Resource provisioning in Science Clouds: Requirements and challenges

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

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

Challenges and complexities in application of LCA approaches in the case of ICT for a sustainable future

In this work, three of many ICT-specific challenges of LCA are discussed. First, the inconsistency versus uncertainty is reviewed with regard to the meta-technological nature of ICT. As an example, the semiconductor technologies are used to highlight the complexities especially with respect to energy and water consumption. The need for specific representations and metric to separately assess products and technologies is discussed. It is highlighted that applying product-oriented approaches would result in abandoning or disfavoring of new technologies that could otherwise help toward a better world. Second, several believed-untouchable hot spots are highlighted to emphasize on their importance and footprint. The list includes, but not limited to, i) User Computer-Interfaces (UCIs), especially screens and displays, ii) Network-Computer Interlaces (NCIs), such as electronic and optical ports, and iii) electricity power interfaces. In addition, considering cross-regional social and economic impacts, and also taking into account the marketing nature of the need for many ICT's product and services in both forms of hardware and software, the complexity of End of Life (EoL) stage of ICT products, technologies, and services is explored. Finally, the impact of smart management and intelligence, and in general software, in ICT solutions and products is highlighted. In particular, it is observed that, even using the same technology, the significance of software could be highly variable depending on the level of intelligence and awareness deployed. With examples from an interconnected network of data centers managed using Dynamic Voltage and Frequency Scaling (DVFS) technology and smart cooling systems, it is shown that the unadjusted assessments could be highly uncertain, and even inconsistent, in calculating the management component's significance on the ICT impacts.Comment: 10 pages. Preprint/Accepted of a paper submitted to the ICT4S Conferenc

Scientific Workflow Applications on Amazon EC2

The proliferation of commercial cloud computing providers has generated significant interest in the scientific computing community. Much recent research has attempted to determine the benefits and drawbacks of cloud computing for scientific applications. Although clouds have many attractive features, such as virtualization, on-demand provisioning, and "pay as you go" usage-based pricing, it is not clear whether they are able to deliver the performance required for scientific applications at a reasonable price. In this paper we examine the performance and cost of clouds from the perspective of scientific workflow applications. We use three characteristic workflows to compare the performance of a commercial cloud with that of a typical HPC system, and we analyze the various costs associated with running those workflows in the cloud. We find that the performance of clouds is not unreasonable given the hardware resources provided, and that performance comparable to HPC systems can be achieved given similar resources. We also find that the cost of running workflows on a commercial cloud can be reduced by storing data in the cloud rather than transferring it from outside

Infrastructure Specifications

This document presents the computing infrastructure deployed in the context of StratusLab project, details the configuration of the computing resources used and the commitments made from the project partners to contribute with computing resources in the project. During the first months of operation this infrastructure has already been significantly exploited to deliver the first results towards the project's goals. The OpenNebula virtual management software has been used to install private clouds on different OS platforms. Two pre-production grid sites have been deployed in the private cloud and are used to test the implications of providing grid services in cloud environments. Experience from the deployment and operation of the above sites will help us identify the required tools and procedures for offering grid production sites over computing clouds. Finally the document presents related work, relevant to infrastructure operations, taking place in other projects and initiatives

High availability using virtualization

High availability has always been one of the main problems for a data center. Till now high availability was achieved by host per host redundancy, a highly expensive method in terms of hardware and human costs. A new approach to the problem can be offered by virtualization. Using virtualization, it is possible to achieve a redundancy system for all the services running on a data center. This new approach to high availability allows to share the running virtual machines over the servers up and running, by exploiting the features of the virtualization layer: start, stop and move virtual machines between physical hosts. The system (3RC) is based on a finite state machine with hysteresis, providing the possibility to restart each virtual machine over any physical host, or reinstall it from scratch. A complete infrastructure has been developed to install operating system and middleware in a few minutes. To virtualize the main servers of a data center, a new procedure has been developed to migrate physical to virtual hosts. The whole Grid data center SNS-PISA is running at the moment in virtual environment under the high availability system. As extension of the 3RC architecture, several storage solutions have been tested to store and centralize all the virtual disks, from NAS to SAN, to grant data safety and access from everywhere. Exploiting virtualization and ability to automatically reinstall a host, we provide a sort of host on-demand, where the action on a virtual machine is performed only when a disaster occurs.Comment: PhD Thesis in Information Technology Engineering: Electronics, Computer Science, Telecommunications, pp. 94, University of Pisa [Italy

Automated tools and techniques for distributed Grid Software: Development of the testbed infrastructure

Grid technology is becoming more and more important as the new paradigm for sharing computational resources across different organizations in a secure way. The great powerfulness of this solution, requires the definition of a generic stack of services and protocols and this is the scope of the different Grid initiatives. As a result of international collaborations for its development, the Open Grid Forum created the Open Grid Services Architecture (OGSA) which aims to define the common set of services that will enable interoperability across the different implementations. This master thesis has been developed in this framework, as part of the two European-funded projects ETICS and OMII-Europe. The main objective is to contribute to the design and maintenance of large distributed development projects with the automated tool that enables to implement Software Engineering techniques oriented to achieve an acceptable level of quality at the release process. Specifically, this thesis develops the testbed concept as the virtual production-like scenario where to perform compliance tests. As proof of concept, the OGSA Basic Execution Service has been chosen in order to implement and execute conformance tests within the ETICS automated testbed framework