A machine learning approach to 5G infrastructure market optimization

It is now commonly agreed that future 5G Networks will build upon the network slicing concept. The ability to provide virtual, logically independent "slices" of the network will also have an impact on the models that will sustain the business ecosystem. Network slicing will open the door to new players: the infrastructure provider, which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the infrastructure provider to deliver a specific service to their customers. In this new context, how to correctly handle resource allocation among tenants and how to maximize the monetization of the infrastructure become fundamental problems that need to be solved. In this paper, we address this issue by designing a network slice admission control algorithm that (i) autonomously learns the best acceptance policy while (ii) it ensures that the service guarantees provided to tenants are always satisfied. The contributions of this paper include: (i) an analytical model for the admissibility region of a network slicing-capable 5G Network, (ii) the analysis of the system (modeled as a Semi-Markov Decision Process) and the optimization of the infrastructure providers revenue, and (iii) the design of a machine learning algorithm that can be deployed in practical settings and achieves close to optimal performance.The work of University Carlos III of Madrid was supported by the H2020 5G-MoNArch project (Grant Agreement No. 761445) and the 5GCity project of the Spanish Ministry of Economy and Competitiveness (TEC2016-76795-C6-3-R). The work of NEC Laboratories Europe was supported by the 5G-Transformer project (Grant Agreement No. 761536)

CARES: computation-aware scheduling in virtualized radio access networks

In a virtualized Radio Access Network (RAN), baseband processing is performed by software running in cloudcomputing platforms. However, current protocol stacks were not designed to run in this kind of environments: the high variability on the computational resources consumed by RAN functions may lead to eventual computational outages (where frames are not decoded on time), severely degrading the resulting performance. In this paper, we address this issue by re-designing two key functions of the protocol stack: (i) scheduling, to select the transmission of those frames that do not incur in computational outages, and (ii) modulation and coding scheme (MCS) selection, to downgrade the selected MCS in case no sufficient computational resources are available. We formulate the resulting problem as a joint optimization and compute the (asymptotically) optimal solution to this problem. We further show that this solution involves solving an NP-hard problem, and propose an algorithm to obtain an approximate solution that is computationally efficient while providing bounded performance over the optimal. We thoroughly evaluate the proposed approach via simulation, showing that it can provide savings as high as 80% of the computational resources while paying a small price in performance.The work of University Carlos III of Madrid was supported by the H2020 5G-MoNArch project (Grant Agreement No. 761445) and the work of NEC Europe Ltd. by the 5G-Transformer project (Grant Agreement No. 761536)

DeepCog: cognitive network management in sliced 5G Networks with deep learning

Proceeding of: 2019 IEEE International Conference on Computer Communications (IEEE INFOCOM 2019), Paris (France), 29 April - 2 May, 2019.Network slicing is a new paradigm for future 5G networks where the network infrastructure is divided into slices devoted to different services and customized to their needs. With this paradigm, it is essential to allocate to each slice the needed resources, which requires the ability to forecast their respective demands. To this end, we present DeepCog, a novel data analytics tool for the cognitive management of resources in 5G systems. DeepCog forecasts the capacity needed to accommodate future traffic demands within individual network slices while accounting for the operator’s desired balance between resource overprovisioning (i.e., allocating resources exceeding the demand) and service request violations (i.e., allocating less resources than required). To achieve its objective, DeepCog hinges on a deep learning architecture that is explicitly designed for capacity forecasting. Comparative evaluations with real-world measurement data prove that DeepCog’s tight integration of machine learning into resource orchestration allows for substantial (50% or above) reduction of operating expenses with respect to resource allocation solutions based on state-of-theart mobile traffic predictors. Moreover, we leverage DeepCog to carry out an extensive first analysis of the trade-off between capacity overdimensioning and unserviced demands in adaptive, sliced networks and in presence of real-world traffic.The work of University Carlos III of Madrid was supported by the H2020 5G-MoNArch project (Grant Agreement No. 761445) and the work of NEC Laboratories Europe by the 5GTransformer project (Grant Agreement No. 761536). The work of CNR-IEIIT was partially supported by the ANR CANCAN project (ANR-18-CE25-0011)

DeepCog: optimizing resource provisioning in network slicing with AI-based capacity forecasting

The dynamic management of network resources is both a critical and challenging task in upcoming multi-tenant mobile networks, which requires allocating capacity to individual network slices so as to accommodate future time-varying service demands. Such an anticipatory resource configuration process must be driven by suitable predictors that take into account the monetary cost associated to overprovisioning or underprovisioning of networking capacity, computational power, memory, or storage. Legacy models that aim at forecasting traffic demands fail to capture these key economic aspects of network operation. To close this gap, we present DeepCog, a deep neural network architecture inspired by advances in image processing and trained via a dedicated loss function. Unlike traditional traffic volume predictors, DeepCog returns a cost-aware capacity forecast, which can be directly used by operators to take short- and long-term reallocation decisions that maximize their revenues. Extensive performance evaluations with real-world measurement data collected in a metropolitan-scale operational mobile network demonstrate the effectiveness of our proposed solution, which can reduce resource management costs by over 50% in practical case studies.The work of University Carlos III of Madrid was supported by H2020 5G-TOURS project (grant agreement no. 856950). The work of NEC Laboratories Europe was supported by H2020 5G-TRANSFORMER project (grant agree-ment no. 761536) and 5GROWTH project (grant agreement no. 856709)

