Proactive cloud management for highly heterogeneous multi-cloud infrastructures

Various literature studies demonstrated that the cloud computing paradigm can help to improve availability and performance of applications subject to the problem of software anomalies. Indeed, the cloud resource provisioning model enables users to rapidly access new processing resources, even distributed over different geographical regions, that can be promptly used in the case of, e.g., crashes or hangs of running machines, as well as to balance the load in the case of overloaded machines. Nevertheless, managing a complex geographically-distributed cloud deploy could be a complex and time-consuming task. Autonomic Cloud Manager (ACM) Framework is an autonomic framework for supporting proactive management of applications deployed over multiple cloud regions. It uses machine learning models to predict failures of virtual machines and to proactively redirect the load to healthy machines/cloud regions. In this paper, we study different policies to perform efficient proactive load balancing across cloud regions in order to mitigate the effect of software anomalies. These policies use predictions about the mean time to failure of virtual machines. We consider the case of heterogeneous cloud regions, i.e regions with different amount of resources, and we provide an experimental assessment of these policies in the context of ACM Framework

Non Markovian Quantum Repeated Interactions and Measurements

A non-Markovian model of quantum repeated interactions between a small quantum system and an infinite chain of quantum systems is presented. By adapting and applying usual pro jection operator techniques in this context, discrete versions of the integro-differential and time-convolutioness Master equations for the reduced system are derived. Next, an intuitive and rigorous description of the indirect quantum measurement principle is developed and a discrete non Markovian stochastic Master equation for the open system is obtained. Finally, the question of unravelling in a particular model of non-Markovian quantum interactions is discussed.Comment: 22 page

Hybridization of multi-objective deterministic particle swarm with derivative-free local searches

The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts

Pancreatic cancer patient survival correlates with DNA methylation of pancreas development genes.

DNA methylation is an epigenetic mark associated with regulation of transcription and genome structure. These markers have been investigated in a variety of cancer settings for their utility in differentiating normal tissue from tumor tissue. Here, we examine the direct correlation between DNA methylation and patient survival. We find that changes in the DNA methylation of key pancreatic developmental genes are strongly associated with patient survival

New Techniques for Relating Dynamically Close Galaxy Pairs to Merger and Accretion Rates : Application to the SSRS2 Redshift Survey

We introduce two new pair statistics, which relate close galaxy pairs to the merger and accretion rates. We demonstrate the importance of correcting these (and other) pair statistics for selection effects related to sample depth and completeness. In particular, we highlight the severe bias that can result from the use of a flux-limited survey. The first statistic, denoted N_c, gives the number of companions per galaxy, within a specified range in absolute magnitude. N_c is directly related to the galaxy merger rate. The second statistic, called L_c, gives the total luminosity in companions, per galaxy. This quantity can be used to investigate the mass accretion rate. Both N_c and L_c are related to the galaxy correlation function and luminosity function in a straightforward manner. We outline techniques which account for various selection effects, and demonstrate the success of this approach using Monte Carlo simulations. If one assumes that clustering is independent of luminosity (which is appropriate for reasonable ranges in luminosity), then these statistics may be applied to flux-limited surveys. These techniques are applied to a sample of 5426 galaxies in the SSRS2 redshift survey. Using close dynamical pairs, we find N_c(-21<M_B<-18) = 0.0226+/-0.0052 and L_c(-21<M_B<-18) = 0.0216+/-0.0055 10^{10} h^2 L_sun at z=0.015. These are the first secure estimates of low-z close pair statistics. If N_c remains fixed with redshift, simple assumptions imply that ~ 6.6% of present day galaxies with -21<M_B<-18 have undergone mergers since z=1. When applied to redshift surveys of more distant galaxies, these techniques will yield the first robust estimates of evolution in the galaxy merger and accretion rates. [Abridged]Comment: 26 pages (including 10 postscript figures) plus 3 gif figures. Accepted for publication in ApJ. Paper (including full resolution images) also available at http://www.astro.utoronto.ca/~patton/ssrs2, along with associated pair classification experiment (clickable version of Figure 5

Strangeness Production in pp,pA,AA Interactions at SPS Energies.HIJING Approach

In this report we have made a systematic study of strangeness production in proton-proton(pp),proton-nucleus(pA) and nucleus- nucleus(AA) collisions at CERN Super Proton Synchroton energies, using$\,\,\, HIJING\,\,\, MONTE \,\,\,CARLO \,\,\,MODEL$ \\ (version $HIJ.01$). Numerical results for mean multiplicities of neutral strange particles ,as well as their ratios to negatives hadrons($$) for p-p,nucleon-nucleon(N-N),\,\,p-S,\,\,p-Ag,\,\,p-Au('min. bias')collisions and p-Au,\,\,S-S,\,\,S-Ag,\,\,S-Au ('central')collisions are compared to experimental data available from CERN experiments and also with recent theoretical estimations given by others models. Neutral strange particle abundances are quite well described for p-p,N-N and p-A interactions ,but are underpredicted by a factor of two in A-A interactions for $\Lambda,\bar{\Lambda}, K^{0}_{S}$ in symmetric collisions(S-S,\,\,Pb-Pb)and for $\Lambda,\bar{\Lambda}\,\,$in asymmetric ones(S-Ag,\,\,S-Au,\,\,S-W). A qualitative prediction for rapidity, transverse kinetic energy and transverse momenta normalized distributions are performed at 200 GeV/Nucleon in p-S,S-S,S-Ag and S-Au collisions in comparison with recent experimental data. HIJING model predictions for coming experiments at CERN for S-Au, S-W and Pb-Pb interactions are given. The theoretical calculations are estimated in a full phase space.Comment: 33 pages(LATEX),18 figures not included,available in hard copy upon request , Dipartamento di Fisica Padova,report DFPD-94-NP-4