Experimental Study of Remote Job Submission and Execution on LRM through Grid Computing Mechanisms

Remote job submission and execution is fundamental requirement of distributed computing done using Cluster computing. However, Cluster computing limits usage within a single organization. Grid computing environment can allow use of resources for remote job execution that are available in other organizations. This paper discusses concepts of batch-job execution using LRM and using Grid. The paper discusses two ways of preparing test Grid computing environment that we use for experimental testing of concepts. This paper presents experimental testing of remote job submission and execution mechanisms through LRM specific way and Grid computing ways. Moreover, the paper also discusses various problems faced while working with Grid computing environment and discusses their trouble-shootings. The understanding and experimental testing presented in this paper would become very useful to researchers who are new to the field of job management in Grid.Comment: Fourth International Conference on Advanced Computing & Communication Technologies (ACCT), 201

Enhancing Job Scheduling of an Atmospheric Intensive Data Application

Nowadays, e-Science applications involve great deal of data to have more accurate analysis. One of its application domains is the Radio Occultation which manages satellite data. Grid Processing Management is a physical infrastructure geographically distributed based on Grid Computing, that is implemented for the overall processing Radio Occultation analysis. After a brief description of algorithms adopted to characterize atmospheric profiles, the paper presents an improvement of job scheduling in order to decrease processing time and optimize resource utilization. Extension of grid computing capacity is implemented by virtual machines in existing physical Grid in order to satisfy temporary job requests. Also scheduling plays an important role in the infrastructure that is handled by a couple of schedulers which are developed to manage data automaticall

Optimizing Splicing Junction Detection in Next Generation Sequencing Data on a Virtual-GRID Infrastructure

The new protocol for sequencing the messenger RNA in a cell, named RNA-seq produce millions of short sequence fragments. Next Generation Sequencing technology allows more accurate analysis but increase needs in term of computational resources. This paper describes the optimization of a RNA-seq analysis pipeline devoted to splicing variants detection, aimed at reducing computation time and providing a multi-user/multisample environment. This work brings two main contributions. First, we optimized a well-known algorithm called TopHat by parallelizing some sequential mapping steps. Second, we designed and implemented a hybrid virtual GRID infrastructure allowing to efficiently execute multiple instances of TopHat running on different samples or on behalf of different users, thus optimizing the overall execution time and enabling a flexible multi-user environmen

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

A DevOps approach to integration of software components in an EU research project

We present a description of the development and deployment infrastructure being created to support the integration effort of HARNESS, an EU FP7 project. HARNESS is a multi-partner research project intended to bring the power of heterogeneous resources to the cloud. It consists of a number of different services and technologies that interact with the OpenStack cloud computing platform at various levels. Many of these components are being developed independently by different teams at different locations across Europe, and keeping the work fully integrated is a challenge. We use a combination of Vagrant based virtual machines, Docker containers, and Ansible playbooks to provide a consistent and up-to-date environment to each developer. The same playbooks used to configure local virtual machines are also used to manage a static testbed with heterogeneous compute and storage devices, and to automate ephemeral larger-scale deployments to Grid5000. Access to internal projects is managed by GitLab, and automated testing of services within Docker-based environments and integrated deployments within virtual-machines is provided by Buildbot

Survey and Analysis of Production Distributed Computing Infrastructures

This report has two objectives. First, we describe a set of the production distributed infrastructures currently available, so that the reader has a basic understanding of them. This includes explaining why each infrastructure was created and made available and how it has succeeded and failed. The set is not complete, but we believe it is representative. Second, we describe the infrastructures in terms of their use, which is a combination of how they were designed to be used and how users have found ways to use them. Applications are often designed and created with specific infrastructures in mind, with both an appreciation of the existing capabilities provided by those infrastructures and an anticipation of their future capabilities. Here, the infrastructures we discuss were often designed and created with specific applications in mind, or at least specific types of applications. The reader should understand how the interplay between the infrastructure providers and the users leads to such usages, which we call usage modalities. These usage modalities are really abstractions that exist between the infrastructures and the applications; they influence the infrastructures by representing the applications, and they influence the ap- plications by representing the infrastructures

A grid-based approach for processing group activity log files

The information collected regarding group activity in a collaborative learning environment requires classifying, structuring and processing. The aim is to process this information in order to extract, reveal and provide students and tutors with valuable knowledge, awareness and feedback in order to successfully perform the collaborative learning activity. However, the large amount of information generated during online group activity may be time-consuming to process and, hence, can hinder the real-time delivery of the information. In this study we show how a Grid-based paradigm can be used to effectively process and present the information regarding group activity gathered in the log files under a collaborative environment. The computational power of the Grid makes it possible to process a huge amount of event information, compute statistical results and present them, when needed, to the members of the online group and the tutors, who are geographically distributed.Peer ReviewedPostprint (author's final draft