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In this paper we propose novel and more realistic analytical models for the determination of the peak demand under four power demand control scenarios. Each scenario considers a finite number of appliances installed in a residential area, with diverse power demands and different arrival rates of power requests. We develop recursive formulas for the efficient calculation of the peak demand under each scenario, which take into account the finite population of the appliances. Moreover, we associate each scenario with a proper real-time pricing process in order to derive the social welfare. The proposed analysis is validated through simulations. Moreover, the performance evaluation of the proposed formulas reveals that the absence of the assumption of finite number of appliances could lead to serious peak-demand over-estimations. ; Grant numbers : This work has been funded by the project of the Catalan Government 2014-SGR-1551.

The fifth generation wireless networks are expected to offer high capacity and accommodate numerous over-the-top applications, relying on users' Internet connectivity, thus involving different stakeholders, that is, network service providers and over-the-top service providers. For the efficient management of over-the-top application flows, the implementation of service functions and their interconnection in service chains, namely network service chaining, should consider the over-the-top service providers' performance goals and user management strategies. However, in current wireless network deployments, the network service providers have full control of network service chaining. Considering that user satisfaction from the offered services is a common interest for both types of stakeholders, the over-the-top service providers need to participate in network service chaining, and apply QoS and user prioritization policies to network service chain resource management, which involves users connected at different network points, in a distributed manner. In this article, we describe 5G network management architectures and propose virtualization components that enable over-the-top service-provider- oriented network service. We also outline the arising issues for over-the-top service providers in network service chaining and introduce a distributed prioritization network service chain management scheme for over-the-top application flows, based on matching theory. The evaluation results indicate the performance gains in over-the-top service providers' service levels that stem from the proposed scheme, demonstrating the benefits of introducing prioritization in network service chain deployment. ; Grant numbers : This work has been funded by the Catalan Government (2014-SGR-1551), CellFive (TEC2014-60130-P).© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or ...

The rise of third-party content providers and the introduction of numerous applications has been driving the growth of mobile data traffic in the past few years. The applications' various Quality of Service (QoS) requirements as well as the use of multiple devices per user have increased the traffic heterogeneity, pressing the telecommunications industry to the deployment of dense Heterogeneous Networks (HetNets). At the same time, the content providers' rise has also led to the decrease of the Mobile Network Operators' (MNOs) revenues. Under these circumstances, the MNOs need to guarantee the users' Quality of Experience (QoE) requirements, while ensuring the sustainability of HetNet investments. To this end, we consider a HetNet deployment where MNOs offer a multitude of services with diverse pricing. We propose a heuristic, joint QoE-aware resource allocation and dynamic pricing algorithm with overall user satisfaction constraints to maximize the MNO profit, while providing high QoE. Simulation results show that the proposed algorithm can handle traffic heterogeneity by achieving substantial profit and QoE gains, compared to a state of the art algorithm. Moreover, we demonstrate the benefits of our dynamic pricing scheme and its applicability on other resource allocation algorithms. ; Grant numbers : This work has been funded by the Project CellFive (TEC2014- 60130- P), anb by the Catalan Government (2014-SGR-1551).© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

The rise of Over The Top (OTT) content providers and the introduction of numerous applications has been driving the growth of mobile data traffic in the past few years. The applications' various Quality of Service (QoS) requirements as well as the use of multiple devices per user have increased the traffic heterogeneity, pressing the telecommunications industry to the deployment of 5G networks in 2020. At the same time, the rise of OTT providers has also led to the decrease of the Mobile Network Operators' (MNOs) revenues. Under these circumstances, the MNOs need to guarantee the users' Quality of Experience (QoE) requirements, while ensuring the sustainability of a 5G investment. To this end, we consider a 5G Heterogeneous Network (HetNet) deployment where MNOs use a QoE-based charging scheme. We propose a heuristic, QoE-aware user association algorithm to maximize the MNO profit, while providing high QoE. Simulation results show that the proposed algorithm can handle traffic heterogeneity by achieving substantial profit and QoE gains, compared to a baseline SINR-based scheme. ; Grant numbers : This work has been funded by the Project CellFive (TEC2014- 60130-P), and the Catalan Government (2014-SGR-1551).© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Wireless network virtualization enables efficient network sharing in Long Term Evolution Advanced (LTE-A) networks. For the accommodation of ever-increasing user demands, Mobile Virtual Network Operators (MVNOs) can lease and operate virtual resources in network infrastructure that belongs to Mobile Network Operators (MNOs). The coexistence of multiple MVNOs that serve users with different Quality of Service (QoS) requirements and spatial distribution in an LTE-A cell further complicates the arising resource allocation problem. Moreover, the MVNOs aim to satisfy the QoS demands for the maximum possible number of users with the minimum possible cost. This multifaceted context renders centralized optimization approaches for virtual resource allocation in shared LTE-A networks unsuitable. As an alternative, the framework of matching theory, thanks to its distributed nature, can achieve a proper allocation through a stable matching between resources and MVNOs. In this context, we introduce a matching-theoretic formulation for the virtual resource allocation problem and propose a distributed algorithm that properly matches the MVNOs with the available resources. Our simulation results show that the proposed algorithm reaches a stable matching that satisfies the QoS demands for more users in comparison with other approaches. ; Grant numbers : This work has been funded by the Research Project CellFive (TEC2014-60130-P), AGAUR the Catalan Government (2014-SGR-1551).© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

We study the performance of Machine-to-Machine (M2M) communications in Long Term Evolution Advanced (LTE-A) networks. The proposed analysis is applied to the random access procedure within LTE-A for the calculation of the average overall delay, by considering multiple classes of M2M devices with diverse Quality-of-Service (QoS) requirements. The QoS differentiation is expressed through different Access Class Barring (ACB) parameters and backoff times for the various QoS classes. For the packet arrival process of M2M devices we consider an ON-OFF arrival process, which is a realistic approach, especially for M2M communications in Smart Grid environments. Furthermore, the proposed analysis is suitable for smart grid communication networks, where multiple M2M gateways are installed in different points of the network. The comparison of analytical results with simulations reveals the high accuracy of the proposed analysis. ; Grant numbers : This work has been partially funded by the project AGAUR by the Catalan Government 2014-SGR-1551 and by the project CellFive (TEC2014-60130-P).© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.