Enrica Zola

Performance evaluation of a TOA-based trilateration method to locate terminals in WLAN

Nowadays, several systems are available for outdoor localization, such as GPS, assisted GPS and other systems working on cellular networks. However, there is no proper location system for indoor scenarios. Research into designing location systems for 802.11 networks is being carried out, so locating mobile devices on global networks (GSM/cellular + GPS + WLAN) finally seems feasible. The technique presented in this paper uses existing wireless LAN infrastructure with minor changes to provide an accurate estimation of the location of mobile devices in indoor environments. This technique is based on round-trip time (RTT) measurements, which are used to estimate distances between the device to be located and WLAN access points. Each RTT measurement estimates the time elapsed between the RTS (Request-to-Send) and the CTS (Clear-to-Send) frame of the 802.11 standard. By applying trilateration algorithms, an accurate estimation of the mobile position is calculated.

Impact of mobility models on the cell residence time in WLAN networks

Several mobility models are available for simulating WLAN, with different impacts on network performance. This work deals with the impact of the assumed mobility model on two key teletraffic variables involved in the planning of the network: the cell residence time (i.e., time connected to an access point) and the handoff rate. These two variables are studied in different scenarios for WLANs designed for pedestrians. For this purpose, discrete event simulations are run with different mobility patterns and number of access points. The time between changes of access point (i.e., handoffs) is studied as a random variable. This research proves the importance of correctly selecting the assumed mobility pattern, as it has a strong impact on the number of handoffs. The probability density function of the cell residence time is also studied as a combination of a distribution that models fast disassociation events (i.e., short ping-pongs between two access points) and a gamma or lognormal distribution, depending on the mobility pattern, which model longer dwells.

On the energy cost of robustness for green virtual network function placement in 5G virtualized infrastructures

Abstract Next generation 5G networks will rely on virtualized Data Centers (vDC) to host virtualized network functions on commodity servers. Such Network Function Virtualization (NFV) will lead to significant savings in terms of infrastructure cost and reduced management complexity. However, green strategies for networking and computing inside data centers, such as server consolidation or energy aware routing, should not negatively impact the quality and service level agreements expected from network operators. In this paper, we study how robust strategies that place virtual network functions (VNF) inside vDC impact the energy savings and the protection level against resource demand uncertainty. We propose novel optimization models that allow the minimization of the energy of the computing and network infrastructure which is hosting a set of service chains that implement the VNFs. The model explicitly provides for robustness to unknown or imprecisely formulated resource demand variations, powers down unused routers, switch ports and servers, and calculates the energy optimal VNF placement and network embedding also considering latency constraints on the service chains. We propose both exact and heuristic methods. Our experiments were carried out using the virtualized Evolved Packet Core (vEPC), which allows us to quantitatively assess the trade-off between energy cost, robustness and the protection level of the solutions against demand uncertainty. Our heuristic is able to converge to a good solution in a very short time, in comparison to the exact solver, which is not able to output better results in a longer run as demonstrated by our numerical evaluation. We also study the degree of robustness of a solution for a given protection level and the cost of additional energy needed because of the usage of more computing and network elements.

Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty

Reducing the energy consumption of virtualized datacenters and the Cloud is very important in order to lower CO

Energy efficient line-of-sight millimeter wave small cell backhaul: 60, 70, 80 or 140 GHz?

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Planning the Base Station Layout in UMTS Urban Scenarios: A Simulation Approach to Coverage and Capacity Estimation

2 footprint and operational cost of a Cloud operator. However, there is a trade-off between energy consumption and perceived application performance. In order to save energy, Cloud operators want to consolidate as many Virtual Machines (VM) on the fewest possible physical servers, possibly involving overbooking of resources. However, that may involve SLA violations when many VMs run on peak load. Such consolidation is typically done using VM migration techniques, which stress the network. As a consequence, it is important to find the right balance between the energy consumption and the number of migrations to perform. Unfortunately, the resources that a VM requires are not precisely known in advance, which makes it very difficult to optimise the VM migration schedule. In this paper, we therefore propose a novel approach based on the theory of robust optimisation. We model the VM consolidation problem as a robust Mixed Integer Linear Program and allow to specify bounds for e.g. resource requirements of the VMs. We show that, by using our model, Cloud operators can effectively trade-off uncertainty of resource requirements with total energy consumption. Also, our model allows us to quantify the price of the robustness in terms of energy saving against resource requirement violations.

A fast robust optimization-based heuristic for the deployment of green virtual network functions

Users in a cellular network are allowed to move freely while the network handoffs their calls or data transfers among different access points. Studies demonstrate that the mobility pattern followed by the users has an impact on some metrics strongly related to the performance (i.e., handoff rate, cell residence time, etc). Some aspects of the Random Waypoint mobility model have been studied in depth, but relating those studies with different possible layouts for the access points still remains an open issue. The interest in forecasting the next cell where the device may be handed off is twofold: 1. it gives a deeper insight into the statistical study of the mobility model; 2. it is useful to manage the resource allocation to improve the performance. The goal of this paper is to provide an analytical framework for these predictions in a symmetric layout composed of four circular cells covering a circular area. Equations are provided along with numerical examples for a given scenario.

Energy Efficient Virtual Machine Consolidation under Uncertain Input Parameters for Green Data Centers

Spectrum scarcity together with high capacity demands make the use of millimeter wave (mmWave) frequencies an interesting alternative for next generation, i.e., fifth generation (5G), networks. Although mmWave is expected to play a key role for both access network and backhaul (BH), its initial use in the BH network seems more straight-forward. This stems from the fact that, in the BH case, its deployment is less challenging due to the fixed locations of BH transceivers. Still, provided that mmWave spectrum consists of several subbands, each one with different characteristics and thus different deployment constraints (e.g., channel bandwidth, maximum transmission power), a comparison is required in order to gain a better insight into the potentials of each solution. To that end, in this paper, the main mmWave candidate frequency bands are compared in terms of range, throughput and energy consumption. In our results, the bandwidth availability, the maximum transmission power as well as the antenna gains of each BH technology are taken into account, as defined by the Federal Communications Commission. The results are also compared with current industry-oriented state-of-the-art transceiver characteristics in order to gain further insights into the maximum achievable gains of each subband.