Computational and Data Grids: Principles, Applications and Design

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Applications that need interaction through graphical user interfaces are more difficult torun on a grid, but not impossible. For instance, they can use remote graphical terminal support,such as X Windows or other similar capabilities. While High Performance Computing HPC clusters aresometimes used to handle the execution of applications that can utilize parallel processing, gridsprovide the ability to run these applications across a heterogeneous, geographically dispersed setof clusters.

Rather than run the application on a single homogenous cluster, the application cantake advantage of the larger set of resources in the grid. If the algorithm is such that eachcomputation depends on the prior calculation, then a new algorithm if possible may bebeneficial.

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In a distributed application, partial results or data dependencies may be met by communicatingamong sub-jobs. That is, one job may compute some intermediate result and then transmit it to If possible, one should consider whether it might be more efficient tosimply re-compute the intermediate result at the point where it is needed rather than waiting for itfrom another job.

One should also consider the transfer time from another job, versus retrievingit from a database of prior computations. This offers the opportunity to share this data rather than staging the entire set with each sub-job. However, one must consider that even with a shared mountable file system, the data is being sentover the network. The goal is to locate the shared data closer to the jobs that need the data.

If thedata is going to be used more than once, it could be replicated to the degree that space permits. If the application can be split into small work unitsrequiring little input data and producing small amounts of output data, that would be most ideal. The data in this kind of case is said to be staged to the node doing the work. Sending this dataalong with the executable file to the grid node doing the work is part of the function of most gridsystems. However, a grid solution does not come to fruition by simply installing software toallocate resources on demand.

Given that grid solutions are adaptable to meet the needs ofvarious business problems, differing types of grids are designed to meet specific usagerequirements and constraints. Additionally, differing topologies are designed to meet varyinggeographical constraints and network connectivity requirements.

The success of a grid solution isheavily dependent on the amount of thought the IT architect puts into the solution design. Somegrids are designed to take advantage of extra processing resources, whereas some gridarchitectures are designed to support collaboration between various organizations. Takingthe goals of the business into consideration will help you choose the proper type of gridframework. A business that wants to tap into unused resources for calculating risk analysiswithin their corporate data center will have a much different design than a company that wants toopen their distributed network to create a federated database with one or two of their mainsuppliers.

Such different types of grid applications will require different designs, based on theirrespective unique requirements. The selection of a specific grid type will have a direct impact on the grid solution design. Additionally, it should be mentioned that grid technologies are still evolving and tacticalmodifications to a grid reference architecture may be required to satisfy a particular businessrequirement. A well-known example of a computational grid is the SETI home grid. This type of grid isprimarily comprised of low-powered computers with minimal application logic awareness andminimal storage capacity.

Additional uses for a computational grid include mathematical equations, derivatives, pricing,portfolio valuation, and simulation especially risk measurement. Note that not all algorithmsare able to leverage parallel processing, data intensive and high throughput computing, order andtransaction processing, market information dissemination, and enterprise risk management.

Inmany cases, the grid architecture model is not yet suitable for real-time applications. Through collaboration, datagrids can also include resources such as a federated database. Within a federated database, as Through this single interface, the federated databaseprovides a single query point, data modeling, and data consistency. Data grids also harness data, storage, and network resources located in distinct administrativedomains, respect local and global policies governing how data can be used, schedule resourcesefficiently again subject to local and global constraints , and provide high speed and reliableaccess to data.

Businesses interested in data grids typically have IT initiatives to expand data-mining abilities while maximizing the utilization of an existing storage infrastructure investment,and to reduce the complexity of data management. The single organization could be made up of a number of computers that share acommon security domain, and share data internally on a private network.


The primary Within anintra-grid, it is easier to design and operate computational and data grids. An extra-grid, typically involves more than one security provider, and thelevel of management complexity increases. Within an extra-grid,the resources become more dynamic and your grid needs to be more reactive to failed resourcesand failed components. The design becomes more complicated and information services becomerelevant to ensure that grid resources have access to workload management at run time. An inter-grid topology, is primarily used by engineering firms, life scienceindustries, manufacturers, and by businesses in the financial industry.

