314 research outputs found
A fast GPU Monte Carlo Radiative Heat Transfer Implementation for Coupling with Direct Numerical Simulation
We implemented a fast Reciprocal Monte Carlo algorithm, to accurately solve
radiative heat transfer in turbulent flows of non-grey participating media that
can be coupled to fully resolved turbulent flows, namely to Direct Numerical
Simulation (DNS). The spectrally varying absorption coefficient is treated in a
narrow-band fashion with a correlated-k distribution. The implementation is
verified with analytical solutions and validated with results from literature
and line-by-line Monte Carlo computations. The method is implemented on GPU
with a thorough attention to memory transfer and computational efficiency. The
bottlenecks that dominate the computational expenses are addressed and several
techniques are proposed to optimize the GPU execution. By implementing the
proposed algorithmic accelerations, a speed-up of up to 3 orders of magnitude
can be achieved, while maintaining the same accuracy
Self-* overload control for distributed web systems
Unexpected increases in demand and most of all flash crowds are considered
the bane of every web application as they may cause intolerable delays or even
service unavailability. Proper quality of service policies must guarantee rapid
reactivity and responsiveness even in such critical situations. Previous
solutions fail to meet common performance requirements when the system has to
face sudden and unpredictable surges of traffic. Indeed they often rely on a
proper setting of key parameters which requires laborious manual tuning,
preventing a fast adaptation of the control policies. We contribute an original
Self-* Overload Control (SOC) policy. This allows the system to self-configure
a dynamic constraint on the rate of admitted sessions in order to respect
service level agreements and maximize the resource utilization at the same
time. Our policy does not require any prior information on the incoming traffic
or manual configuration of key parameters. We ran extensive simulations under a
wide range of operating conditions, showing that SOC rapidly adapts to time
varying traffic and self-optimizes the resource utilization. It admits as many
new sessions as possible in observance of the agreements, even under intense
workload variations. We compared our algorithm to previously proposed
approaches highlighting a more stable behavior and a better performance.Comment: The full version of this paper, titled "Self-* through self-learning:
overload control for distributed web systems", has been published on Computer
Networks, Elsevier. The simulator used for the evaluation of the proposed
algorithm is available for download at the address:
http://www.dsi.uniroma1.it/~novella/qos_web
The role of the tachyonic instability in Horndeski gravity
The tachyonic instability is associated with the unboundedness of the
Hamiltonian from below and results in an unstable low- regime. In the
cosmological exploration of modified gravity, it is seldom taken into account,
with more focus given to the popular no-ghost and no-gradient conditions. The
latter though are intrinsically high- statements. Here we combine all three
conditions into a full set of requirements that we show to guarantee stability
on the whole range of cosmological scales. We then explore the impact of the
different conditions on the parameter space of scalar-tensor gravity, with
particular emphasis on the no-tachyon one. We focus on Horndeski gravity and
also consider separately the two subclasses of and Generalized Brans
Dicke theories. We identify several interesting features, for instance in the
parameter space of designer on a CDM background, shedding light on
previous findings. When looking at the phenomenological functions and
, associated to the weak lensing and clustering potential respectively, we
find that in the case of Generalized Brans Dicke the no-tachyon condition
clearly cuts models with . This effect is less prevalent in
the Horndeski case due to the larger amount of free functions in the theory.Comment: 9 pages, 4 figures - accepted version by JCA
Towards a Realistic Model for Failure Propagation in Interdependent Networks
Modern networks are becoming increasingly interdependent. As a prominent
example, the smart grid is an electrical grid controlled through a
communications network, which in turn is powered by the electrical grid. Such
interdependencies create new vulnerabilities and make these networks more
susceptible to failures. In particular, failures can easily spread across these
networks due to their interdependencies, possibly causing cascade effects with
a devastating impact on their functionalities.
In this paper we focus on the interdependence between the power grid and the
communications network, and propose a novel realistic model, HINT
(Heterogeneous Interdependent NeTworks), to study the evolution of cascading
failures. Our model takes into account the heterogeneity of such networks as
well as their complex interdependencies. We compare HINT with previously
proposed models both on synthetic and real network topologies. Experimental
results show that existing models oversimplify the failure evolution and
network functionality requirements, resulting in severe underestimations of the
cascading failures.Comment: 7 pages, 6 figures, to be published in conference proceedings of IEEE
International Conference on Computing, Networking and Communications (ICNC
2016), Kauai, US
On the vulnerabilities of voronoi-based approaches to mobile sensor deployment
Mobile sensor networks are the most promising solution to cover an Area of Interest (AoI) in safety critical scenarios. Mobile devices can coordinate with each other according to a distributed deployment algorithm, without resorting to human supervision for device positioning and network configuration. In this paper, we focus on the vulnerabilities of the deployment algorithms based on Voronoi diagrams to coordinate mobile sensors and guide their movements. We give a geometric characterization of possible attack configurations, proving that a simple attack consisting of a barrier of few compromised sensors can severely reduce network coverage. On the basis of the above characterization, we propose two new secure deployment algorithms, named SecureVor and Secure Swap Deployment (SSD). These algorithms allow a sensor to detect compromised nodes by analyzing their movements, under different and complementary operative settings. We show that the proposed algorithms are effective in defeating a barrier attack, and both have guaranteed termination. We perform extensive simulations to study the performance of the two algorithms and compare them with the original approach. Results show that SecureVor and SSD have better robustness and flexibility and excellent coverage capabilities and deployment time, even in the presence of an attac
Do current cosmological observations rule out all Covariant Galileons?
