"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

In task-parallel programs, diverse activities can take place concurrently, and communication and synchronization patterns are complex and not easily predictable. Previous work has identified compositionality as an important design principle for task-parallel programs. In this paper, we discuss alternative approaches to the realization of this principle. We first provide a review and critical analysis of Strand, an early compositional programming language. We examine the strengths of the Strand approach and also its weaknesses, which we attribute primarily to the use of a specialized language. Then, we present an alternative programming language framework that overcomes these weaknesses. This framework uses simple extensions to existing sequential languages (C++ and Fortran) and a common runtime system to provide a basis for the construction of large, task-parallel programs. We also discuss the runtime system techniques required to support these languages on parallel and distributed computer systems.

/pdf/language-constructs-and-runtime-systems-for-compositional-3194qqxhyi.pdf

Language Constructs and Runtime Systems for Compositional Parallel Programming

“Grid” is the term used to refer to an emerging computational and networking infrastructure that provides pervasive, uniform, and reliable access to remote data, computational, sensor, and human resources (Foster and Kesselman, 1999). The Grid enhances today’s Internet with new services in such areas as interdomain security, resource discovery, and resource allocation. By thus eliminating barriers to the use of (in particular, high-end) computational capabilities for problem solving, this infrastructure is enabling new applications in such areas as collaborative work, distributed data mining, computer-enhanced instrumentation, and distributed super-computing.

https://link.springer.com/content/pdf/10.1007%2F978-94-010-0948-5_3.pdf

The Emerging Grid

Progress in our understanding of brain disorders increasingly relies on costly collection of large standardized brain magnetic resonance imaging (MRI) data sets. Moreover, clinical interpretations of brain scans benefit from compare and contrast analyses of scans from patients with similar, and sometimes rare, demographic, diagnostic, and treatment status. A solution to both needs is to acquire standardized, research-ready clinical brain scans and to build the information technology infrastructure to share such scans, along with other pertinent information, across hospitals. The resulting research-ready brain imaging resource would provide a wealth of accessible standardized brain imaging data relevant to patient care and research. This paper describes a pilot project that develops such a brain resource, including the rationale, the short imaging protocol, the access to patient data, and the system architecture. This pilot project is a joined effort by researchers from the Clinical Translational Science Institutes (CTSIs) at the University of California Irvine (UCI) and University of Southern California (USC) with strong support from the Biomedical Informatics Research Network (BIRN). The pilot system developed enables capture and sharing of standardized, de-identified clinical brain images across institutions via a federated database system.

Infrastructure for sharing standardized clinical brain scans across hospitals

GriPhyN/PPDG Data Grid Architecture, Toolkit, and Roadmap — Version 2 —

Progress in our understanding of brain disorders increasingly relies on the costly collection of large standardized brain magnetic resonance imaging (MRI) data sets. Moreover, the clinical interpretation of brain scans benefits from compare and contrast analyses of scans from patients with similar, and sometimes rare, demographic, diagnostic, and treatment status. A solution to both needs is to acquire standardized, research-ready clinical brain scans and to build the information technology infrastructure to share such scans, along with other pertinent information, across hospitals. This paper describes the design, deployment, and operation of a federated imaging system that captures and shares standardized, de-identified clinical brain images in a federation across multiple institutions. In addition to describing innovative aspects of the system architecture and our initial testing of the deployed infrastructure, we also describe the Standardized Imaging Protocol (SIP) developed for the project and our interactions with the Institutional Review Board (IRB) regarding handling patient data in the federated environment.

/pdf/a-system-architecture-for-sharing-de-identified-research-pp4twupo9t.pdf

Carl Kesselman

Papers

Language Constructs and Runtime Systems for Compositional Parallel Programming

The Emerging Grid

Infrastructure for sharing standardized clinical brain scans across hospitals

GriPhyN/PPDG Data Grid Architecture, Toolkit, and Roadmap — Version 2 —

A system architecture for sharing de-identified, research-ready brain scans and health information across clinical imaging centers.