3,983 research outputs found
Evaluating Streaming Strategies for Event Processing across Infrastructure Clouds
Abstract-Infrastructure clouds revolutionized the way in which we approach resource procurement by providing an easy way to lease compute and storage resources on short notice, for a short amount of time, and on a pay-as-you-go basis. This new opportunity, however, introduces new performance trade-offs. Making the right choices in leveraging different types of storage available in the cloud is particularly important for applications that depend on managing large amounts of data within and across clouds. An increasing number of such applications conform to a pattern in which data processing relies on streaming the data to a compute platform where a set of similar operations is repeatedly applied to independent chunks of data. This pattern is evident in virtual observatories such as the Ocean Observatory Initiative, in cases when new data is evaluated against existing features in geospatial computations or when experimental data is processed as a series of time events. In this paper, we propose two strategies for efficiently implementing such streaming in the cloud and evaluate them in the context of an ATLAS application processing experimental data. Our results show that choosing the right cloud configuration can improve overall application performance by as much as three times
Evaluating Streaming Strategies for Event Processing across Infrastructure Clouds
International audienceInfrastructure clouds revolutionized the way in which we approach resource procurement by providing an easy way to lease compute and storage resources on short notice, for a short amount of time, and on a pay-as-you-go basis. This new opportunity, however, introduces new performance trade-offs. Making the right choices in leveraging different types of storage available in the cloud is particularly important for applications that depend on managing large amounts of data within and across clouds. An increasing number of such applications conformto a pattern in which data processing relies on streaming the data to a compute platform where a set of similar operations is repeatedly applied to independent chunks of data. This pattern is evident in virtual observatories such as the Ocean Observatory Initiative, in cases when new data is evaluated against existing features in geospatial computations or when experimental data is processed as a series of time events. In this paper, we propose two strategies for efficiently implementing such streaming in the cloud and evaluate them in the contextof an ATLAS application processing experimental data. Our results show that choosing the right cloud configuration can improve overall application performance by as much as three times
Mobile Service Clouds: A self-managing infrastructure for autonomic mobile computing services
Abstract. We recently introduced Service Clouds, a distributed infrastructure designed to facilitate rapid prototyping and deployment of autonomic communication services. In this paper, we propose a model that extends Service Clouds to the wireless edge of the Internet. This model, called Mobile Service Clouds, enables dynamic instantiation, composition, configuration, and reconfiguration of services on an overlay network to support mobile computing. We have implemented a prototype of this model and applied it to the problem of dynamically instantiating and migrating proxy services for mobile hosts. We conducted a case study involving data streaming across a combination of PlanetLab nodes, local proxies, and wireless hosts. Results are presented demonstrating the effectiveness of the prototype in establishing new proxies and migrating their functionality in response to node failures.
SMART: An Application Framework for Real Time Big Data Analysis on Heterogeneous Cloud Environments
International audienceThe amount of data that human activities generate poses a challenge to current computer systems. Big data processing techniques are evolving to address this challenge, with analysis increasingly being performed using cloud-based systems. Emerging services, however, require additional enhancements in order to ensure their applicability to highly dynamic and heterogeneous environments and facilitate their use by Small & Medium-sized Enterprises (SMEs). Observing this landscape in emerging computing system development, this work presents Small & Medium-sized Enterprise Data Analytic in Real Time (SMART) for addressing some of the issues in providing compute service solutions for SMEs. SMART offers a framework for efficient development of Big Data analysis services suitable to small and medium-sized organizations, considering very heterogeneous data sources, from wireless sensor networks to data warehouses, focusing on service composability for a number of domains. This paper presents the basis of this proposal and preliminary results on exploring application deployment on hybrid infrastructure
Models of everywhere revisited: a technological perspective
The concept âmodels of everywhereâ was first introduced in the mid 2000s as a means of reasoning about the
environmental science of a place, changing the nature of the underlying modelling process, from one in which
general model structures are used to one in which modelling becomes a learning process about specific places, in
particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another
it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of everywhere,
models of everything and models at all times, being constantly re-evaluated against the most current
evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and nonlinearities.
However, the approach has, as yet, not been fully utilised or explored. This paper examines the
concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first
proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud
computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again
at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the
remaining research questions. The paper concludes by identifying the key elements of a research agenda that
should underpin such experimentation and deployment
HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation
Historically, high energy physics computing has been performed on large
purpose-built computing systems. These began as single-site compute facilities,
but have evolved into the distributed computing grids used today. Recently,
there has been an exponential increase in the capacity and capability of
commercial clouds. Cloud resources are highly virtualized and intended to be
able to be flexibly deployed for a variety of computing tasks. There is a
growing nterest among the cloud providers to demonstrate the capability to
perform large-scale scientific computing. In this paper, we discuss results
from the CMS experiment using the Fermilab HEPCloud facility, which utilized
both local Fermilab resources and virtual machines in the Amazon Web Services
Elastic Compute Cloud. We discuss the planning, technical challenges, and
lessons learned involved in performing physics workflows on a large-scale set
of virtualized resources. In addition, we will discuss the economics and
operational efficiencies when executing workflows both in the cloud and on
dedicated resources.Comment: 15 pages, 9 figure
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