Time-Sensitive Networking (TSN) is a toolbox of technologies that enable deterministic communication over Ethernet. A key area has been TSN’s time-aware traffic shaping (TAS), which supports stringent end-to-end latency and reliability requirements. Configuration of TAS requires the computation of a network-wide traffic schedule, which is particularly challenging with integrated wireless networks (e.g., 5G, Wi-Fi) due to the stochastic nature of wireless links. This paper introduces a novel method for configuring TAS, focusing on cyclic traffic patterns and jitter of wireless links. We formulate a linear program that computes a network-wide time-aware schedule, robust to wireless performance uncertainties. The given method enables robust scheduling of multiple TSN frames per transmission window using a tunable robustness parameter (Γ). To reduce computational complexity, we also propose a sequential batch-scheduling heuristic that runs in polynomial time. Our approach is evaluated by using different network topologies and wireless link characteristics, demonstrating that the heuristic can schedule 90% of 6500 requested TSN streams in a large topology.

Small cells (SCs) mounted on top of Unmanned Aerial Vehicles (UAVs) can be used to boost the radio capacity in hotspot zones. However, UAV-SCs are subject to tight battery constraints, resulting in frequent recharges operated at the ground sites. To meet the UAV-SCs energy demanded to the ground sites, the operator leverages a set of Solar Panels (SPs) and grid connection. In this work, we demonstrate that both i) the level of throughput provided to a set of areas and ii) the amount of energy that is exchanged with the grid by the ground sites play a critical role in such UAV-aided cellular network. We then formulate the J-MATE model to jointly optimize the energy and throughput through revenue and cost components. In addition, we design the BBSR algorithm, which is able to retrieve a solution even for large problem instances. We evaluate J-MATE and BBSR over a realistic scenario composed of dozens of areas and multiple ground sites, showing that: i) both J-MATE and BBSR outperform previous approaches targeting either the throughput maximization or the energy minimization, and ii) the computation time and the memory occupation of BBSR are reduced up to five orders of magnitude compared to J-MATE.

This paper addresses the problem of communication in resource-limited broadcast/receive wireless networks. In large scale and resource-limited wireless networks, such as the Internet of Things (IoT), a massive amount of data is becoming increasingly available. Therefore, implementing protocols achieving error-free communication channels presents an important challenge. Indeed, in this new kind of network, the prevention of message conflicts and message collisions is a crucial issue. In terms of graph theory, solving this issue amounts to solve the distance-2 coloring problem on the network. This paper presents a first study on dynamic management in distance-2 coloring in resource-limited wireless networks. We propose a distributed distance-2 coloring in a dynamic network where (one) new node can join the network. Our protocol assigns to the new node a correct color without re-running the whole algorithm of time slot assigning. Our protocol is time-efficient and uses only local information with a high probability.

5G is the fifth generation technology standard for cellular networks, which is already being deployed and offering many advantages for communications, such as the high degree of flexibility, the possibility of management and control of systems and resources, thus allowing to host and execute services in one or more distinct network slices, etc. Non-orthogonal multiple access (NOMA) is being considered as a key enabling technique for 5G cellular systems. The earlier generations are becoming less practical with the emergent requirements such as very high spectral efficiency, very low latency, supporting diverse quality of services (QoS). Researchers are working on different techniques to fulfill these requirements, and NOMA is one of the essentials due to its capacity to increase spectrum efficiency. NOMA schemes exploit channel gain differences to serve multiple users concurrently, by superimposing multiple users' information signals at the transmitter side. In this paper, we review, analyze and classify recent papers on resource allocation problem in 5G networks, and then we discuss the proposed solutions. In particular, the papers are classified into two main categories: power/energy-efficient and rate-optimal. For each paper, the objective, optimization method and main results are discussed. Finally, some open challenges are highlighted.

