonomic creation of MEC services to allow any-where-anytime data access with optimum Quality of Experience (QoE) and reduced latency. Considering its application in smart city scenar-ios, the proposed scheme represents an important solution for reduc-ing core network traffic and ensuring ultra-short latency, and that is through a smart MEC architecture capable of achieving 1 ms latency dream for the upcoming 5G mobile system

A MEC-based Extended Virtual Sensing for Automotive Services

Multi-access edge computing (MEC) comes with the promise of enabling low-latency applications and of reducing core network load by offloading traffic to edge service instances. Recent standardization efforts, among which the ETSI MEC, have brought about detailed architectures for the MEC. Leveraging the ETSI model, in this paper we first present a flexible, yet full-fledged, MEC architecture that is compliant with the standard specifications. We then use such architecture, along with the popular OpenAir Interface (OAI), for the support of automotive services with very tight latency requirements. We focus in particular on the Extended Virtual Sensing (EVS) services, which aim at enhancing the sensor measurements aboard vehicles with the data collected by the network infrastructure, and exploit this information to achieve better safety and improved passengers/driver comfort. For the sake of concreteness, we select the intersection control as an EVS service and present its design and implementation within the MEC platform. Experimental measurements obtained through our testbed show the excellent performance of the MEC EVS service against its equivalent cloud-based implementation, proving the need for MEC to support critical automotive services, as well as the benefits of the solution we designed.This work was supported by the European Commission through the H2020 5G-TRANSFORMER project (Project ID 761536). The work of Christian Vitale was also supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for Euro-pean Programmes, Coordination, and Development