The Globus Toolkit (GT) has been developed since the late 1990s to support the development of service-oriented distributed computing applications and infrastructures. Core GT components address, within a common framework, basic issues relating to security, resource access, resource management, data movement, resource discovery, and so forth. These components enable a broader “Globus ecosystem” of tools and components that build on, or interoperate with, core GT functionality to provide a wide range of useful application-level functions. These tools have in turn been used to develop a wide range of both “Grid” infrastructures and distributed applications. I summarize here the principal characteristics of the latest release, the Web services-based GT4, which provides significant improvements over previous releases in terms of robustness, performance, usability, documentation, standards compliance, and functionality.

https://cs.uwaterloo.ca/~brecht/courses/702/Possible-Readings/grid-computing/globus-v4-icnpc-2005.pdf

Globus toolkit version 4: software for service-oriented systems

This paper describes the Pegasus framework that can be used to map complex scientific workflows onto distributed resources. Pegasus enables users to represent the workflows at an abstract level without needing to worry about the particulars of the target execution systems. The paper describes general issues in mapping applications and the functionality of Pegasus. We present the results of improving application performance through workflow restructuring which clusters multiple tasks in a workflow into single entities. A real-life astronomy application is used as the basis for the study.

/pdf/pegasus-a-framework-for-mapping-complex-scientific-workflows-4rcfo17ymc.pdf

Pegasus: A framework for mapping complex scientific workflows onto distributed systems

Progress of research efforts in a novel technology is contingent on having a rigorous organization of its knowledge domain and a comprehensive understanding of all the relevant components of this technology and their relationships. Cloud computing is one contemporary technology in which the research community has recently embarked. Manifesting itself as the descendant of several other computing research areas such as service-oriented architecture, distributed and grid computing, and virtualization, cloud computing inherits their advancements and limitations. Towards the end-goal of a thorough comprehension of the field of cloud computing, and a more rapid adoption from the scientific community, we propose in this paper an ontology of this area which demonstrates a dissection of the cloud into five main layers, and illustrates their interrelations as well as their inter-dependency on preceding technologies. The contribution of this paper lies in being one of the first attempts to establish a detailed ontology of the cloud. Better comprehension of the technology would enable the community to design more efficient portals and gateways for the cloud, and facilitate the adoption of this novel computing approach in scientific environments. In turn, this will assist the scientific community to expedite its contributions and insights into this evolving computing field.

https://smestorage.com/files/8f047da34a2d3a3528136ba8b59a465d.pdf

Toward a Unified Ontology of Cloud Computing

Cloud computing is an emerging commercial infrastructure paradigm that promises to eliminate the need for maintaining expensive computing facilities by companies and institutes alike. Through the use of virtualization and resource time sharing, clouds serve with a single set of physical resources a large user base with different needs. Thus, clouds have the potential to provide to their owners the benefits of an economy of scale and, at the same time, become an alternative for scientists to clusters, grids, and parallel production environments. However, the current commercial clouds have been built to support web and small database workloads, which are very different from typical scientific computing workloads. Moreover, the use of virtualization and resource time sharing may introduce significant performance penalties for the demanding scientific computing workloads. In this work, we analyze the performance of cloud computing services for scientific computing workloads. We quantify the presence in real scientific computing workloads of Many-Task Computing (MTC) users, that is, of users who employ loosely coupled applications comprising many tasks to achieve their scientific goals. Then, we perform an empirical evaluation of the performance of four commercial cloud computing services including Amazon EC2, which is currently the largest commercial cloud. Last, we compare through trace-based simulation the performance characteristics and cost models of clouds and other scientific computing platforms, for general and MTC-based scientific computing workloads. Our results indicate that the current clouds need an order of magnitude in performance improvement to be useful to the scientific community, and show which improvements should be considered first to address this discrepancy between offer and demand.

/pdf/performance-analysis-of-cloud-computing-services-for-many-3e3awq09a7.pdf

Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing

Workflows and e-Science: An overview of workflow system features and capabilities

Utility grids such as the Amazon EC2 cloud and Amazon S3 offer computational and storage resources that can be used on-demand for a fee by compute and data-intensive applications. The cost of running an application on such a cloud depends on the compute, storage and communication resources it will provision and consume. Different execution plans of the same application may result in significantly different costs. Using the Amazon cloud fee structure and a real-life astronomy application, we study via simulation the cost performance tradeoffs of different execution and resource provisioning plans. We also study these trade-offs in the context of the storage and communication fees of Amazon S3 when used for long-term application data archival. Our results show that by provisioning the right amount of storage and compute resources, cost can be significantly reduced with no significant impact on application performance.

The cost of doing science on the cloud: the Montage example

Advances in computational, storage and network technologies as well as middle ware such as the Globus Toolkit allow scientists to expand the sophistication and scope of data-intensive applications. These applications produce and analyze terabytes and petabytes of data that are distributed in millions of files or objects. To manage these large data sets efficiently, metadata or descriptive information about the data needs to be managed. There are various types of metadata, and it is likely that a range of metadata services will exist in Grid environments that are specialized for particular types of metadata cataloguing and discovery. In this paper, we present the design of a Metadata Catalog Service (MCS) that provides a mechanism for storing and accessing descriptive metadata and allows users to query for data items based on desired attributes. We describe our experience in using the MCS with several applications and present a scalability study of the service.

/pdf/a-metadata-catalog-service-for-data-intensive-applications-3quptbzeyu.pdf

A Metadata Catalog Service for Data Intensive Applications

This paper describes the design of a grid-enabled version of Montage, an astronomical image mosaic service, suitable for large scale processing of the sky. All the re-projection jobs can be added to a pool of tasks and performed by as many processors as are available, exploiting the parallelization inherent in the Montage architecture. We show how we can describe the Montage application in terms of an abstract workflow so that a planning tool such as Pegasus can derive an executable workflow that can be run in the Grid environment. The execution of the workflow is performed by the workflow manager DAGMan and the associated Condor-G. The grid processing will support tiling of images to a manageable size when the input images can no longer be held in memory. Montage will ultimately run operationally on the Teragrid. We describe science applications of Montage, including its application to science product generation by Spitzer Legacy Program teams and large-scale, all-sky image processing projects.

/pdf/montage-a-grid-enabled-engine-for-delivering-custom-science-2kq8l90hj8.pdf

Gurmeet Singh

Papers

Pegasus: A framework for mapping complex scientific workflows onto distributed systems

The cost of doing science on the cloud: the Montage example

A Metadata Catalog Service for Data Intensive Applications

A Metadata Catalog Service for Data Intensive Applications

Montage: a grid-enabled engine for delivering custom science-grade mosaics on demand