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Major interest is currently given to the integration of clusters of virtualization servers, also referred to as 'cloudlets', into the access network to allow higher performance and reliability in the access to mobile cloud services. We tackle the cloudlet network design problem for mobile access networks. The model is such that virtual machines are associated with mobile users and are allocated to cloudlets. Designing a cloudlet network implies first determining where to install cloudlet facilities among the available sites, then assigning sets of access points such as basestations to cloudlets, while supporting virtual machine migrations and taking into account partial user mobility information, as well as the satisfaction of service-level agreements. We present linkpath formulations supported by heuristics to compute solutions in reasonable time. We qualify the advantage in considering mobility for both users and virtual machines as up to 40% less cloudlet facilities to install and 40% less virtual machine migrations to execute. We compare two migration modes, bulk and live migration, as a function of mobile cloud service requirements, determining that a high preference should be given to bulk migrations for delay-stringent services such as augmented reality support, while for applications with less stringent delay requirements, live migration appears as largely preferable.

Orchestrating network and computing resources in Mobile Edge Computing (MEC) is an important item in the networking research agenda. In this paper, we propose a novel algorithmic approach to solve the problem of dynamically assigning base stations to MEC facilities, while taking into consideration multiple time-periods, and computing load switching and access latency costs. In particular, leveraging on an existing state of the art on mobile data analytics, we propose a methodology to integrate arbitrary time-period aggregation methods into a network optimization framework. We notably apply simple consecutive time period aggregation and agglomerative hierarchical clustering. Even if the aggregation and optimization methods represent techniques which are different in nature, and whose aim is partially overlapping, we show that they can be integrated in an efficient way. By simulation on real mobile cellular datasets, we show that, thanks to the clustering, we can scale with the number of time-periods considered, that our approach largely outperforms the case without time-period aggregations in terms of MEC access latency, and at which extent the use of clustering and time aggregation affects computing time and solution quality.

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The 5G mobile network will rely on network slicing to provide a wide variety of services with various quality of service (QoS) requirements. Network slicing is promoted by 3GPP and provides a logical vertical partition of the network that is based on network virtualization technologies, namely, network function virtualization (NFV), software-defined networking (SDN) and ETSI multi-access edge computing (MEC). Despite the undisputed benefits in terms of flexibility and scalability that are pledged by the paradigm, network slicing requires intelligent resource scheduling and allocation algorithms to efficiently use the network resources, especially at the edge of the network, due to their scarcity. In this paper, we propose an optimization algorithm for steering data traffic of multiple slices in the edge backhaul network, which aims at maximizing the QoS. We extensively analyze the realizable grade of QoS by testing various levels of MEC resources, demonstrate the beneficial impact of the approach for mobile operators, and highlight the performance advantage that is realized versus a single-slice approach of undifferentiated traffic.INDEX TERMS Multi-access edge computing, network slices, mathematical optimization.