We revisit the cosmology of Covariant Galileon gravity in view of the most
recent cosmological data sets, including weak lensing. As a higher derivative
theory, Covariant Galileon models do not have a CDM limit and predict
a very different structure formation pattern compared with the standard
CDM scenario. Previous cosmological analyses suggest that this model
is marginally disfavoured, yet can not be completely ruled out. In this work we
use a more recent and extended combination of data, and we allow for more
freedom in the cosmology, by including a massive neutrino sector with three
different mass hierarchies. We use the Planck measurements of Cosmic Microwave
Background temperature and polarization; Baryonic Acoustic Oscillations
measurements by BOSS DR12; local measurements of ; the joint light-curve
analysis supernovae sample; and, for the first time, weak gravitational lensing
from the KiDS collaboration. We find, that in order to provide a reasonable
fit, a non-zero neutrino mass is indeed necessary, but we do not report any
sizable difference among the three neutrino hierarchies. Finally, the
comparison of the Bayesian Evidence to the CDM one shows that in all
the cases considered, Covariant Galileon models are statistically ruled out by
cosmological data.Comment: 8 pages, 5 figures, 2 tables. The Covariant Galileon patch of EFTCAMB
is released in the EFTCAMB developers version - accepted version by PR
Network recovery after massive failures
This paper addresses the problem of efficiently restoring sufficient resources in a communications network to support the demand of mission critical services after a large scale disruption. We give a formulation of the problem as an MILP and show that it is NP-hard. We propose a polynomial time heuristic, called Iterative Split and Prune (ISP) that decomposes the original problem recursively into smaller problems, until it determines the set of network components to be restored. We performed extensive simulations by varying the topologies, the demand intensity, the number of critical services, and the disruption model. Compared to several greedy approaches ISP performs better in terms of number of repaired components, and does not result in any demand loss. It performs very close to the optimal when the demand is low with respect to the supply network capacities, thanks to the ability of the algorithm to maximize sharing of repaired resources
: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing
The sharing-economy-based business model has recently seen success in the
transportation and accommodation sectors with companies like Uber and Airbnb.
There is growing interest in applying this model to energy systems, with
modalities like peer-to-peer (P2P) Energy Trading, Electric Vehicles (EV)-based
Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and
Battery Swapping Technology (BST). In this work, we exploit the increasing
diffusion of EVs to realize a crowdsourcing platform called e-Uber that jointly
enables ride-sharing and energy-sharing through V2G and BST. e-Uber exploits
spatial crowdsourcing, reinforcement learning, and reverse auction theory.
Specifically, the platform uses reinforcement learning to understand the
drivers' preferences towards different ride-sharing and energy-sharing tasks.
Based on these preferences, a personalized list is recommended to each driver
through CMAB-based Algorithm for task Recommendation System (CARS). Drivers bid
on their preferred tasks in their list in a reverse auction fashion. Then
e-Uber solves the task assignment optimization problem that minimizes cost and
guarantees V2G energy requirement. We prove that this problem is NP-hard and
introduce a bipartite matching-inspired heuristic, Bipartite Matching-based
Winner selection (BMW), that has polynomial time complexity. Results from
experiments using real data from NYC taxi trips and energy consumption show
that e-Uber performs close to the optimum and finds better solutions compared
to a state-of-the-art approachComment: Preprint, under revie
On critical service recovery after massive network failures
This paper addresses the problem of efficiently restoring sufficient resources in a communications network to support the demand of mission critical services after a large-scale disruption. We give a formulation of the problem as a mixed integer linear programming and show that it is NP-hard. We propose a polynomial time heuristic, called iterative split and prune (ISP) that decomposes the original problem recursively into smaller problems, until it determines the set of network components to be restored. ISP's decisions are guided by the use of a new notion of demand-based centrality of nodes. We performed extensive simulations by varying the topologies, the demand intensity, the number of critical services, and the disruption model. Compared with several greedy approaches, ISP performs better in terms of total cost of repaired components, and does not result in any demand loss. It performs very close to the optimal when the demand is low with respect to the supply network capacities, thanks to the ability of the algorithm to maximize sharing of repaired resources
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