Enhancing Federated Cloud Management with an Integrated Service Monitoring Approach

Cloud Computing enables the construction and the provisioning of virtualized service-based applications in a simple and cost effective outsourcing to dynamic service environments. Cloud Federations envisage a distributed, heterogeneous environment consisting of various cloud infrastructures by aggregating different IaaS provider capabilities coming from both the commercial and the academic area. In this paper, we introduce a federated cloud management solution that operates the federation through utilizing cloud-brokers for various IaaS providers. In order to enable an enhanced provider selection and inter-cloud service executions, an integrated monitoring approach is proposed which is capable of measuring the availability and reliability of the provisioned services in different providers. To this end, a minimal metric monitoring service has been designed and used together with a service monitoring solution to measure cloud performance. The transparent and cost effective operation on commercial clouds and the capability to simultaneously monitor both private and public clouds were the major design goals of this integrated cloud monitoring approach. Finally, the evaluation of our proposed solution is presented on different private IaaS systems participating in federations. © 2013 Springer Science+Business Media Dordrecht

The Hubbard model within the equations of motion approach

The Hubbard model has a special role in Condensed Matter Theory as it is considered as the simplest Hamiltonian model one can write in order to describe anomalous physical properties of some class of real materials. Unfortunately, this model is not exactly solved except for some limits and therefore one should resort to analytical methods, like the Equations of Motion Approach, or to numerical techniques in order to attain a description of its relevant features in the whole range of physical parameters (interaction, filling and temperature). In this manuscript, the Composite Operator Method, which exploits the above mentioned analytical technique, is presented and systematically applied in order to get information about the behavior of all relevant properties of the model (local, thermodynamic, single- and two- particle ones) in comparison with many other analytical techniques, the above cited known limits and numerical simulations. Within this approach, the Hubbard model is shown to be also capable to describe some anomalous behaviors of the cuprate superconductors.Comment: 232 pages, more than 300 figures, more than 500 reference

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Search for direct pair production of the top squark in all-hadronic final states in proton-proton collisions at s√=8 TeV with the ATLAS detector

The results of a search for direct pair production of the scalar partner to the top quark using an integrated luminosity of 20.1fb−1 of proton–proton collision data at √s = 8 TeV recorded with the ATLAS detector at the LHC are reported. The top squark is assumed to decay via t˜→tχ˜01 or t˜→ bχ˜±1 →bW(∗)χ˜01 , where χ˜01 (χ˜±1 ) denotes the lightest neutralino (chargino) in supersymmetric models. The search targets a fully-hadronic final state in events with four or more jets and large missing transverse momentum. No significant excess over the Standard Model background prediction is observed, and exclusion limits are reported in terms of the top squark and neutralino masses and as a function of the branching fraction of t˜ → tχ˜01 . For a branching fraction of 100%, top squark masses in the range 270–645 GeV are excluded for χ˜01 masses below 30 GeV. For a branching fraction of 50% to either t˜ → tχ˜01 or t˜ → bχ˜±1 , and assuming the χ˜±1 mass to be twice the χ˜01 mass, top squark masses in the range 250–550 GeV are excluded for χ˜01 masses below 60 GeV

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Correlation between impact factor and public availability of published research data in Information Science and Library Science journals

