640 research outputs found

    Teadusarvutuse algoritmide taandamine hajusarvutuse raamistikele

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    Teadusarvutuses kasutatakse arvuteid ja algoritme selleks, et lahendada probleeme erinevates reaalteadustes nagu geneetika, bioloogia ja keemia. Tihti on eesmĂ€rgiks selliste loodusnĂ€htuste modelleerimine ja simuleerimine, mida pĂ€ris keskkonnas oleks vĂ€ga raske uurida. NĂ€iteks on vĂ”imalik luua pĂ€ikesetormi vĂ”i meteoriiditabamuse mudel ning arvutisimulatsioonide abil hinnata katastroofi mĂ”ju keskkonnale. Mida keerulisemad ja tĂ€psemad on sellised simulatsioonid, seda rohkem arvutusvĂ”imsust on vaja. Tihti kasutatakse selleks suurt hulka arvuteid, mis kĂ”ik samaaegselt töötavad ĂŒhe probleemi kallal. Selliseid arvutusi nimetatakse paralleel- vĂ”i hajusarvutusteks. Hajusarvutuse programmide loomine on aga keeruline ning nĂ”uab palju rohkem aega ja ressursse, kuna vaja on sĂŒnkroniseerida erinevates arvutites samaaegselt tehtavat tööd. On loodud mitmeid tarkvararaamistikke, mis lihtsustavad seda tööd automatiseerides osa hajusprogrammeerimisest. Selle teadustöö eesmĂ€rk oli uurida selliste hajusarvutusraamistike sobivust keerulisemate teadusarvutuse algoritmide jaoks. Tulemused nĂ€itasid, et olemasolevad raamistikud on ĂŒksteisest vĂ€ga erinevad ning neist ĂŒkski ei ole sobiv kĂ”igi erinevat tĂŒĂŒpi algoritmide jaoks. MĂ”ni raamistik on sobiv ainult lihtsamate algoritmide jaoks; mĂ”ni ei sobi olukorras, kus andmed ei mahu arvutite mĂ€llu. Algoritmi jaoks kĂ”ige sobivama hajusarvutisraamistiku valimine vĂ”ib olla vĂ€ga keeruline ĂŒlesanne, kuna see nĂ”uab olemasolevate raamistike uurimist ja rakendamist. Sellele probleemile lahendust otsides otsustati luua dĂŒnaamiline algoritmide modelleerimise rakendus (DAMR), mis oskab simuleerida algoritmi implementatsioone erinevates hajusarvutusraamistikes. DAMR aitab hinnata milline hajusraamistik on kĂ”ige sobivam ette antud algoritmi jaoks, ilma algoritmi reaalselt ĂŒhegi hajusraamistiku peale implementeerimata. Selle uurimustöö peamine panus on hajusarvutusraamistike kasutuselevĂ”tu lihtsamaks tegemine teadlastele, kes ei ole varem nende kasutamisega kokku puutunud. See peaks mĂ€rkimisvÀÀrselt aega ja ressursse kokku hoidma, kuna ei pea ĂŒkshaaval kĂ”iki olemasolevaid hajusraamistikke tundma Ă”ppima ja rakendama.Scientific computing uses computers and algorithms to solve problems in various sciences such as genetics, biology and chemistry. Often the goal is to model and simulate different natural phenomena which would otherwise be very difficult to study in real environments. For example, it is possible to create a model of a solar storm or a meteor hit and run computer simulations to assess the impact of the disaster on the environment. The more sophisticated and accurate the simulations are the more computing power is required. It is often necessary to use a large number of computers, all working simultaneously on a single problem. These kind of computations are called parallel or distributed computing. However, creating distributed computing programs is complicated and requires a lot more time and resources, because it is necessary to synchronize different computers working at the same time. A number of software frameworks have been created to simplify this process by automating part of a distributed programming. The goal of this research was to assess the suitability of such distributed computing frameworks for complex scientific computing algorithms. The results showed that existing frameworks are very different from each other and none of them are suitable for all different types of algorithms. Some frameworks are only suitable for simple algorithms; others are not suitable when data does not fit into the computer memory. Choosing the most appropriate distributed computing framework for an algorithm can be a very complex task, because it requires studying and applying the existing frameworks. While searching for a solution to this problem, it was decided to create a Dynamic Algorithms Modelling Application (DAMA), which is able to simulate the implementation of the algorithm in different distributed computing frameworks. DAMA helps to estimate which distributed framework is the most appropriate for a given algorithm, without actually implementing it in any of the available frameworks. This main contribution of this study is simplifying the adoption of distributed computing frameworks for researchers who are not yet familiar with using them. It should save significant time and resources as it is not necessary to study each of the available distributed computing frameworks in detail

