"Grid" computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and, in some cases, high performance orientation. In this article, the authors define this new field. First, they review the "Grid problem," which is defined as flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources--what is referred to as virtual organizations. In such settings, unique authentication, authorization, resource access, resource discovery, and other challenges are encountered. It is this class of problem that is addressed by Grid technologies. Next, the authors present an extensible and open Grid architecture, in which protocols, services, application programming interfaces, and software development kits are categorized according to their roles in enabling resource sharing. The authors describe requirements that they believe any such mechanisms must satisfy and discuss the importance of defining a compact set of intergrid protocols to enable interoperability among different Grid systems. Finally, the authors discuss how Grid technologies relate to other contemporary technologies, including enterprise integration, application service provider, storage service provider, and peer-to-peer computing. They maintain that Grid concepts and technologies complement and have much to contribute to these other approaches.

The Anatomy of the Grid: Enabling Scalable Virtual Organizations

A 2001 IBM manifesto observed that a looming software complexity crisis -caused by applications and environments that number into the tens of millions of lines of code - threatened to halt progress in computing. The manifesto noted the almost impossible difficulty of managing current and planned computing systems, which require integrating several heterogeneous environments into corporate-wide computing systems that extend into the Internet. Autonomic computing, perhaps the most attractive approach to solving this problem, creates systems that can manage themselves when given high-level objectives from administrators. Systems manage themselves according to an administrator's goals. New components integrate as effortlessly as a new cell establishes itself in the human body. These ideas are not science fiction, but elements of the grand challenge to create self-managing computing systems.

The vision of autonomic computing

http://cloud.pubs.dbs.uni-leipzig.de/sites/cloud.pubs.dbs.uni-leipzig.de/files/Brandic2008CloudcomputingandemergingITplatformsVisionhypeand.pdf

Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility

物件導向軟體之架構(Object-Oriented Software Construction)探討

Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end-users under a usage-based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd.

https://beloglazov.info/papers/2010-cloudsim-spe.pdf

CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

The pace of discovery in eScience is increasingly dependent on a scientist&#x2019;s ability to acquire, curate, integrate, analyze, and share large and diverse collections of data. It is all too common for investigators to spend inordinate amounts of time developing ad hoc procedures to manage their data. In previous work, we presented DERIVA, a Scientific Asset Management System, designed to accelerate data driven discovery. In this paper, we report on the use of DERIVA in a number of substantial and diverse eScience applications. We describe the lessons we have learned, both from the perspective of the DERIVA technology, as well as the ability and willingness of scientists to incorporate Scientific Asset Management into their daily workflows.

/pdf/experiences-with-deriva-an-asset-management-platform-for-26na4ud0eu.pdf

Experiences with DERIVA: An Asset Management Platform for Accelerating eScience

The combination of high-brilliance x-ray sources, fast detector systems, wide-bandwidth networks, and parallel computers can substantially reduce the time required to acquire, reconstruct, and visualize high-resolution three-dimensional tomographic datasets A quasi-realtime computed x-ray microtomography system has been implemented at the 2-BM beamline at the Advanced Photon Source at Argonne National Laboratory With this system, a complete tomographic data set can be collected in about 15 minutes Immediately after each projection is obtained, it is rapidly transferred to the Mathematics and Computing Sciences Division where preprocessing and reconstruction calculations are performed concurrently with the data acquisition by a SGI parallel computer The reconstruction results, once completed, are transferred to a visualization computer that performs the volume rendering calculations Rendered images of the reconstructed data are available for viewing back at the beamline experiment station minutes after the data acquisition was complete The fully pipelined data acquisition and reconstruction system also gives us the option to acquire the tomographic data set in several cycles, initially with coarse then with fine angular steps At present the projections are acquired with a straight-ray projection imaging scheme using 5-20 keV hard x rays in either phase or amplitude contrast mode at a 1-10 pm resolution In the future, we expect to increase the resolution of the projections to below 100 nm by using a focused x-ray beam at the 2-ID-B beamline and to reduce the combined acquisition and computation time to the 1 min scale with improvements in the detectors, network links, software pipeline, and computation algorithms

/pdf/quasi-real-time-x-ray-microtomography-system-at-the-advanced-npogsrs3r9.pdf

Quasi-real-time x-ray microtomography system at the Advanced Photon Source

Computational grids are enabling collaboration between scientists and organizations to generate and archive extremely large datasets across shared, distributed resources. There is a need to visually explore such data throughout the life-cycle of projects. Practical exploration of large datasets requires visualization tools that can function in the same grid environment in which the data is created and stored. Resource management interfaces are an important structural component of grid computing environments because they enable uniform access to the wide variety of resources necessary for scientific work. We describe a new advance-reservation system for graphics resources; and an application of existing grid technology to create general-purpose active storage systems. We report our experience with prototype infrastructure and application components, involving experiments coupling end-to-end resources for interactive visual exploration of large data in representative distributed environments.

/pdf/practical-resource-management-for-grid-based-visual-kqohol21op.pdf

Practical resource management for grid-based visual exploration

This document gives a concise definition of the syntax and semantics of CC++. Knowledge of the C++ language definition and the C++ language reference manual is assumed.

The Compositional C++ Language Definition

In this introductory chapter, we lay the groundwork for the rest of the book by providing a more detailed picture of the expected purpose, shape, and architecture of future grid systems. We structure the chapter in terms of six questions that we believe are central to this discussion: Why do we need computational grids? What types of applications will grids be used for? Who will use grids? How will grids be used? What is involved in building a grid? And, what problems must be solved to make grids commonplace? We provide an overview of each of these issues here, referring to subsequent chapters for more detailed discussion.

Carl Kesselman

Papers

Experiences with DERIVA: An Asset Management Platform for Accelerating eScience

Quasi-real-time x-ray microtomography system at the Advanced Photon Source

Practical resource management for grid-based visual exploration

The Compositional C++ Language Definition

Computational Grids