The final publication is available at Springer via http://dx.doi.org/10.1007/s11192-016-1868-7[EN] Scientists continuously generate research data but only a few of them are published. If these data were accessible and reusable, researchers could examine them and generate new knowledge. Our purpose is to determine whether there is a relationship between the impact factor and the policies concerning open availability of raw research data in journals of Information Science and Library Science (ISLS) subject category from the Web of Science database. We reviewed the policies related to public availability of papers and data sharing in the 85 journals included in the ISLS category of the Journal Citation Reports in 2012. The relationship between public availability of published data and impact factor of journals is analysed through different statistical tests. The variable "statement of complementary material" was accepted in 50 % of the journals; 65 % of the journals support "reuse"; 67 % of the journals specified "storage in thematic or institutional repositories"; the "publication of the manuscript in a website" was accepted in 69 % of the journals. We have found a 50 % of journals that include the possibility to deposit data as supplementary material, and more than 60 % accept reuse, storage in repositories and publication in websites. There is a clear positive relationship between being a top journal in impact factor ranking of JCR and having an open policy.This work has benefited from assistance by the National R+D+I of the Ministry of Economy and Competitiveness of the Spanish Government (CSO2012-39632-C02).Aleixandre-Benavent, R.; Moreno-Solano, L.; Ferrer Sapena, A.; Sánchez Pérez, EA. (2016). Correlation between impact factor and public availability of published research data in Information Science and Library Science journals. Scientometrics. 107(1):1-13. https://doi.org/10.1007/s11192-016-1868-7S1131071Aleixandre-Benavent, R., Vidal-Infer, A., Alonso-Arroyo, A., Valderrama-Zurián, J. C., Bueno-Cañigral, F., & Ferrer-Sapena, A. (2014). Public availability of published research data in substance abuse journals. International Journal of Drug Policy, 25, 1143–1146.Alsheikh-Ali, A. A., Qureshi, W., Al-Mallah, M. H., & Ioannidis, J. P. A. (2011). Public availability of published research data in high-impact journals. PLoS One, 6(9), e24357.Anderson, B. J., & Merry, A. F. (2009). Data sharing for pharmacokinetic studies. Paediatr Anaesthes, 19(10), 1005–1010.Blahous, B., Gorraiz, J., Gumpenberger, C., Lehner, O., Stein, B., & Ulrych, U. (2015). Research data policies in scientific journals—An empirical study. Zeitschrift fur Bibliothekswesen und Bibliographie, 62(1), 12–24.Borrego, A., & Garcia, F. (2013). Provision of supplementary materials in Library and Information Science scholarly journals. Aslib Proceedings, 65(5), 503–514.Cech, T. R. (2003). Sharing publication-related data and materials: responsibilities of authorship in the life sciences. www.nap.edu/books/0309088593/html . Accessed 24 November 2015CODATA. (2015). http://www.codata.org . Accessed 21 February 2015Conradie, P., & Choenni, S. (2014). On the barriers for local government releasing open data. Government Information Quarterly, 31, S10–S17.De Castro, P., Calzolari, A., Napolitani, F., Maria Rossi, A., Mabile, L., Cambon-Thomsen, A., & Bravo, E. (2013). Open data sharing in the context of bioresources. Acta Informatica Medica, 21(4), 291–292.Digital Curation Centre (DCC). (2015). http://www.dcc.ac.uk . Accessed 4 March 2015European Commission. (2015). Guidelines on open access to scientific publications and research data in Horizon 2020. European Commission, 2013. http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot-guide_en.pdf . Accessed 15 March 2015Fear, K. (2015). Building outreach on assessment: Researcher compliance with journal policies for data sharing. Bulletin of the Association for Information Science and Technology, 41(6), 18–21.González, L. M., Saorín, T., Ferrer, A., Aleixandre-Benavent, R., & Peset, F. (2013). Gestión de datos de investigación: infraestructuras para su difusión. Professional Information, 22, 414–423.Jones, R. B., Reeves, D., & Martinez, C. S. (2012). Overview of electronic data sharing: Why, how, and impact. Current Oncology Reports, 14(6), 486–493.Kaye, J. (2012). The tension between data sharing and the protection of privacy ingenomics research. Annual Review of Genomics and Human Genetics, 13, 415–431.Leonelli, S., Smirnoff, N., Moore, J., Cook, C., & Bastow, R. (2013). Making open data work for plant scientists. Journal of Experimental Botany, 64(14), 4109–41017.National Institutes of Health (NIH). (2015). Data sharing policy. http://grants.nih.gov/grants/policy/data_sharing/index.htm . Accessed 3 March 2015National Science Foundation (NSF). (2014). Dissemination and sharing of research results. NSF Data Sharing Policy. http://www.nsf.gov/bfa/dias/policy/dmp.jsp . Accessed 21 November 2014Nelson, B. (2009). Data sharing: Empty archives. Nature, 461(7261), 160–163.Open Knowledge Foundation. (2015). https://okfn.org/ . Accessed 3 March 2015Pisani, E., & AbouZahr, C. (2010). Sharing health data: Good intentions are not enough. Bulletin of the World Health Organization, 88(6), 462–466.Piwowar, H. A., Day, R. S., & Fridsma, D. B. (2007). Sharing detailed research data is associated with increased citation rate. PLoS One, 2(3), e308.Piwowar, H. A., & Chapman, W.W. (2008). A review of journal policies for sharing research data. http://precedings.nature.com/documents/1700/version/1.hdl:10101/npre.2008.1700.1 . Accessed 11 December 2015Piwowar, H. A., & Todd, J. (2013). Data reuse and the open data citation advantage. PeerJ, 1, e175.Registry of Research Data Repositories (re3data). (2015). http://www.re3data.org/2014/03/re3data-org-from-funding-to-growing/ . Accessed 5 March 2015Savage, C. J., & Vickers, A. J. (2009). Empirical study of data saharing by authors publishing in PLOS journals. PLoS One, 4(9), e7078.Sayogo, D. S., & Pardo, T. A. (2013). Exploring the determinants of scientific data sharing: Understanding the motivation to publish research data. Government Information Quarterly, 30, S19–S31.Spencer, H. (2015). Thoughts on the sharing of data and research materials and the role of journal policies. http://web.stanford.edu/~vcs/Nov21/hilary_spencer_rdcscsJan2010.pdf . Accessed 11 December 2015Sturges, P., Bamkin, M., Anders, J., & Hussain, A. (2014). Journals and their policies on research data sharing. https://jordproject.wordpress.com/reportsand-article/journals-and-their-policies-on-research-data-sharing/ . Accessed 24 November 2015Tenenbaum, J. D., Sansone, S. A., & Haendel, M. (2007). A sea of standards for omics data: sink or swim? Journal of the American Medical Informatics Association, 21(2), 200–203.Tenopir, C., Allard, S., Douglass, K., Aydinoglu, A. U., Wu, L., Read, E., et al. (2011). Data sharing by scientists: Practices and perceptions. PLoS One, 6(6), e21101.The Royal Society Publishing. (2015). http://royalsocietypublishing.org/data-sharing . Accessed 15 March 2015Toronto International Data Release Workshop Authors. (2009). Prepublication data sharing. Nature, 461(7261), 168–170.Van Noorden, R. (2013). Data-sharing: Everything on display. Nature, 500, 243–245.Wellcome Trust. (2015). Data sharing. http://www.wellcome.ac.uk/About-us/Policy/Spotlight-issues/Data-sharing/ . Accessed 21 January 201