In 1998, Ian Foster and Carl Kesselman, together with thirty distinguished experts in high-performance computing and networking, laid the foundations of a new computing model called GRID. Their vision was introduced in the book, The GRID: Blueprint for a New Computing Infrastructure (see review, SCPE Vol. 3, No. 3 ). The editors wrote: … A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities… allowing new classes of applications to emerge… as yet, we have only a preliminary understanding of what these new applications will look like. Over the next five years, Grid computing became the hottest R&D field. Academia and national labs built experimental infrastructures, new startups were established to develop Grid technologies, and the HPC giant vendors joined the party. Each one pushed in a different direction based on what and how he believed Grid computing should look. It was only a matter of time until people started to ask what the Grid is all about. A survey of ten Grid experts produced ten different answers. The question remains without a clear answer, leaving many people confused. Ian Foster picked up the gloves. In his article, What is the GRID? A three point checklist (GRIDtoday, July 22, 2002: Vol. 1, No. 6), he redefines the Grid model to reflect the trades and changes in the field during the past five years. According to Foster, a Grid is a system that: Coordinates resources that are not subject to centralized control. Uses standard, open, general-purpose protocols and interfaces. Delivers a nontrivial quality of service. But Foster's new definition could not encompass all of the Grid's approaches. Moreover, Foster explained by example what the Grid is not: A cluster management system such as Sun's Sun Grid Engine, Platform's Load Sharing Facility, or Viridian's Portable Batch System can, when installed on a parallel computer or local area network, deliver quality of service guarantees and thus constitutes a powerful Grid resource. However, such a system is not a Grid itself, due to its centralized control of the hosts that it manages Within a week the responses started to come in. The first one was from Wolfgang Gentzsch, Director of Grid computing at Sun Microsystems (GRIDtoday, August 5, 2002: Vol. 1, No. 8). Gentzsch wrote: I very much like Ian's two earlier definitions. Combined, they may provide a very generic Grid definition. The new checklist, however, reduces the potential of a wide and rich variety of Grids, with different architectures, operational models, and applications, and may miss the chance to becoming widely accepted as a standard definition of the Grid. Why do we need a definition for the Grid? Can the Grid be defined? Is a definition is necessary to the Grid's development? The Grid is an open vision and thus cannot be defined. An attempt to define the Grid can only reach the foreseen technology horizon, and this horizon is only one to two years ahead. Grid computing is a paradigm in shift. New architectures and standards are changing the shape and direction of the Grid's development. In the past year one such prominent architecture was Open Grid Services Architecture (OGSA). The OGSA is the first attempt to bridge the gap between two cyberspace worlds: the Internet and the Grid. The Internet and the Grid are two different computing models. There are people who believe that the Grid is the next generation of the Internet and the World Wide Web, saying that the Great Global Grid is still many years away. Others state that the Grid will complement, not replace, the Internet as we know it. In 1998, when the Grid blueprint was published, a new Internet language appeared called eXtensible Markup Language (XML). XML is a development tool for a new Internet computing model known as Web Services. At that time, nobody understood that there is a relationship between Web Services and the Grid. Three years past before Steve Tuecke from Argonne National Laboratory started to write a spec for Grid Services. Later, people from IBM joined the mission and together they created OGSA. The OGSA specification outlines interfaces to grid computing software that comply with Web Services standards. If adopted, Grid services such as job scheduling, authentication, failure-detection, staging of applications and data, and migration of applications and data, will all be accessed through standard Web Services architecture. OGSA is basically where Grid meets Web Services and maybe the first step towards full integration of the Internet and the Grid. The Internet appeared a decade ago. In the beginning, the Net offered primitive applications: a mailing system, ftp services, and a web of home pages. Nobody asked, What is the Internet? The years passed and new emerging Internet technologies were developed. Today we can talk on the phone over the Net, chat with friends, and video conference with colleagues on the other side of the world. New computing and business models were developed: e-commerce, e-learning, e-science, and e-you-name-it. And nobody asks what the Internet is. The Grid is a super-model, a bag of networking models associated with many types of architectures, software packages, applications, and standards. Today, when people talk about the Grid they mention Data Grids, Science Grids, and Campus Grids. And at the same time they mention Farm computing, Peer-to-peer computing, and Utility computing, among others. Ten years from now many of today's technologies will disappear and new ones will be developed. I don't know how Grid computing will look a decade from now, but I know that we will have g-commerce, g-learning, g-science, and g-chat. Nobody will ask what the Grid is. It will be obvious, just as the Internet is today. Ami Marowka The Computer Aided Design Laboratory School of Engineering and Computer Science Hebrew University of Jerusalem

