779 research outputs found
Acoustic detection of UHE neutrinos in the Mediterranean sea: Status and perspective
In recent years the astro-particle community is involved in the realization of experimental apparatuses for the detection of high energy neutrinos originated in cosmic sources or produced in the interaction of Cosmic Rays with the Cosmic Microwave Background. For neutrino energies in the TeV-PeV range, optical Cherenkov detectors, that have been so far positively exploited by Baikal[1], IceCube[2] and ANTARES[3], are considered optimal. For higher energies, three different experimental techniques are under study: the detection of radio pulses produced by showers induced by a neutrino interaction, the detection of air showers initiated by neutrinos interacting with rocks or deep Earth's atmosphere and the detection of acoustic waves produced by deposition of energy following the interaction of neutrinos in an acoustically transparent medium. The potential of the acoustic detection technique, first proposed by Askaryan[4], to build very large neutrino detectors is appealing, thanks to the optimal properties of media such as water or ice as sound propagator. Though the studies on this technique are still in an early stage, acoustic positioning systems used to locate the optical modules in underwater Cherenkov neutrino detectors, give the possibility to study the ambient noise and provide important information for the future analysis of acoustic data. © 2017 The Authors, published by EDP Sciences
Context Information for Fast Cell Discovery in mm-wave 5G Networks
The exploitation of the mm-wave bands is one of the most promising solutions
for 5G mobile radio networks. However, the use of mm-wave technologies in
cellular networks is not straightforward due to mm-wave harsh propagation
conditions that limit access availability. In order to overcome this obstacle,
hybrid network architectures are being considered where mm-wave small cells can
exploit an overlay coverage layer based on legacy technology. The additional
mm-wave layer can also take advantage of a functional split between control and
user plane, that allows to delegate most of the signaling functions to legacy
base stations and to gather context information from users for resource
optimization. However, mm-wave technology requires high gain antenna systems to
compensate for high path loss and limited power, e.g., through the use of
multiple antennas for high directivity. Directional transmissions must be also
used for the cell discovery and synchronization process, and this can lead to a
non-negligible delay due to the need to scan the cell area with multiple
transmissions at different directions. In this paper, we propose to exploit the
context information related to user position, provided by the separated control
plane, to improve the cell discovery procedure and minimize delay. We
investigate the fundamental trade-offs of the cell discovery process with
directional antennas and the effects of the context information accuracy on its
performance. Numerical results are provided to validate our observations.Comment: 6 pages, 8 figures, in Proceedings of European Wireless 201
Fast Cell Discovery in mm-wave 5G Networks with Context Information
The exploitation of mm-wave bands is one of the key-enabler for 5G mobile
radio networks. However, the introduction of mm-wave technologies in cellular
networks is not straightforward due to harsh propagation conditions that limit
the mm-wave access availability. Mm-wave technologies require high-gain antenna
systems to compensate for high path loss and limited power. As a consequence,
directional transmissions must be used for cell discovery and synchronization
processes: this can lead to a non-negligible access delay caused by the
exploration of the cell area with multiple transmissions along different
directions.
The integration of mm-wave technologies and conventional wireless access
networks with the objective of speeding up the cell search process requires new
5G network architectural solutions. Such architectures introduce a functional
split between C-plane and U-plane, thereby guaranteeing the availability of a
reliable signaling channel through conventional wireless technologies that
provides the opportunity to collect useful context information from the network
edge.
In this article, we leverage the context information related to user
positions to improve the directional cell discovery process. We investigate
fundamental trade-offs of this process and the effects of the context
information accuracy on the overall system performance. We also cope with
obstacle obstructions in the cell area and propose an approach based on a
geo-located context database where information gathered over time is stored to
guide future searches. Analytic models and numerical results are provided to
validate proposed strategies.Comment: 14 pages, submitted to IEEE Transaction on Mobile Computin
Traffic Management Applications for Stateful SDN Data Plane
The successful OpenFlow approach to Software Defined Networking (SDN) allows
network programmability through a central controller able to orchestrate a set
of dumb switches. However, the simple match/action abstraction of OpenFlow
switches constrains the evolution of the forwarding rules to be fully managed
by the controller. This can be particularly limiting for a number of
applications that are affected by the delay of the slow control path, like
traffic management applications. Some recent proposals are pushing toward an
evolution of the OpenFlow abstraction to enable the evolution of forwarding
policies directly in the data plane based on state machines and local events.
