Programmable Grids Framework Enabling QoS in an OGSA Context

Soldatos, John; Polymenakos, Lazaros; Kormentzas, George

doi:10.1007/978-3-540-24688-6_28

Cited by 4 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The principles of the Grid architecture are outlined in Foster and Kesselman (2004) and Czajkowski et al (2001), while in Misev and Atanassov (2010), Al-Ali et al (2004) and Soldatos et al (2004) we can see approaches to ensuring QoS in Grid context.…”

Section: Job Track Servicementioning

confidence: 97%

Quasi-Monte Carlo integration on the grid for sensitivity studies

et al. 2010

View full text Add to dashboard Cite

In this paper we present error and performance analysis of quasi-Monte Carlo algorithms for solving multidimensional integrals (up to 100 dimensions) on the grid using MPI. We take into account the fact that the Grid is a potentially heterogeneous computing environment, where the user does not know the specifics of the target architecture. Therefore parallel algorithms should be able to adapt to this heterogeneity, providing automated load-balancing. Monte Carlo algorithms can be tailored to such environments, provided parallel pseudorandom number generators are available. The use of quasi-Monte Carlo algorithms poses more difficulties. In both cases the efficient implementation of the algorithms depends on the functionality of the corresponding packages for generating pseudorandom or quasirandom numbers. We propose efficient parallel implementation of the Sobol sequence for a grid environment and we demonstrate numerical experiments on a heterogeneous grid. To achieve high parallel efficiency we use a newly developed special grid service called Job Track Service which provides efficient management of available computing resources through reservations.

show abstract

Section: Job Track Servicementioning

confidence: 97%

Quasi-Monte Carlo integration on the grid for sensitivity studies

et al. 2010

View full text Add to dashboard Cite

show abstract

“…The top hierarchical grid node provides the information requested by the resource discovery query. This information is provided to the system selection module as a piece of OGSA Grid SD (Service Data) [27]. This piece of service data provides access to the respective service data structures for each Grid host.…”

Section: Information Retrieval Y System Selectionmentioning

confidence: 99%

EMPEROR: An OGSA Grid Meta-Scheduler Based on Dynamic Resource Predictions

2005

Self Cite

View full text Add to dashboard Cite

Scheduling constitutes an integral feature of Grid computing infrastructures, being also a key to realizing several of the Grid promises. In particular, scheduling can maximize the resources available to end users, accelerate the execution of jobs, while also supporting scalable and autonomic management of the resources comprising a Grid. Grid scheduling functionality hinges on middleware components called meta-schedulers, which undertake to automatically distribute jobs across the dispersed heterogeneous resources of a Grid. In this paper we present the design and implementation of a Grid meta-scheduler, which we call EMPEROR. EMPEROR provides a framework for implementing scheduling algorithms based on performance criteria. In implementing a particular instantiation of this framework, we have devised models for predicting host load and memory resources, and accordingly for estimating the running time of a task. These models hinge on time series analysis techniques and take into account results of the cluster computing literature. Apart from incorporating these models, EMPEROR provides fully fledged Grid scheduling functionality, which complies with OGSA standards as the later are reflected in the Globus toolkit. Specifically, EMPEROR interfaces to Globus middleware services (i.e., GSI, MDS, GRAM) towards discovering resources, implementing the scheduling algorithm and ultimately submitting jobs to local scheduling systems. By and large, EMPEROR is one of the few standards based meta-schedulers making use of dynamic scheduling information.

show abstract

“…• It can alleviate the high cost of computing resources required to support microarray experiments [7,8]: Researchers can leverage the computing power, the immense storage resources, as well as the quality of service provided by Grid computing infrastructures [28,29]. • It can enable access to distributed resources (i.e.…”

Section: Introductionmentioning

confidence: 99%

HECTOR: Enabling Microarray Experiments over the Hellenic Grid Infrastructure

et al. 2009

Self Cite

View full text Add to dashboard Cite

Biologists, medical experts, biochemical engineers and researchers working on DNA microarray experiments are increasingly turning on Grid computing with the scope of leveraging the Grid's computing power, immense storage resources, and quality of service to the expedient processing of a wide range of datasets. In this paper we present a combined experience of grid application experts and bioinformatics scientists in deploying a pilot service enabling computationally efficient processing and analysis of data stemming from microarray experiments. This pilot service is accessible over the Hellenic portion of the EGEE grid and has been demonstrated in the scope of several public events. We highlight the process of grid application enablement, grid deployment challenges, as well as lessons learnt from a bi-annual effort to port and deploy a MATLAB DNA microarray application on a production grid. In addition to describing the parallelization of the application, we also emphasize on the development of a distributed federated database for storing and post-processing the results of the microarray experiments. Overall we believe that our experience could be proven valuable not only to microarray data scientists but also to other Grid users that intend to Grid-enable and deploy their applications.

show abstract

Programmable Grids Framework Enabling QoS in an OGSA Context

Cited by 4 publications

References 9 publications

Quasi-Monte Carlo integration on the grid for sensitivity studies

Quasi-Monte Carlo integration on the grid for sensitivity studies

EMPEROR: An OGSA Grid Meta-Scheduler Based on Dynamic Resource Predictions

HECTOR: Enabling Microarray Experiments over the Hellenic Grid Infrastructure

Contact Info

Product

Resources

About