5,743 research outputs found
Service composition in stochastic settings
With the growth of the Internet-of-Things and online Web services, more services with more capabilities are available to us. The ability to generate new, more useful services from existing ones has been the focus of much research for over a decade. The goal is, given a specification of the behavior of the target service, to build a controller, known as an orchestrator, that uses existing services to satisfy the requirements of the target service. The model of services and requirements used in most work is that of a finite state machine. This implies that the specification can either be satisfied or not, with no middle ground. This is a major drawback, since often an exact solution cannot be obtained. In this paper we study a simple stochastic model for service composition: we annotate the tar- get service with probabilities describing the likelihood of requesting each action in a state, and rewards for being able to execute actions. We show how to solve the resulting problem by solving a certain Markov Decision Process (MDP) derived from the service and requirement specifications. The solution to this MDP induces an orchestrator that coincides with the exact solution if a composition exists. Otherwise it provides an approximate solution that maximizes the expected sum of values of user requests that can be serviced. The model studied although simple shades light on composition in stochastic settings and indeed we discuss several possible extensions
Learning Queuing Networks by Recurrent Neural Networks
It is well known that building analytical performance models in practice is
difficult because it requires a considerable degree of proficiency in the
underlying mathematics. In this paper, we propose a machine-learning approach
to derive performance models from data. We focus on queuing networks, and
crucially exploit a deterministic approximation of their average dynamics in
terms of a compact system of ordinary differential equations. We encode these
equations into a recurrent neural network whose weights can be directly related
to model parameters. This allows for an interpretable structure of the neural
network, which can be trained from system measurements to yield a white-box
parameterized model that can be used for prediction purposes such as what-if
analyses and capacity planning. Using synthetic models as well as a real case
study of a load-balancing system, we show the effectiveness of our technique in
yielding models with high predictive power
R&D for new silicon pixel sensors for the High Luminosity phase of the CMS experiment at LHC
The High Luminosity upgrade of the CERN LHC collider (HLLHC) demands a new high-radiation–tolerant solid-state pixel sensor capable of surviving fluencies up to a few 1016 neq/cm2 at ∼ 3 cm from the interaction point. To this extent the INFN ATLAS-CMS joint research activity, in collaboration with Fondazione Bruno Kessler (FBK), is aiming at the development of thin n-in-p–type pixel sensors for the HL-LHC. The R&D covers both planar and single-sided 3D columnar pixel devices made with the Si-Si Direct Wafer Bonding technique, which allows for the production of sensors with 100 μm and 130 μm active thickness for planar sensors, and 130 μm for 3D sensors, the thinnest ones ever produced so far. The first prototypes of hybrid modules, bump-bonded to the present CMS readout chip, have been tested on beam. The first results on their performance before and after irradiation are presented
The INFN R&D: New pixel detector for the High Luminosity upgrade of the LHC
The High Luminosity upgrade of the CERN-LHC (HL-LHC) demands for a new high-radiation tolerant solid-state pixel sensor capable of surviving fluencies up to a few 1016 particles/cm2 at ∼3 cm from the interaction point. To
this extent the INFN ATLAS-CMS joint research activity, in collaboration with Fondazione Bruno Kessler-FBK, is aiming at the development of thin n-in-p type pixel sensors for the HL-LHC. The R&D covers both planar and single-sided 3D
columnar pixel devices made with the Si-Si Direct Wafer Bonding technique, which allows for the production of sensors with 100 μm and 130 μm active thickness for planar sensors, and 130 μm for 3D sensors, the thinnest ones ever produced so far. The first prototypes of hybrid modules bump-bonded to the present CMS and ATLAS readout chips have been tested in beam tests. The preliminary results on their performance before and after irradiation are presented
Search for CP Violation in the decays D+ -> K_S pi+ and D+ -> K_S K+
A high statistics sample of photo-produced charm from the FOCUS(E831)
experiment at Fermilab has been used to search for direct CP violation in the
decays D+->K_S pi+ and D+ -> K_S K+. We have measured the following asymmetry
parameters relative to D+->K-pi+pi+: A_CP(K_S pi+) = (-1.6 +/- 1.5 +/- 0.9)%,
A_CP(K_S K+) = (+6.9 +/- 6.0 +/- 1.5)% and A_CP(K_S K+) = (+7.1 +/- 6.1 +/-
1.2)% relative to D+->K_S pi+. The first errors quoted are statistical and the
second are systematic. We also measure the relative branching ratios:
\Gamma(D+->\bar{K0}pi+)/\Gamma(D+->K-pi+pi+) = (30.60 +/- 0.46 +/- 0.32)%,
\Gamma(D+->\bar{K0}K+)/\Gamma(D+->K-pi+pi+) = (6.04 +/- 0.35 +/- 0.30)% and
\Gamma(D+->\bar{K0}K+)/\Gamma(D+->\bar{K0}pi+) = (19.96 +/- 1.19 +/- 0.96)%.Comment: 4 pages, 3 figure
A High Statistics Measurement of the Lambdac+ Lifetime
A high statistics measurement of the Lambdac+ lifetime from the Fermilab
fixed-target FOCUS photoproduction experiment is presented. We describe the
analysis technique with particular attention to the determination of the
systematic uncertainty. The measured value of 204.6 +/- 3.4 (stat.) +/- 2.5
(syst.) fs from 8034 +/- 122 Lambdac -> pKpi decays represents a significant
improvement over the present world average.Comment: Submitted to Physical Review Letter
The Target Silicon Detector for the FOCUS Spectrometer
We describe a silicon microstrip detector interleaved with segments of a
beryllium oxide target which was used in the FOCUS photoproduction experiment
at Fermilab. The detector was designed to improve the vertex resolution and to
enhance the reconstruction efficiency of short-lived charm particles.Comment: 18 pages, 14 figure
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