What is the GRID

Branch replication scheme: A new model for data replication in large scale data grids

Monitoring in distributed environment such as a grid is crucial for normal operation of all subsystems. Constant gathering of information enables efficient security auditing, failure detection, maintenance, job scheduling, accounting, resource performance tuning, debugging, etc.In this paper we focus on monitoring of resources in the grid with the purpose of failure detection, notifications and automatic recovery. We introduce our system based on open source monitoring framework Nagios that achieves these functionalities. We describe grid specific features we implemented in order to achieve efficient grid monitoring system, namely sensors for various grid services, advanced sensor hierarchy and certificate-based authorization on web interface. Finally, we give overview of the implementation of our system for monitoring EGEE grid infrastructure.

Grid infrastructure monitoring system based on Nagios

With virtual machine (VM) technology being increasingly mature, compute resources in cloud systems can be partitioned in fine granularity and allocated on demand. We make three contributions in this paper: 1) We formulate a deadline-driven resource allocation problem based on the cloud environment facilitated with VM resource isolation technology, and also propose a novel solution with polynomial time, which could minimize users' payment in terms of their expected deadlines. 2) By analyzing the upper bound of task execution length based on the possibly inaccurate workload prediction, we further propose an error-tolerant method to guarantee task's completion within its deadline. 3) We validate its effectiveness over a real VM-facilitated cluster environment under different levels of competition. In our experiment, by tuning algorithmic input deadline based on our derived bound, task execution length can always be limited within its deadline in the sufficient-supply situation; the mean execution length still keeps 70 percent as high as user-specified deadline under the severe competition. Under the original-deadline-based solution, about 52.5 percent of tasks are completed within 0.95-1.0 as high as their deadlines, which still conforms to the deadline-guaranteed requirement. Only 20 percent of tasks violate deadlines, yet most (17.5 percent) are still finished within 1.05 times of deadlines.

/pdf/error-tolerant-resource-allocation-and-payment-minimization-1tcnxuuowf.pdf

Error-Tolerant Resource Allocation and Payment Minimization for Cloud System

Science and engineering applications are now generating data at an unprecedented rate. From large facilities such as the Large Hadron Collider to portable DNA sequencing devices, these instruments can produce hundreds of terabytes in short periods of time. Researchers and other professionals rely on networks to transfer data between sensing locations, instruments, data storage devices, and computing systems. While general-purpose networks, also referred to as enterprise networks, are capable of transporting basic data, such as e-mails and Web content, they face numerous challenges when transferring terabyte- and petabyte-scale data. At best, transfers of science data on these networks may last days or even weeks. In response to this challenge, the Science Demilitarized Zone (Science DMZ) has been proposed. The Science DMZ is a network or a portion of a network designed to facilitate the transfer of big science data. The main elements of the Science DMZ include: 1) specialized end devices, referred to as data transfer nodes (DTNs), built for sending/receiving data at a high speed over wide area networks; 2) high-throughput, friction-free paths connecting DTNs, instruments, storage devices, and computing systems; 3) performance measurement devices to monitor end-to-end paths over multiple domains; and 4) security policies and enforcement mechanisms tailored for high-performance environments. Despite the increasingly important role of Science DMZs, the literature is still missing a guideline to provide researchers and other professionals with the knowledge to broaden the understanding and development of Science DMZs. This paper addresses this gap by presenting a comprehensive tutorial on Science DMZs. The tutorial reviews fundamental network concepts that have a large impact on Science DMZs, such as router architecture, TCP attributes, and operational security. Then, the tutorial delves into protocols and devices at different layers, from the physical cyberinfrastructure to application-layer tools and security appliances, that must be carefully considered for the optimal operation of Science DMZs. This paper also contrasts Science DMZs with general-purpose networks, and presents empirical results and use cases applicable to current and future Science DMZs.

