Juliusz Pukacki

Enabling Applications on the Grid: A Gridlab Overview

Grid technology is widely emerging. Still, there is an eminent shortage of real Grid users, mostly due to the lack of a “critical mass” of widely deployed and reliable higher-level Grid services, tailored to application needs. The GridLab project aims to provide fundamentally new capabilities for applications to exploit the power of Grid computing, thus bridging the gap between application needs and existing Grid middleware. We present an overview of GridLab, a large-scale, EU-funded Grid project spanning over a dozen groups in Europe and the US. We first outline our vision of Grid-empowered applications and then discuss GridLab’s general architecture and its Grid Application Toolkit (GAT). We illustrate how applications can be Grid-enabled with the GAT and discuss GridLab’s scheduler as an example of GAT services.

Dynamic grid scheduling with job migration and rescheduling in the GridLab resource management system

Grid computing has become one of the most important research topics that appeared in the field of computing in the last years. Simultaneously, we have noticed the growing popularity of new Web-based technologies which allow us to create application-oriented Grid middleware services providing capabilities required for dynamic resource and job management, monitoring, security, etc. Consequently, end users are able to get easier access to geographically distributed resources. In this paper we present the results of our experiments with the Grid(Lab) Resource Management System (GRMS), which acts on behalf of end users and controls their computations efficiently using distributed heterogeneous resources. We show how resource matching techniques used within GRMS can be improved by the use of a job migration based rescheduling policy. The main aim of this policy is to shorten job pending times and reduce machine overloads. The influence of this method on application performance and resource utilization is studied in detail and compared with two other simple policies.

GridLab: Enabling Applications on the Grid

Grid technology is widely emerging. Still, there is an eminent shortage of real Grid users, due to the absence of two important catalysts: First, a widely accepted vision on how applications can substantially benefit from Grids, and second a toolkit of higher-level Grid services, tailored to application needs. The GridLab project aims to provide fundamentally new capabilities for applications to exploit the power of Grid computing, thus bridging the gap between application needs and existing Grid middleware. We present an overview of GridLab, a largescale, EU-funded Grid project spanning over a dozen groups in Europe and the US. We first outline our vision of Grid-empowered applications and then discuss GridLabs general architecture.

Federated clouds for biomedical research: Integrating OpenStack for ICTBioMed

Increasingly complex biomedical data from diverse sources demands large storage, efficient software and high performance computing for the datas computationally intensive analysis. Cloud technology provides flexible storage and data processing capacity to aggregate and analyze complex data; facilitating knowledge sharing and integration from different disciplines in a collaborative research environment. The ICTBioMed collaborative is a team of internationally renowned academic and medical research institutions committed to advancing discovery in biomedicine. In this work we describe the cloud framework design, development, and associated software platform and tools we are working to develop, federate and deploy in a coordinated and evolving manner to accelerate research developments in the biomedical field. Further, we highlight some of the essential considerations and challenges to deploying a complex open architecture cloud-based research infrastructure with numerous software components, internationally distributed infrastructure and a diverse user base.

Abstract 4272: Applying TCGA data for breast cancer diagnostics and pathway analysis

The main objective of our project is to obtain a comprehensive insight into oncogenic signaling in order to develop novel diagnostic tools for molecular subtypes of breast cancer (BRCA). We have provided about 100 BRCA samples along with full pathology and clinical annotations to the TCGA program. These cancer samples have been extensively characterized using all available genomics platforms and the obtained molecular data contributed to the molecular characterization of BRCA. This integrated effort identified four major molecular subtypes of breast cancer that can be identified across five profiling platform including genomics, transcriptomics and proteomics (Nature, 490 (7418):61-70). Building upon these datasets we aim to translate these genomics profiles into diagnostic tools that can be used in every day medical practice. In the first step, we have generated tissue microarrays (TMAs) from FFPE BRCA samples obtained from the patients enrolled into the TCGA project. Next, the TMA slides were used for immunohistochemistry (IHC) using about 100 antibodies specific for major cancer markers, including oncogenic kinases frequently activated or overexpressed in BRCA. The obtained IHC results have been scored and verified independently for each marker by two board-certified pathologists. To identify markers that correlate with or are specific to the molecular subtypes of BRCA, the TMA/IHC read-outs from each tumor sample were correlated with all TCGA genomic data. In addition, we integrate the pathology and genomics results with clinical data obtained from the TCGA-enrolled patients, including up to 4 years follow-up. Finally, we apply system biology tools to integrate the genomic data from TCGA with the proteomic analysis to understand causalities between changes in DNA, transcriptome and signal transduction pathways. Our long-term goal is to identify novel diagnostic biomarkers that will precisely identify each molecular subtype of BRCA and which may be predictive of patient response to therapy thus paving the way for novel personalized therapies for cancer. Citation Format: Jannik Andersen, Parantu Shah, Konstanty Korski, Matthew Ibbs, Violetta Filas, Michal Kosiedowski, Juliusz Pukacki, Cezary Mazurek, Yuanqing Wu, Edward Chang, Carlo Toniatti, Giulio Draetta, Maciej Wiznerowicz. Applying TCGA data for breast cancer diagnostics and pathway analysis. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4272. doi:10.1158/1538-7445.AM2014-4272

Semantic Data Sharing and Presentation in Integrated Knowledge System

A rapid development of semantic technologies and a growing awareness of a potential of semantic data has a great influence and effects on an increasing number of semantic data repositories. However, the abundance of these repositories is hidden behind sophisticated data schemas. At the same time, the lack of user-friendly tools for presenting data makes this collected knowledge unavailable for average users. In this paper we present our concept of creating a convenient semantic environment for ordinary users as well as maintaining rich functionality available for professionals. Our adaptation of a classic three-layer architecture for the semantic data sharing and presentation purpose reveals how the great usability and accessibility of data can be achieved. We also pay special attention to the standards and cooperation issues, which makes our system available for other tools implementing semantic data sharing concepts and keeps it open for Linked Open Data environment integration.

A knowledge-based approach to scheduling jobs in metacomputer environment

The importance of metacomputing is without question. Only such a use of distributed resources, as in metacomputing, has the potential to maximize performance and cost effectiveness of a wide range of scientific and distributed applications. There are several projects that address the metacomputing area. Globus, Legion and HPCM are only a few examples. However, in most cases resource dynamics are not taken into account. In this paper we present a concept of the metacomputer management software based on the expert system techniques. The knowledge about some specific aspects of metacomputing, like system specific knowledge, communication networks parameters, architecture specialization, run-time scheduling techniques, optimal resource allocation etc. is kept and managed by different experts. They negotiate with each other to make up a common scheduling decision.