Virtual Machines Performance Modeling with Support Vector Regressions

Virtualization is a key technology in cloudcomputing to render on-demand provisioning of virtual services.Xen, an open source paravirtualized virtual machine monitor(hypervisor), has been adopted by many leading data centersof the world today. A scheduler in Xen handles CPU resourcessharing among virtual machines hosted on the same physicalsystem. This study is focused on a scheduler in the currentXen release - the Credit scheduler. Credit uses two parameters(weight and cap) to fine tune CPU resources sharing. Previousstudies have shown that these two parameters can impact variousperformance measures of virtual machines hosted on Xen. In thisstudy, we present a holistic procedure to establish performancemodels of virtual machines. Empirical data of two commonly usedmeasures, namely calculation power and network throughput,were collected by simulations under various settings of weightand cap. We then employed a powerful machine learning tool(multi-kernel support vector regression) to learn performancemodels from the empirical data. These models were evaluatedsatisfactorily by using established procedures in machinelearning

Role of A20 in cIAP-2 Protection against Tumor Necrosis Factor α (TNF-α)-Mediated Apoptosis in Endothelial Cells

Tumor necrosis factor α (TNF-α) influences endothelial cell viability by altering the regulatory molecules involved in induction or suppression of apoptosis. However, the underlying mechanisms are still not completely understood. In this study, we demonstrated that A20 (also known as TNFAIP3, tumor necrosis factor α-induced protein 3, and an anti-apoptotic protein) regulates the inhibitor of apoptosis protein-2 (cIAP-2) expression upon TNF-α induction in endothelial cells. Inhibition of A20 expression by its siRNA resulted in attenuating expression of TNF-α-induced cIAP-2, yet not cIAP-1 or XIAP. A20-induced cIAP-2 expression can be blocked by the inhibition of phosphatidyl inositol-3 kinase (PI3-K), but not nuclear factor (NF)-κB, while concomitantly increasing the number of endothelial apoptotic cells and caspase 3 activation. Moreover, TNF-α-mediated induction of apoptosis was enhanced by A20 inhibition, which could be rescued by cIAP-2. Taken together, these results identify A20 as a cytoprotective factor involved in cIAP-2 inhibitory pathway of TNF-α-induced apoptosis. This is consistent with the idea that endothelial cell viability is dependent on interactions between inducers and suppressors of apoptosis, susceptible to modulation by TNF-α

Prostaglandin E(2 )receptor subtype 2 (EP2) regulates microglial activation and associated neurotoxicity induced by aggregated α-synuclein

BACKGROUND: The pathogenesis of idiopathic Parkinson's disease (PD) remains elusive, although evidence has suggested that neuroinflammation characterized by activation of resident microglia in the brain may contribute significantly to neurodegeneration in PD. It has been demonstrated that aggregated α-synuclein potently activates microglia and causes neurotoxicity. However, the mechanisms by which aggregated α-synuclein activates microglia are not understood fully. METHODS: We investigated the role of prostaglandin E(2 )receptor subtype 2 (EP2) in α-synuclein aggregation-induced microglial activation using ex vivo, in vivo and in vitro experimental systems. RESULTS: Results demonstrated that ablation of EP2(EP2(-/-)) significantly enhanced microglia-mediated ex vivo clearance of α-synuclein aggregates (from mesocortex of Lewy body disease patients) while significantly attenuating neurotoxicity and extent of α-synuclein aggregation in mice treated with a parkinsonian toxicant 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. Furthermore, we report that reduced neurotoxicity by EP2(-/- )microglia could be attributed to suppressed translocation of a critical cytoplasmic subunit (p47-phox) of NADPH oxidase (PHOX) to the membranous compartment after exposure to aggregated α-synuclein. CONCLUSION: Thus, it appears that microglial EP2 plays a critical role in α-synuclein-mediated neurotoxicity

Fermion confinement induced by geometry

We consider a five-dimensional model in which fermions are confined in a hypersurface due to an interaction with a purely geometric field. Inspired by the Rubakov-Shaposhnikov field-theoretical model, in which massless fermions can be localized in a domain wall through the interaction of a scalar field, we show that particle confinement may also take place if we endow the five-dimensional bulk with a Weyl integrable geometric structure, or if we assume the existence of a torsion field acting in the bulk. In this picture, the kind of interaction considered in the Rubakov-Shaposhnikov model is replaced by the interaction of fermions with a geometric field, namely a Weyl scalar field or a torsion field. We show that in both cases the confinement is independent of the energy and the mass of the fermionic particle. We generalize these results to the case in which the bulk is an arbitrary n-dimensional curved space.Comment: 8 page

Dynamics of electrons in the quantum Hall bubble phases

In Landau levels N > 1, the ground state of the two-dimensional electron gas (2DEG) in a perpendicular magnetic field evolves from a Wigner crystal for small filling of the partially filled Landau level, into a succession of bubble states with increasing number of guiding centers per bubble as the filling increases, to a modulated stripe state near half filling. In this work, we show that these first-order phase transitions between the bubble states lead to measurable discontinuities in several physical quantities such as the density of states and the magnetization of the 2DEG. We discuss in detail the behavior of the collective excitations of the bubble states and show that their spectra have higher-energy modes besides the pinned phonon mode. The frequencies of these modes, at small wavevector k, have a discontinuous evolution as a function of filling factor that should be measurable in, for example, microwave absorption experiments.Comment: 13 pages, 7 figures. Corrected typos in eqs. (38),(39),(40

Convective Systems over the South China Sea: Cloud-Resolving Model Simulations

The two-dimensional version of the Goddard Cumulus Ensemble (GCE) model is used to simulate two South China Sea Monsoon Experiment (SCSMEX) convective periods [18–26 May (prior to and during the monsoon onset) and 2–11 June (after the onset of the monsoon) 1998]. Observed large-scale advective tendencies for potential temperature, water vapor mixing ratio, and horizontal momentum are used as the main forcing in governing the GCE model in a semiprognostic manner. The June SCSMEX case has stronger forcing in both temperature and water vapor, stronger low-level vertical shear of the horizontal wind, and larger convective available potential energy (CAPE). The temporal variation of the model-simulated rainfall, time- and domain-averaged heating, and moisture budgets compares well to those diagnostically determined from soundings. However, the model results have a higher temporal variability. The model underestimates the rainfall by 17 % to 20 % compared to that based on soundings. The GCE model-simulated rainfall for June is in very good agreement with the Tropical Rainfall Measuring Mission (TRMM), precipitation radar (PR), and the Global Precipitation Climatology Project (GPCP). Overall, the model agrees better with observations for the June case rather than the May case. The model-simulated energy budgets indicate that the two largest terms for both cases are net condensatio

Robustness and Generalization

We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is "similar" to a training sample, then the testing error is close to the training error. This provides a novel approach, different from the complexity or stability arguments, to study generalization of learning algorithms. We further show that a weak notion of robustness is both sufficient and necessary for generalizability, which implies that robustness is a fundamental property for learning algorithms to work

Cosmic acceleration and phantom crossing in f(T)f(T)-gravity

In this paper, we propose two new models in $f(T)$ gravity to realize universe acceleration and phantom crossing due to dark torsion in the formalism. The model parameters are constrained and the observational test are discussed. The best fit results favors an accelerating universe with possible phantom crossing in the near past or future followed respectively by matter and radiation dominated era.Comment: 20 pages, 18 figures, Will appear in Astrophys Space Sc