Network Slicing is envisaged as a key component to address the challenges arising in next generation networks concerning the deployment, control and management of services. Besides, it promotes concurrent operation of multiple logical networks with diverging requirements on a common substrate platform. In this regard, the problem of designing individual logical network slices and mapping them onto the underlying substrate network gains significance. We denote this problem as the Network Slice Design Problem. In this work, we first consider the general network slice design problem. Adopting the robust optimisation approach of Bertsimas and Sim [1], [2], we then develop two additional formulations: i) to handle traffic demand uncertainties, and ii) to account for the correlations among the uncertain traffic demands. Finally, we present an extensive evaluation of the proposed formulations using realistic network instances.

This paper proposes a network architecture and supporting optimization framework that allows Unmanned Aerial Vehicles (UAVs) to perform city-scale video monitoring of a set of Points of Interest (PoI). Our approach is systems-driven, relying on experimental studies to identify the permissible number of hops for multi-UAV video relaying in a noisy 3-D environment. Our architecture itself is innovative in the sense that it defines a mathematical framework for selecting the UAVs for periodic re-charging by landing on public transportation buses, and then `riding' the bus to the successive chosen Pol. Specifically, we show that our UAV scheduler can be modeled as an instance of multicommodity flow problems, and mathematically solved through Mixed Integer Linear Programming (MILP) techniques. Thus, our centralized formulation identifies the UAV, the next bus, and the next PoI, given the information about energy thresholds, the bus routes in the city and their next arrival times, to ensure persistent and reliable video coverage of all PoIs in the city. Finally, our work is validated via emulation of a city environment with live traffic updates from a real bus transportation network.

We target the problem of managing the power states of the servers in a Cloud Data Center (CDC) to jointly minimize the electricity consumption and the maintenance costs derived from the variation of power (and consequently of temperature) on the servers' CPU. More in detail, we consider a set of virtual machines (VMs) and their requirements in terms of CPU and memory across a set of Time Slot (TSs). We then model the consumed electricity by taking into account the VMs processing costs on the servers, the costs for transferring data between the VMs, and the costs for migrating the VMs across the servers. In addition, we employ a material-based fatigue model to compute the maintenance costs needed to repair the CPU, as a consequence of the variation over time of the server power states. After detailing the problem formulation, we design an original algorithm, called Maintenance and Electricity Costs Data Center (MECDC), to solve it. Our results, obtained over several scenarios from a real CDC, show that MECDC largely outperforms two reference algorithms, which instead either target the load balancing or the energy consumption of the servers.

We consider the problem of clustering N users into K groups such that users in the same group are assigned a common service profile over M commodities. The profile of each group k sets for each commodity m the maximum of the service quality that users in the k-th group are willing to pay. The objective is to find the clustering that maximizes the total service user quality, which corresponds to the revenue of the service provider. This Service Profile Optimization Problem (SPOP) emerges in various applications, as for example the bit-loading in Hybrid Fiber Coax data distribution systems. We propose a Mixed Integer Linear Programming (MILP) model for the problem, that allows to use state-of-the-art MILP solvers as the core tool in an original powerful heuristic. We show complexity and performance gains with respect to previously proposed methods and a direct application of a state of the art MILP solver.

Abstract In Wireless mesh networks mesh access points (MAPs) forward traffic wirelessly towards users or Internet gateways. A user device usually connects to the MAP with the strongest signal, as such MAP should guarantee the best quality of service. However, this connection policy may lead to: (i) unfairness towards users that are distant from gateways; (ii) uneven distribution of users to MAPs; and (iii) inefficient use of network paths. We present a new model and solution approach to the problem of assigning users to MAPs and routing the data within the mesh network with the objective of providing max–min fair throughput. The problem is formulated as a mixed‐integer linear programming problem (MILP). Because of the inherent complexity of the problem, real size instances cannot be solved to optimality within the time limits for online optimization. Therefore, we propose an original heuristic solution algorithm for the resulting MILP. Both numerical comparisons and network simulations demonstrate the effectiveness of the proposed heuristic. For random networks, the heuristic achieves 98% of the optimal solution. Network simulations show that in medium‐sized networks, the number of users with at least 1Mbit/s minimum end‐to‐end rate increases by 550% when compared with the classical signal‐strength based association. Copyright © 2013 John Wiley & Sons, Ltd.