Bruno Schulze

Using clouds to address grid limitations

This article is a review of the importance of identifying the conceptual components participating in the design of Grid infrastructure, the interfaces presented to other elements and the semantics involved. We will show that the middleware layer still exposes too much detail of the underlying implementation, thus making the application development more complex, difficulting interoperability and scaling. We also discuss Cloud Computing, an emerging technology that has been so far successful in the IT market, also show how Grids and Clouds are related, and to what extent these technologies may provide features that will help accomplish the Grid vision for e-Science applications.

An Analysis of Public Clouds Elasticity in the Execution of Scientific Applications: a Survey

Elasticity can be seen as the ability of a system to increase or decrease the computing resources allocated in a dynamic and on demand way. It is an important feature provided by cloud computing, that has been widely used in web applications and is also gaining attention in the scientific community. Considering the possibilities of using elasticity in this context, a question arises: “Are the available public cloud solutions suitable to provide elasticity to scientific applications?” To answer the question, in a first moment we present a survey on the use of cloud computing in scientific scenarios, providing an overview of the subject. Next, we describe the elasticity mechanisms offered by major public cloud providers and analyzes the limitations of the solutions in providing elasticity for scientific applications. As the main contribution of the article, we also present an analysis over some initiatives that are being developed to overcome the current challenges. In our opinion, current computational clouds are developing rapidly but have not yet reached the necessary maturity level to meet all scientific applications elasticity requirements. We expect that in the coming years the efforts being taken by numerous researchers in this area identify and address these challenges and lead to better and more mature technologies that will improve cloud computing practices.

An adaptive distributed query processing grid service

Grid services provide an important abstract layer on top of heterogeneous components (hardware and software) that take part into a grid environment. We are developing a data grid service prototype that aims at providing transparent use of grid resources to data intensive scientific applications. Our prototype was designed having as target three main issues: (1) dynamic scheduling and allocation of query execution engine modules into grid nodes; (2)adaptability of query execution to variations on environment conditions and (3) support to special scientific operations. We propose a new node scheduling algorithm and show how it can be integrated into a simple distributed and parallel query optimization strategy. Our implementation demonstrates a speedup of 16.6 with 18 scheduled nodes and a steady throughput rate, obtained applying a dynamic adaptive strategy.

QEF - Supporting Complex Query Applications

This paper describes QEF a query evaluation framework designed to support complex applications on the grid. QEF has been extended to support querying within a number of different applications, including supporting scientific visualization and implementing a web service semantic search engine. Application requests take a form of a workflow in which tasks are represented as algebraic operators and specific data types are enveloped into a common tuple structure. The implemented system is automatically deployed into schedule grid nodes and autonomously manages query evaluation according to grid environment conditions. The generality of our approach has been tested with a number of applications leading to a full grid web service implementation available at http://codims. epfl. ch.

Performance and deployment evaluation of a parallel application on a private Cloud

In this paper we present the case study of an application of a parallel simulation optimization deployed on a private Cloud. The compute‐intensive application uses a Master/Worker model, supporting communication over both Java RMI and Globus Grid Services between the nodes. The Master deploys Workers over a Eucalyptus Cloud using the Nimbus Context Broker for just‐in‐time configuration and runtime Worker discovery. The computational performance of the Workers under different communication mechanisms and deployment scenarios is presented in an attempt to evaluate the use of Virtual Machines in a Cloud as a tool to achieve application scaling. The deployment of this particular application was crafted to support on‐the‐fly addition of working nodes. The case study suggests a deployment pattern that shapes some requirements and considerations of a scalable Globus‐driven Platform as a Service Cloud. Copyright

An adaptive parallel query processing middleware for the Grid

Grid services provide an important abstract layer on top of heterogeneous components (hardware and software) that take part in a Grid environment. In this scenario, applications such as scientific visualization require access to data of non‐conventional data types, such as fluid path geometry, and the evaluation of special user programs and algebraic operators, such as spatial hash‐join, on these data. In order to support such applications we are developing a Configurable Data Integration Middleware System for the Grid (CoDIMS‐G). CoDIMS‐G provides a query execution environment adapted to the heterogeneity and variations found in a Grid environment by offering a node scheduling algorithm and an adaptive query execution strategy. The latter both adapts to performance variations in a scheduled node and deals efficiently with repetitive evaluation of a query execution plan fragment, as needed for computing a particles, trajectory. Copyright

CoDIMS-G: a data and program integration service for the grid

Grid services provide an important abstract layer on top of heterogeneous components (hardware and software) that take part into a Grid environment. In this scenario, applications, like scientific visualization, require access to data of non-conventional data types, like fluid path geometry, and the evaluation of special user programs on these data. In order to support such applications we are developing CoDIMS-G, which is a data and program integration service for the Grid. CoDIMS-G provides users transparent access to data and programs distributed on the Grid, as well as dynamic resource allocation and management. We conceived a new node scheduling algorithm and designed an adaptive distributed query engine for the grid environment.

Automatic generation of platforms in cloud computing

Cloud computing has been established in recent years as an important area of research. This reality has been consolidated because, currently, tasks such as obtainment, manipulation, sharing and exploitation of large amounts of data are common and require many computational resources, so cloud computing can contribute because it can provide this resources indefinitely, including processing, memory, storage and others, all them for immediate use. Cloud computing has many challenges, among them, the specification of work platforms in a cloud environment. In this context, the use of models can help insofar as they may contain several types of information that can be handled with the intention of automating the process of creation of an environment. A template can contain information about the user, hardware and software that can be used by computer systems to automatically build the necessary infrastructure for the operation of a virtual machine on a cloud computing environment. In addition, models can be easily exported to other formats used by cloud services providers. The use of concepts of Service Level Agreements - SLA to control the utilization of computational resources of a provider is common in computing environments and can be used to ensure the quality of services that being offered. The objective of this work is to combine these paradigms, cloud, models and SLA, in order to use the resources of a computational cloud to provide automatically a platform as a service - PaaS. This project will be developed in a web environment, using a model-driven approach to create the platforms and the management of resources and quality will be made by SLA.

Understanding scheduling implications for scientific applications in clouds

This paper explores some of the effects that the paradigm of Cloud Computing has on schedulers when executing scientific applications. We present premises regarding to provisioning and architectural aspects of a Cloud infrastructure, that are not present in other environments, and which implications they may have on scheduling decisions in presence of relevant policies like improving performance. We also argue that using virtualization as a mechanism for workload consolidation in a multi-core environment has important performance consequences for e-science. We propose and test a preliminary workload classification, based on usage modes, that may improve early scheduling decisions as we research towards automatic deployment of scientific applications.

Heterogeneous Strong Computation Migration

The emergence of new environments, such as grids and mobile devices, poses new requirements for programming systems and models. Migration techniques, which have been extensively studied but not widely used, gain a chance to rise in light of these requirements. This survey reviews a particular case of migration that fits well into those environments—that is, heterogeneous strong migration of running computations at application level. We approach it from an implementation point of view, commenting on related issues and current solutions. We discuss the strong influence that language support for migration has on implementation issues, and the advantages that a higher level of support would represent for the portability and performance of the application, as well as in terms of control and flexibility for the programmer. Copyright