EPOBF: Energy Efficient Allocation of Virtual Machines in High Performance Computing Cloud

Cloud computing has become more popular in provision of computing resources under virtual machine (VM) abstraction for high performance computing (HPC) users to run their applications. A HPC cloud is such cloud computing environment. One of challenges of energy efficient resource allocation for VMs in HPC cloud is tradeoff between minimizing total energy consumption of physical machines (PMs) and satisfying Quality of Service (e.g. performance). On one hand, cloud providers want to maximize their profit by reducing the power cost (e.g. using the smallest number of running PMs). On the other hand, cloud customers (users) want highest performance for their applications. In this paper, we focus on the scenario that scheduler does not know global information about user jobs and user applications in the future. Users will request shortterm resources at fixed start times and non interrupted durations. We then propose a new allocation heuristic (named Energy-aware and Performance per watt oriented Bestfit (EPOBF)) that uses metric of performance per watt to choose which most energy-efficient PM for mapping each VM (e.g. maximum of MIPS per Watt). Using information from Feitelson's Parallel Workload Archive to model HPC jobs, we compare the proposed EPOBF to state of the art heuristics on heterogeneous PMs (each PM has multicore CPU). Simulations show that the EPOBF can reduce significant total energy consumption in comparison with state of the art allocation heuristics.Comment: 10 pages, in Procedings of International Conference on Advanced Computing and Applications, Journal of Science and Technology, Vietnamese Academy of Science and Technology, ISSN 0866-708X, Vol. 51, No. 4B, 201

Energy-Aware Lease Scheduling in Virtualized Data Centers

Energy efficiency has become an important measurement of scheduling algorithms in virtualized data centers. One of the challenges of energy-efficient scheduling algorithms, however, is the trade-off between minimizing energy consumption and satisfying quality of service (e.g. performance, resource availability on time for reservation requests). We consider resource needs in the context of virtualized data centers of a private cloud system, which provides resource leases in terms of virtual machines (VMs) for user applications. In this paper, we propose heuristics for scheduling VMs that address the above challenge. On performance evaluation, simulated results have shown a significant reduction on total energy consumption of our proposed algorithms compared with an existing First-Come-First-Serve (FCFS) scheduling algorithm with the same fulfillment of performance requirements. We also discuss the improvement of energy saving when additionally using migration policies to the above mentioned algorithms.Comment: 10 pages, 2 figures, Proceedings of the Fifth International Conference on High Performance Scientific Computing, March 5-9, 2012, Hanoi, Vietna

Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods

This article takes an approach to creating a machine learning model for the oil and gas industry. This task is dedicated to the most up-to-date issues of machine learning and artificial intelligence. One of the goals of this research was to build a model to predict the possible risks arising in the process of drilling wells. Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays, companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work describes a comparison of machine learning algorithms for anomaly detection during well drilling. In particular, machine learning algorithms will make it possible to make decisions when determining the geometry of the grid of wells—the nature of the relative position of production and injection wells at the production facility. Development systems are most often subdivided into the following: placement of wells along a symmetric grid, and placement of wells along a non-symmetric grid (mainly in rows). The tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we used historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. The experiential results illustrate that a model based on gradient boosting can classify the complications in the drilling process better than other models.publishedVersio

КОРРЕКЦИЯ ИММУНОЛОГИЧЕСКИХ НАРУШЕНИЙ У БЕРЕМЕННЫХ С ХРОНИЧЕСКИМ ПИЕЛОНЕФРИТОМ

Дослідження проведено з метою оцінки ефективності пребіотика лактулози як імунокорегуючого засобу при загостренні хронічного пієлонефриту у вагітних із функціональними запорами. Матеріал та методи дослідження. Проведено обстеження та лікування 37 вагітних жінок із загостренням хронічного пієлонефриту, які страждають функціональними закрепами. Тривалість спостереження склала 28 діб. У всіх вагітних вивчали показники антиендотоксинового імунітету, фагоцитарну активність клітин крові та рівень цитокінів. Результати дослідження. Виявлено, що у пацієнток, які отримували лактулозу в комплексному лікуванні пієлонефриту, нормалізуються такі показники антиендотоксінових імунітету як Ig класів А, M і G до ліпополісахариду кишкової палички та рівень експресії ліпополісахаридзв’язуючих рецепторів гранулоцитами і моноцитами периферичної крові, а також підвищується фагоцитарна активність гранулоцитів периферичної крові, як фактор неспецифічного захисту, при достовірно якнайшвидшому зниженні активності запальних реакцій. Висновки. Використання пребіотика лактулози у комплексному лікуванні загострення хронічного пієлонефриту у вагітних з функціональними закрепами веде до нормалізації вказаних показників антиендотоксинового імунітету, а також до підвищення фагоцитарної активності гранулоцитів периферичної крові

DESIGNING THE LIFE PATH OF THE YOUTH LIVING IN MOSCOW AND THE REGIONS

The features of the construction of the life path by young people, living in the regions, Moscow and those who moved to the capital, are shown. Having used narrative interview and the “life line” method, differences in ideas about the future life path were shown. The result of the study are description of the process of choosing a certain life trajectory, the factors contributing to the choice of more desirable path and activity in a situation of uncertainty.Изучены особенности конструирования жизненного пути молодежью, проживающей в регионах, Москве, и тех, кто переехал в столицу. С помощью нарративного интервью и методики «линия жизни» показаны различия в представлениях о дальнейшем жизненном пути. Результатом исследования стало описание процесса выбора жизненной траектории, а также выделены факторы, способствующие выбору желаемого пути и активности в ситуации неопределенности

Towards energy aware cloud computing application construction

The energy consumption of cloud computing continues to be an area of significant concern as data center growth continues to increase. This paper reports on an energy efficient interoperable cloud architecture realised as a cloud toolbox that focuses on reducing the energy consumption of cloud applications holistically across all deployment models. The architecture supports energy efficiency at service construction, deployment and operation. We discuss our practical experience during implementation of an architectural component, the Virtual Machine Image Constructor (VMIC), required to facilitate construction of energy aware cloud applications. We carry out a performance evaluation of the component on a cloud testbed. The results show the performance of Virtual Machine construction, primarily limited by available I/O, to be adequate for agile, energy aware software development. We conclude that the implementation of the VMIC is feasible, incurs minimal performance overhead comparatively to the time taken by other aspects of the cloud application construction life-cycle, and make recommendations on enhancing its performance

Measurement of the Beam Asymmetry Σ\Sigma in the Forward Direction for pi0 Photoproduction

Photoproduction of neutral pions has been studied with the CBELSA/TAPS detector for photon energies between 0.92 and 1.68~GeV at the electron accelerator ELSA. The beam asymmetry~$\Sigma$ has been extracted for $115^\circ < \theta_{\rm c.m.} < 155^\circ$ of the $\pi^0$~meson and for $\theta_{\rm c.m.} < 60^\circ$. The new beam asymmetry data improve the world database for photon energies above 1.5~GeV and, by covering the very forward region, extend previously published data for the same reaction by our collaboration. The angular dependence of $\Sigma$ shows overall good agreement with the SAID parameterization.Comment: 11 pages, 10 figures, published in Phys. Rev. C, included LEPS data and MAID 2007 predictions for comparison with our dat