A Comprehensive Tutorial on Science DMZ

Grid is a distributed environment that integrates computing, storage and other resources in order to enable execution of applications that cannot be run on a single resource. Such environment requires advanced scheduling system in order to efficiently execute users' applications. In this paper, we give overview of issues related to grid scheduling. We describe in details one of the most mature solutions - Condor-G matchmaking mechanism. Furthermore, we propose our own approach to building grid scheduling system based on Condor-G matchmaking

/pdf/an-approach-to-grid-scheduling-by-using-condor-g-matchmaking-3hdazxpsh2.pdf

An approach to grid scheduling by using condor-G matchmaking mechanism

Grid is a highly distributed heterogeneous environment which interoperates towards completing a common goal. As such grids performance is highly dependant on the speed of data transfer between systems of which grid comprises. Most commonly used and a "de facto" standard for data transfer on grid systems is GridFTP. While working on CRO-GRID Infrastructure project we faced problems with small data transfer speeds when transferring small files. In this paper we present a program we developed which uses GridFTP and speeds up data transfer of high number of small or medium sized files. We also present empirical results of the performance measurements achieved while using the implemented system.

/pdf/optimization-of-data-transfer-for-grid-using-gridftp-266qomh6mz.pdf

Optimization of Data Transfer for Grid Using GridFTP

Grid is a highly distributed heterogeneous environment which interoperates towards completing a common goal. As such, grids performance is highly dependant on the speed of data transfer between systems of which grid consists. Most commonly used and a “de facto” standard for data transfer on grid systems is GridFTP [7]. While working on CRO-GRID Infrastructure [3] project, we faced problems with small data transfer speeds when transferring small files. In this paper we present a program we have developed which uses GridFTP and speeds up data transfer of large number of small or medium sized files. We also present empirical results of the performance measurements achieved while using the implemented system.

Computational grids are platform for demanding scientific and engineering applications. Such applications usually need complex mapping of tasks to resources. Scheduling in dynamic and fault-prone grid environment is very difficult procedure and universal solution has not been designed yet. However scheduling system is necessary in order to enable users to easily submit and manage their jobs without detailed knowledge about underlying grid architecture. In this paper, we give overview of existing grid scheduling systems. Furthermore, we propose our own system with following functionalities: support for several job description languages, support for user defined scheduling algorithms, extensive use of advance reservations and storing detailed records of users' jobs.

CRO-GRID grid execution management system

Grid is distributed, heterogeneous environment comprised of various resources. Monitoring of grid resources is essential for realizing functionalities such as failure detection, application performance analysis, debugging, execution management, etc. Today many mature monitoring systems exist, but there are no complete solutions and functionalities of existing systems often overlap. In this article, we propose methodology of integration of various existing systems in order to provide rich information base for applications, users and administrators. Furthermore, we extend this system with following functionalities: additional sensors, data archiving, integrated web interface and tools for installation, configuration and maintenance of monitoring components.

CRO-GRID grid monitoring architecture

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An approach to grid scheduling by using condor-G matchmaking mechanism

Optimization of Data Transfer for Grid Using GridFTP

Optimization of Data Transfer for Grid Using GridFTP

CRO-GRID grid execution management system

CRO-GRID grid monitoring architecture