    Assessment, Design and Implementation of a Private Cloud for MapReduce Applications

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    [Abstract] Scientific computation and data intensive analyses are ever more frequent. On the one hand, the MapReduce programming model has gained a lot of attention for its applicability in large parallel data analyses and Big Data applications. On the other hand, Cloud computing seems to be increasingly attractive in solving these computing problems that demand a lot of resources. This paper explores the potential symbiosis between MapReduce and Cloud Computing, in order to create a robust and scalable environment to execute MapReduce workflows regardless of the underlaying infrastructure. The main goal of this work is to provide an easy-to-install interface, so as non-expert scientists can deploy a suitable testbed for their MapReduce experiments on local resources of their institution. Testing cases were performed in order to evaluate the required time for the whole executing process on a real cluster

    On Evaluating Commercial Cloud Services: A Systematic Review

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    Background: Cloud Computing is increasingly booming in industry with many competing providers and services. Accordingly, evaluation of commercial Cloud services is necessary. However, the existing evaluation studies are relatively chaotic. There exists tremendous confusion and gap between practices and theory about Cloud services evaluation. Aim: To facilitate relieving the aforementioned chaos, this work aims to synthesize the existing evaluation implementations to outline the state-of-the-practice and also identify research opportunities in Cloud services evaluation. Method: Based on a conceptual evaluation model comprising six steps, the Systematic Literature Review (SLR) method was employed to collect relevant evidence to investigate the Cloud services evaluation step by step. Results: This SLR identified 82 relevant evaluation studies. The overall data collected from these studies essentially represent the current practical landscape of implementing Cloud services evaluation, and in turn can be reused to facilitate future evaluation work. Conclusions: Evaluation of commercial Cloud services has become a world-wide research topic. Some of the findings of this SLR identify several research gaps in the area of Cloud services evaluation (e.g., the Elasticity and Security evaluation of commercial Cloud services could be a long-term challenge), while some other findings suggest the trend of applying commercial Cloud services (e.g., compared with PaaS, IaaS seems more suitable for customers and is particularly important in industry). This SLR study itself also confirms some previous experiences and reveals new Evidence-Based Software Engineering (EBSE) lessons

    Assessment, Design and Implementation of a Private Cloud for MapReduce Applications

    Get PDF
    Scientific computation and data intensive analyses are ever more frequent. On the one hand, the MapReduce programming model has gained a lot of attention for its applicability in large parallel data analyses and Big Data applications. On the other hand, Cloud computing seems to be increasingly attractive in solving these computing problems that demand a lot of resources. This paper explores the potential symbiosis between MapReduce and Cloud Computing, in order to create a robust and scalable environment to execute MapReduce workflows regardless of the underlaying infrastructure. The main goal of this work is to provide an easy-to-install interface, so as non-expert scientists can deploy a suitable testbed for their MapReduce experiments on local resources of their institution. Testing cases were performed in order to evaluate the required time for the whole executing process on a real clusterS

    Resource provisioning in Science Clouds: Requirements and challenges

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    Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications
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