End-to-End QoS Support for a Medical Grid Service Infrastructure

Quality of Service support is an important prerequisite for the adoption of Grid technologies for medical applications. The GEMSS Grid infrastructure addressed this issue by offering end-to-end QoS in the form of explicit timeliness guarantees for compute-intensive medical simulation services. Within GEMSS, parallel applications installed on clusters or other HPC hardware may be exposed as QoS-aware Grid services for which clients may dynamically negotiate QoS constraints with respect to response time and price using Service Level Agreements. The GEMSS infrastructure and middleware is based on standard Web services technology and relies on a reservation based approach to QoS coupled with application specific performance models. In this paper we present an overview of the GEMSS infrastructure, describe the available QoS and security mechanisms, and demonstrate the effectiveness of our methods with a Grid-enabled medical imaging service

The Locus Algorithm III: A Grid Computing system to generate catalogues of optimised pointings for Differential Photometry

This paper discusses the hardware and software components of the Grid Computing system used to implement the Locus Algorithm to identify optimum pointings for differential photometry of 61,662,376 stars and 23,799 quasars. The scale of the data, together with initial operational assessments demanded a High Performance Computing (HPC) system to complete the data analysis. Grid computing was chosen as the HPC solution as the optimum choice available within this project. The physical and logical structure of the National Grid computing Infrastructure informed the approach that was taken. That approach was one of layered separation of the different project components to enable maximum flexibility and extensibility

Montage: a grid portal and software toolkit for science-grade astronomical image mosaicking

Montage is a portable software toolkit for constructing custom, science-grade mosaics by composing multiple astronomical images. The mosaics constructed by Montage preserve the astrometry (position) and photometry (intensity) of the sources in the input images. The mosaic to be constructed is specified by the user in terms of a set of parameters, including dataset and wavelength to be used, location and size on the sky, coordinate system and projection, and spatial sampling rate. Many astronomical datasets are massive, and are stored in distributed archives that are, in most cases, remote with respect to the available computational resources. Montage can be run on both single- and multi-processor computers, including clusters and grids. Standard grid tools are used to run Montage in the case where the data or computers used to construct a mosaic are located remotely on the Internet. This paper describes the architecture, algorithms, and usage of Montage as both a software toolkit and as a grid portal. Timing results are provided to show how Montage performance scales with number of processors on a cluster computer. In addition, we compare the performance of two methods of running Montage in parallel on a grid.Comment: 16 pages, 11 figure

Optimization grid scheduling with priority base and bees algorithm

Grid computing depends upon sharing large-scales in a network that is widely connected within itself such as the Internet. Therefore, many grid computing researchers and scholars have focused on task scheduling, which is considered one of the NP-Complete issues. The main aim of this current research to propose an optimization of the initial scheduler for grid computing using the bees algorithm. Modern algorithms informed this research. The suggested procedure means that a newly developed algorithm can implement the schedule grid task while accounting for priorities and deadlines to decrease the completion time required for the tasks. The average waiting time of the grid environment can be minimized, and this minimization, in turn, creates an increase in the throughput of the environment

Communication network analysis of the enterprise grid systems

This paper addresses the problem of performance analysis based on communication modelling of largescale heterogeneous distributed systems with emphases on enterprise grid computing systems. The study of communication layers is important because the overall performance of a distributed system is often critically hinged on the effectiveness of this part. This model considers processor as well as network heterogeneity of target system. The model is validated through comprehensive simulation, which demonstrates that the proposed model exhibits a good degree of accuracy for various system sizes and under different working conditions. The proposed model is then used to investigate the performance analysis of typical systems.<br /

Analysis of interconnection networks in heterogeneous multi-cluster systems

The study of interconnection networks is important because the overall performance of a distributed system is often critically hinged on the effectiveness of its interconnection network. In the mean time, the heterogeneity is one of the most important factors of such systems. This paper addresses the problem of interconnection networks performance modeling of large-scale distributed systems with emphases on heterogeneous multi-cluster computing systems. So, we present an analytical model to predict message latency in multi-cluster systems in the presence of cluster size heterogeneity. The model is validated through comprehensive simulation, which demonstrates that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.<br /

eXCloud: Transparent runtime support for scaling mobile applications in cloud

Cloud computing augments applications with ease-of-access to the enormous resources on the Internet. Combined with mobile computing technologies, mobile applications can exploit the Cloud everywhere by statically distributing code segments or dynamically migrating running processes onto cloud services. Existing migration techniques are however too coarse-grained for mobile devices, so the overheads often offset the benefits of migration. To build a truly elastic mobile cloud computing infrastructure, we introduce eXCloud (eXtensible Cloud) - a middleware system with multi-level mobility support, ranging from as coarse as a VM instance to as fine as a runtime stack frame, and allows resources to be integrated and used dynamically. In eXCloud, a stack-on-demand (SOD) approach is used to support computation mobility throughout the mobile cloud environment. The approach is fully adaptive, goal-driven and transparent. By downward task migration, applications running on the cloud nodes can exploit or take control of special resources in mobile devices such as GPS and cameras. With a restorable MPI layer, task migrations of MPI parallel programs can happen between cloud nodes or be initiated from a mobile device. Our evaluation shows that SOD outperforms several existing migration mechanisms in terms of migration overhead and latency. All our techniques result in better resource utilization through task migrations among cloud nodes and mobile nodes.published_or_final_versionThe 2011 International Conference on Cloud and Service Computing (CSC), Hong Kong, China, 12-14 December 2011. In Proceedings of CSC, 2011, p. 103-11

Comparative Analyses of De Novo Transcriptome Assembly Pipelines for Diploid Wheat

Gene expression and transcriptome analysis are currently one of the main focuses of research for a great number of scientists. However, the assembly of raw sequence data to obtain a draft transcriptome of an organism is a complex multi-stage process usually composed of pre-processing, assembling, and post-processing. Each of these stages includes multiple steps such as data cleaning, error correction and assembly validation. Different combinations of steps, as well as different computational methods for the same step, generate transcriptome assemblies with different accuracy. Thus, using a combination that generates more accurate assemblies is crucial for any novel biological discoveries. Implementing accurate transcriptome assembly requires a great knowledge of different algorithms, bioinformatics tools and software that can be used in an analysis pipeline. Many pipelines can be represented as automated scalable scientific workflows that can be run simultaneously on powerful distributed and computational resources, such as Campus Clusters, Grids, and Clouds, and speed-up the analyses. In this thesis, we 1) compared and optimized de novo transcriptome assembly pipelines for diploid wheat; 2) investigated the impact of a few key parameters for generating accurate transcriptome assemblies, such as digital normalization and error correction methods, de novo assemblers and k-mer length strategies; 3) built distributed and scalable scientific workflow for blast2cap3, a step from the transcriptome assembly pipeline for protein-guided assembly, using the Pegasus Workflow Management System (WMS); and 4) deployed and examined the scientific workflow for blast2cap3 on two different computational platforms. Based on the analysis performed in this thesis, we conclude that the best transcriptome assembly is produced when the error correction method is used with Velvet Oases and the “multi-k” strategy. Moreover, the performed experiments show that the Pegasus WMS implementation of blast2cap3 reduces the running time for more than 95% compared to its current serial implementation. The results presented in this thesis provide valuable insight for designing good de novo transcriptome assembly pipeline and show the importance of using scientific workflows for executing computationally demanding pipelines. Advisor: Jitender S. Deogu