High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape

Towards an MPI-like Framework for Azure Cloud Platform

Message passing interface (MPI) has been widely used for implementing parallel and distributed applications. The emergence of cloud computing offers a scalable, fault-tolerant, on-demand al-ternative to traditional on-premise clusters. In this thesis, we investigate the possibility of adopt-ing the cloud platform as an alternative to conventional MPI-based solutions. We show that cloud platform can exhibit competitive performance and benefit the users of this platform with its fault-tolerant architecture and on-demand access for a robust solution. Extensive research is done to identify the difficulties of designing and implementing an MPI-like framework for Azure cloud platform. We present the details of the key components required for implementing such a framework along with our experimental results for benchmarking multiple basic operations of MPI standard implemented in the cloud and its practical application in solving well-known large-scale algorithmic problems

Scientific High Performance Computing (HPC) Applications On The Azure Cloud Platform

Cloud computing is emerging as a promising platform for compute and data intensive scientific applications. Thanks to the on-demand elastic provisioning capabilities, cloud computing has instigated curiosity among researchers from a wide range of disciplines. However, even though many vendors have rolled out their commercial cloud infrastructures, the service offerings are usually only best-effort based without any performance guarantees. Utilization of these resources will be questionable if it can not meet the performance expectations of deployed applications. Additionally, the lack of the familiar development tools hamper the productivity of eScience developers to write robust scientific high performance computing (HPC) applications. There are no standard frameworks that are currently supported by any large set of vendors offering cloud computing services. Consequently, the application portability among different cloud platforms for scientific applications is hard. Among all clouds, the emerging Azure cloud from Microsoft in particular remains a challenge for HPC program development both due to lack of its support for traditional parallel programming support such as Message Passing Interface (MPI) and map-reduce and due to its evolving application programming interfaces (APIs). We have designed newer frameworks and runtime environments to help HPC application developers by providing them with easy to use tools similar to those known from traditional parallel and distributed computing environment set- ting, such as MPI, for scientific application development on the Azure cloud platform. It is challenging to create an efficient framework for any cloud platform, including the Windows Azure platform, as they are mostly offered to users as a black-box with a set of application programming interfaces (APIs) to access various service components. The primary contributions of this Ph.D. thesis are (i) creating a generic framework for bag-of-tasks HPC applications to serve as the basic building block for application development on the Azure cloud platform, (ii) creating a set of APIs for HPC application development over the Azure cloud platform, which is similar to message passing interface (MPI) from traditional parallel and distributed setting, and (iii) implementing Crayons using the proposed APIs as the first end-to-end parallel scientific application to parallelize the fundamental GIS operations

On Evaluating Commercial Cloud Services: A Systematic Review

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

On a Catalogue of Metrics for Evaluating Commercial Cloud Services

Given the continually increasing amount of commercial Cloud services in the market, evaluation of different services plays a significant role in cost-benefit analysis or decision making for choosing Cloud Computing. In particular, employing suitable metrics is essential in evaluation implementations. However, to the best of our knowledge, there is not any systematic discussion about metrics for evaluating Cloud services. By using the method of Systematic Literature Review (SLR), we have collected the de facto metrics adopted in the existing Cloud services evaluation work. The collected metrics were arranged following different Cloud service features to be evaluated, which essentially constructed an evaluation metrics catalogue, as shown in this paper. This metrics catalogue can be used to facilitate the future practice and research in the area of Cloud services evaluation. Moreover, considering metrics selection is a prerequisite of benchmark selection in evaluation implementations, this work also supplements the existing research in benchmarking the commercial Cloud services.Comment: 10 pages, Proceedings of the 13th ACM/IEEE International Conference on Grid Computing (Grid 2012), pp. 164-173, Beijing, China, September 20-23, 201

Висока продуктивність java-сокетів для оперування накопиченими даними в медицині

Computer clouds are using in health science for its data collections, manipulations and providing security needs in communications to exchange. The clouds distribution data character is using in science applications created to evaluate the data of the health-care. The science programs like medical visualization, genetic and protein conclusions, map-drag therapy and clinical decisions systems of support (CDSS) require high performance messaging libraries with minimum computer and communication spends and the effective utilization of the resources. The highperformance Java sockets (HPJS) encapsulate the needs of message high communications between cloud platforms science applications. HPJS effectively uses the Java socket realization for high-performance inner-process communications. With single-copy protocol, re-usability of the thread and communication overhead reduction, HPJS can use the message exchange in two times quickly to conventional buffered communication libraries.Компьютерные накопления данных используются в здравохранении для сохранения данных отдельных личностей, их манипуляции и обеспечения необходимости безопасного обмена. Характер распределения таких накоплений данных может быть разработан для использования в научных приложениях, которые разработаны для формирования оценки данных здравохранения. Такие научные программы як медицинская визуализация, генетические и протеиновые заключения, лечебно-профилактическая терапия та клинические системы поддержки принятия решений (CDSS) требуют библиотек скоростного обмена сообщениями с минимальными компьютерными и коммуникационными рас ходами, а также эффективным разграничением ресурсов. Высокопродуктивные Java-сокеты (HPJS) инкапсулируют необходимость высокопродуктивного обмена сообщениями между научными приложениями для cloud-платформ та эффективно используют Java-сокетную реализацию для образования высокоэффективной связи между процессами. С единой копией протокола и повторном использовании ниток та уменьшении накладных расходов связи высокопродуктивные Java-сокеты могут исполнять обмен сообщениями в два раза быстрее с обыкновенными буферизированными библиотекамисвязи.Комп’ютерні нагромадження даних використовуються в області охорони здоров’я для зберігання даних осіб, їх маніпуляції і забезпечення потреб безпечного обміну. Характер розподілу подібних нагромаджень даних може бути розроблений для застосування в наукових додатках, які розроблені для формування оцінки даних охорони здоров’я. Такі наукові програми як медична візуалізація, генетичні і протеїнові заключення, лікувально-профілактична терапія та клінічні системи підтримки прийняття рішень (CDSS) вимагають бібліотек швидкого обміну повідомленнями з мінімальними комп’ютерними і комунікаційними затратами та ефективним розшаруванням ресурсів. Високопродуктивні Java-сокети (HPJS) інкапсулюють потреби високопродуктивного обміну повідомленнями між науковими додатками для cloud-платформ та ефективно використовують Java-сокетну реалізацію для утворення високоефективного зв’язку між процесами. З єдиною копією протоколу при повторному використанні ниток та зменшенні накладних витрат зв’язку високопродуктивні Java-сокети можуть виконувати обмін повідомленнями в два рази швидше із звичайними буферизованими бібліотеками зв’язку