Spiros Koulouzis

Distributed Computing on an Ensemble of Browsers

In this article, the authors propose a new approach to distributed computing with Web browsers and introduce the WeevilScout prototype framework. The proliferation of Web browsers and the performance gains being achieved by current JavaScript virtual machines raises the question whether Internet browsers can become yet another middleware for distributed computing. With 2 billion users online, computing through Internet browsers has the potential to amass immense resources, thus transforming the Internet into a distributed computer ideal for common classes of distributed scientific applications such as parametric studies. As a proof of concept, the authors demonstrate how a cluster of globally distributed Internet browsers is used to compute thousands of bio-informatics tasks.

Enabling Web Services to Consume and Produce Large Datasets

Service-oriented architectures and Web services are well-established paradigms for developing distributed applications. However, Web services face problems when accessing, moving, and processing large datasets. To address this problem, the authors present ProxyWS, which uses myriad protocols to transport large amounts of data. ProxyWS undertakes data transfers on behalf of legacy Web services and can serve as an interface for developing new Web services that can stream data. Experiments show how this approach facilitates scalable data transports for two data-intensive applications.

Applying workflow as a service paradigm to application farming

Task farming is often used to enable parameter sweep for exploration of large sets of initial conditions for large scale complex simulations. Such applications occur very often in life sciences. Available solutions enable to perform parameter sweep by creating multiple job submissions with different parameters. This paper presents an approach to farm workflows, employing service oriented paradigms using the WS‐VLAM workflow manager, which provides ways to create, control, and monitor workflow applications and their components. We present two service‐oriented approaches for workflow farming: task level, whereby task harness acts as services by being invoked on which task to load, and data level, where the actual task is invoked as a service with different chunks of data to process. An experimental evaluation of the presented solution is performed with a biomedical application for which 3000 simulations were required to perform a Monte Carlo study. Copyright

Cloud Data Federation for Scientific Applications

Nowadays, data-intensive scientific research needs storage capabilities that enable efficient data sharing. This is of great importance for many scientific domains such as the Virtual Physiological Human. In this paper, we introduce a solution that federates a variety of systems ranging from file servers to more sophisticated ones used in clouds or grids. Our solution follows a client-centric approach that loosely couples a variety of data resources that may use different technologies such as Openstack-Swift, iRODS, GridFTP, and may be geographically distributed. It is implemented as a lightweight service which does not require installation of a software on the resources it uses. In this way we are able to efficiently use heterogeneous storage resources, reduce the usage complexity of multiple storage resources, and avoid vendor lock-in in case of cloud storage. To demonstrate the usability of our approach we performed a number of experiments that assess the performance and functionality of the developed system.

Automatic Collector for Dynamic Cloud Performance Information

When deploying an application in the cloud, a developer often wants to know which of the wide variety of cloud resources is best to use. Most cloud providers only provide static information about different cloud resources which is often not enough because static information does not take into account the hardware and software that is being used or the policy that has been applied by the cloud provider. Therefore, dynamic benchmarking of cloud resources is needed to find out how a certain workload is going to behave on a certain instance. However, benchmarking various cloud resources is a time consuming process. Thus, using a tool which automatically benchmarks various cloud resources will be of great use. % To maximize the effectiveness of such a tool, it will be helpful to maintain an up to date cloud information catalogue, so that users can share and compare their benchmark results to the results of other users. In this paper, we present the Cloud Performance Collector, a modular cloud benchmarking tool aimed to automatically benchmark a wide variety of applications. To demonstrate the benefit of the tool, we did three experiments with three synthetic benchmark applications and one real-world application using the ExoGENI testbed.

Service level management for executable papers

Reproducibility of Science is considered as one of the main principles of the scientific method, and refers to the ability of an experiment to be accurately reproduced, by third person, in complex experiment every detail matters to ensure the correct reproducibility. In the context of the ICCS 2011, Elsevier organized the executable paper grand challenge a contest to improve the way scientific information is communicated and used. While during this contest the focus was on developing methods and technique to realize the idea of executable papers, in this paper we focus on the operational issues related to the creation a viable service with a predefined QoS.

Dynamic Real-Time Infrastructure Planning and Deployment for Disaster Early Warning Systems

An effective nature disaster early warning system often relies on widely deployed sensors, simulation based predicting components, and a decision making system. In many cases, the simulation components require advanced infrastructures such as Cloud for performing the computing tasks. However, effectively customizing the virtualized infrastructure from Cloud based time critical constraints and locations of the sensors, and scaling it based on dynamic loads of the computation at runtime is still difficult. The suitability of a Dynamic Real-time Infrastructure Planner (DRIP) that handles the provisioning within cloud environments of the virtual infrastructure for time-critical applications is demonstrated with respect to disaster early warning systems. The DRIP system is part of the SWITCH project (Software Workbench for Interactive, Time Critical and Highly self-adaptive Cloud applications).