Network slicing via function decomposition and flexible network design

Proceeding of: IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PMRC 2017)We argue for flexible network design as an architecture prototype for next generation networks. Such flexible design is developed by capitalizing on the concept of network function decomposition in conjunction with with its relation to network slicing. A detailed view of the proposed functional architecture is put forward, where the role of network function blocks for forming network slices with given requirements is underlined. We further highlight the impact of common architecture over multiple tenants and elaborate on the emerging multi-tenancy business models along with the resulting implications on security.This work has been performed in the framework of the H2020-ICT-2014-2 project 5G NORMA

Optimising 5G infrastructure markets: the business of network slicing

Proceeding of: IEEE Conference on Computer Communications, INFOCOM 2017, Atlanta, Georgia, USA, 1-4 May 2017In addition to providing substantial performance enhancements, future 5G networks will also change the mobile network ecosystem. Building on the network slicing concept, 5G allows to "slice" the network infrastructure into separate logical networks that may be operated independently and targeted at specific services. This opens the market to new players: the infrastructure provider, which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the infrastructure provider to deliver a specific service to their customers. In this new context, we need new algorithms for the allocation of network resources that consider these new players. In this paper, we address this issue by designing an algorithm for the admission and allocation of network slices requests that (i) maximises the infrastructure provider's revenue and (ii) ensures that the service guarantees provided to tenants are satisfied. Our key contributions include: (i) an analytical model for the admissibility region of a network slicing-capable 5G Network, (ii) the analysis of the system (modelled as a Semi-Markov Decision Process) and the optimisation of the infrastructure provider's revenue, and (iii) the design of an adaptive algorithm (based on Q-learning) that achieves close to optimal performance.This research work has been performed in the framework of the H2020-ICT-2014-2 project 5G NORMA (Grant Agreement No. 671584). The work of A. Banchs was partially supported by the Spanish Ministry of Economy and Competitiveness under the THWART project (Grant TEC2015-70836-ERC)

Mobile traffic forecasting for maximizing 5G network slicing resource utilization

IEEE INFOCOM 2017 - IEEE Conference on Computer CommunicationsAbstract. The emerging network slicing paradigm for 5G provides new business opportunities by enabling multi-tenancy support. At the same time, new technical challenges are introduced, as novel resource allocation algorithms are required to accommodate different business models. In particular, infrastructure providers need to implement radically new admission control policies to decide on network slices requests depending on their Service Level Agreements (SLA). When implementing such admission control policies, infrastructure providers may apply forecasting techniques in order to adjust the allocated slice resources so as to optimize the network utilization while meeting network slices' SLAs. This paper focuses on the design of three key network slicing building blocks responsible for (i) traffic analysis and prediction per network slice, (ii) admission control decisions for network slice requests, and (iii) adaptive correction of the forecasted load based on measured deviations. Our results show very substantial potential gains in terms of system utilization as well as a trade-off between conservative forecasting configurations versus more aggressive ones (higher gains, SLA risk)This work has been partially funded by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No. 671584 5GNORMA

Toward the network of the future: from enabling technologies to 5G concepts

There is a wide consensus by the research community and the industry that it will not be possible to satisfy future mobile traffic demand and application requirements by simply evolving the current fourth-generation architecture. Instead, there is a need for a considerable revision of the mobile network system: such an effort is commonly referred to as the future fifth-generation (5G) architecture, and large-scale initiatives all around the globe have been launched worldwide to address this challenge. While these initiatives have not yet defined the future 5G architecture, the research community has already invested a very substantial effort on the definition of new individual technologies. The fact that all new proposals are tagged as 5G has created a lot of confusion on what 5G really is. The aim of this article is to shed some light on the current status of the 5G architecture definition and the trends on the required technologies. Our key contributions are the following: (1) we review the requirements for 5G identified by the different worldwide initiatives, highlighting similarities and differences; (2) we discuss current trends in technologies, showing that there is a wide consensus on the key enablers for 5G; and (3) we make an effort to understand the new concepts that need to be devised, building on the enablers, to satisfy the desired requirements.This work has been performed in the framework of the H2020-ICT-2014-2 project 5G NORMA. This work has also been performed in the framework of the H2020-ICT-2014 project 5GEx (Grant Agreement no. 671636), which is partially funded by the European Commission

On the benefits of bringing cloud-awareness to network virtual functions

Proceeding of: 2018 European Conference on Networks and Communications (EuCNC), June 18-21, Ljubljana, SloveniaWe are currently observing the softwarization of communication networks, where network functions are translated from monolithic pieces of equipment to programs running over a shared pool of computational, storage, and communication resources. As the amount of this resources might vary over time, in this paper we discuss the potential benefits of introducing resource awareness to softwarized network functions. More specifically, we focus on the case of computational elasticity, namely, the ability to endure shortages of computational resources while providing an adequate (although non-ideal) service. We discuss how to enable this ability by re-designing network functions, and illustrate the potential benefits of this approach with a numerical evaluation

Network slicing to enable scalability and flexibility in 5G mobile networks

We argue for network slicing as an efficient solution that addresses the diverse requirements of 5G mobile networks, thus provid-ing the necessary flexibility and scalability associated with future network implementations. We elaborate on the challenges that emerge when we design 5G networks based on network slicing. We focus on the architectural aspects associated with the coexistence of dedicated as well as shared slices in the network. In particular, we analyze the realization options of a flexible radio access network with focus on network slicing and their impact on the design of 5G mobile networks. In addition to the technical study, this paper provides an investigation of the revenue potential of network slicing, where the applications that originate from such concept and the profit capabilities from the network operator's perspective are put forward.This work has been performed in the framework of the H2020-ICT-2014-2 project 5G NORMA