In this paper, we present two traffic management applications that exploit a
stateful data plane and their prototype implementation based on OpenState, an
OpenFlow evolution that we recently proposed.Comment: 6 pages, 9 figure
Relaxing state-access constraints in stateful programmable data planes
Supporting the programming of stateful packet forwarding functions in
hardware has recently attracted the interest of the research community. When
designing such switching chips, the challenge is to guarantee the ability to
program functions that can read and modify data plane's state, while keeping
line rate performance and state consistency. Current state-of-the-art designs
are based on a very conservative all-or-nothing model: programmability is
limited only to those functions that are guaranteed to sustain line rate, with
any traffic workload. In effect, this limits the maximum time to execute state
update operations. In this paper, we explore possible options to relax these
constraints by using simulations on real traffic traces. We then propose a
model in which functions can be executed in a larger but bounded time, while
preventing data hazards with memory locking. We present results showing that
such flexibility can be supported with little or no throughput degradation.Comment: 6 page
Finite-density corrections to the Unitary Fermi gas: A lattice perspective from Dynamical Mean-Field Theory
We investigate the approach to the universal regime of the dilute unitary
Fermi gas as the density is reduced to zero in a lattice model. To this end we
study the chemical potential, superfluid order parameter and internal energy of
the attractive Hubbard model in three different lattices with densities of
states (DOS) which share the same low-energy behavior of fermions in
three-dimensional free space: a cubic lattice, a "Bethe lattice" with a
semicircular DOS, and a "lattice gas" with parabolic dispersion and a sharp
energy cut-off that ensures the normalization of the DOS. The model is solved
using Dynamical Mean-Field Theory, that treats directly the thermodynamic limit
and arbitrarily low densities, eliminating finite-size effects. At densities of
the order of one fermion per site the lattice and its specific form dominate
the results. The evolution to the low-density limit is smooth and it does not
allow to define an unambiguous low-density regime. Such finite-density effects
are significantly reduced using the lattice gas, and they are maximal for the
three-dimensional cubic lattice. Even though dynamical mean-field theory is
bound to reduce to the more standard static mean field in the limit of zero
density due to the local nature of the self-energy and of the vertex functions,
it compares well with accurate Monte Carlo simulations down to the lowest
densities accessible to the latter.Comment: 9 pages, 8 figure
A Distributed Demand-Side Management Framework for the Smart Grid
This paper proposes a fully distributed Demand-Side Management system for
Smart Grid infrastructures, especially tailored to reduce the peak demand of
residential users. In particular, we use a dynamic pricing strategy, where
energy tariffs are function of the overall power demand of customers. We
consider two practical cases: (1) a fully distributed approach, where each
appliance decides autonomously its own scheduling, and (2) a hybrid approach,
where each user must schedule all his appliances. We analyze numerically these
two approaches, showing that they are characterized practically by the same
performance level in all the considered grid scenarios. We model the proposed
system using a non-cooperative game theoretical approach, and demonstrate that
our game is a generalized ordinal potential one under general conditions.
Furthermore, we propose a simple yet effective best response strategy that is
proved to converge in a few steps to a pure Nash Equilibrium, thus
demonstrating the robustness of the power scheduling plan obtained without any
central coordination of the operator or the customers. Numerical results,
obtained using real load profiles and appliance models, show that the
system-wide peak absorption achieved in a completely distributed fashion can be
reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to
meet the growing energy demand
SPIDER: Fault Resilient SDN Pipeline with Recovery Delay Guarantees
When dealing with node or link failures in Software Defined Networking (SDN),
the network capability to establish an alternative path depends on controller
reachability and on the round trip times (RTTs) between controller and involved
switches. Moreover, current SDN data plane abstractions for failure detection
(e.g. OpenFlow "Fast-failover") do not allow programmers to tweak switches'
detection mechanism, thus leaving SDN operators still relying on proprietary
management interfaces (when available) to achieve guaranteed detection and
recovery delays. We propose SPIDER, an OpenFlow-like pipeline design that
provides i) a detection mechanism based on switches' periodic link probing and
ii) fast reroute of traffic flows even in case of distant failures, regardless
of controller availability. SPIDER can be implemented using stateful data plane
abstractions such as OpenState or Open vSwitch, and it offers guaranteed short
(i.e. ms) failure detection and recovery delays, with a configurable trade off
between overhead and failover responsiveness. We present here the SPIDER
pipeline design, behavioral model, and analysis on flow tables' memory impact.
We also implemented and experimentally validated SPIDER using OpenState (an
OpenFlow 1.3 extension for stateful packet processing), showing numerical
results on its performance in terms of recovery latency and packet losses.Comment: 8 page
Adaptive Robust Traffic Engineering in Software Defined Networks
One of the key advantages of Software-Defined Networks (SDN) is the
opportunity to integrate traffic engineering modules able to optimize network
configuration according to traffic. Ideally, network should be dynamically
reconfigured as traffic evolves, so as to achieve remarkable gains in the
efficient use of resources with respect to traditional static approaches.
Unfortunately, reconfigurations cannot be too frequent due to a number of
reasons related to route stability, forwarding rules instantiation, individual
flows dynamics, traffic monitoring overhead, etc.
In this paper, we focus on the fundamental problem of deciding whether, when
and how to reconfigure the network during traffic evolution. We propose a new
approach to cluster relevant points in the multi-dimensional traffic space
taking into account similarities in optimal routing and not only in traffic
values. Moreover, to provide more flexibility to the online decisions on when
applying a reconfiguration, we allow some overlap between clusters that can
guarantee a good-quality routing regardless of the transition instant.
We compare our algorithm with state-of-the-art approaches in realistic
network scenarios. Results show that our method significantly reduces the
number of reconfigurations with a negligible deviation of the network
performance with respect to the continuous update of the network configuration.Comment: 10 pages, 8 figures, submitted to IFIP Networking 201
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