The data in an inter-grid is global public data, and applications both vertical andhorizontal must be modified for a global audience. A business may deem an inter-grid necessary A mature end-to-end design methodology is comprised of distinct phases and activities. A methodology is not a cookbook for building a gridarchitecture, but a way to trace the progress of the design from the kickoff meeting to the finalend state. The methodology follows a reproducible set of guidelines that can be used over again A methodology allows thearchitecture to follow a set of principles that can be documented from beginning to endthroughout the design.

Many or most of the grid middleware, technologies, andsystem components are probably new to many people within the design team and it is always agood idea to hear firsthand from experienced IT professionals the means by which gridinfrastructures can be implemented, as well as any pitfalls to watch out for when designingenvironments for grid computing.

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The solution document should startwith a high-level overview of the environment and subsequently should drill down into the mostdetailed configuration diagrams and descriptions possible. You will want to include things like Your goal in building a prototypeshould be to produce a small-scale, end-to-end backbone of what your production environmentwill look like.

When allof the kinks are ironed out of your prototype, you will be confident that all of your componentswill work together properly in your designed infrastructure, and, additionally, you will havesome experience in the implementation of such a system. Benefits :-When you deploy a grid, it will be to meet a set of business requirements. To better match gridcomputing capabilities to those requirements, it is useful to keep in mind some commonmotivations for using grid computing.

The machine on which the application is normally run might be unusually busy due to a peak inactivity.


The job in question could be run on an idle machine elsewhere on the grid. There are at least two prerequisites for this scenario.

Distributed Computing

First, the application must be executableremotely and without undue overhead. Second, the remote machine must meet any specialhardware, software, or resource requirements imposed by the application.

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  6. For example, a batchjob that spends a significant amount of time processing a set of input data to produce an outputdata set is perhaps the most ideal and simple use case for a grid. If the quantities of input andoutput are large, more thought and planning might be required to efficiently use the grid for sucha job.

    It would usually not make sense to use a word processor remotely on a grid because therewould probably be greater delays and more potential points of failure.

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    In addition to pure scientific needs, such computing power is drivinga new evolution in industries such as the bio-medical field, financial modeling, oil exploration,motion picture animation, and many others. The common attribute among such uses is that the applications have been written to usealgorithms that can be partitioned into independently running parts. A CPU-intensive grid To the extent that these subjobs do not need to communicate with each other, the morescalable the application becomes. A perfectly scalable application will, for example, finish in onetenth of the time if it uses ten times the number of processors.

    In the past, distributed computing promised this collaboration andachieved it to some extent. Grid computing can take these capabilities to an even wider audience,while offering important standards that enable very heterogeneous systems to work together toform the image of a large virtual computing system offering a variety of resources. The users ofthe grid can be organized dynamically into a number of virtual organizations, each with differentpolicy requirements.

    Grid access control models and architectures

    These virtual organizations can share their resources collectively as a largergrid. A data grid can expand data capabilitiesin several ways. First, files or databases can span many systems and thus have larger capacitiesthan on any single system. Such spanning can improve data transfer rates through the use ofstriping techniques.

    Data can be duplicated throughout the grid to serve as a backup and can behosted on or near the machines most likely to need the data, in conjunction with advancedscheduling techniques. Sharing is not limited to files, but also includes other resources, such as specialized devices,software, services, licenses, and so on. These resources are virtualized to give them a moreuniform interoperability among heterogeneous grid participants. For example, ifa user needs to increase their total bandwidth to the Internet to implement a data mining searchengine, the work can be split among grid machines that have independent connections to theInternet.

    In this way, total searching capability is multiplied, since each machine has a separateconnection to the Internet. If the machines had shared the connection to the Internet, there wouldnot have been an effective increase in bandwidth. Some machines may have expensive licensed software installed that users require. Some machines on the grid may have special devices. Most of us have used remote printers,perhaps with advanced color capabilities or faster speeds. Similarly, a grid can be used to makeuse of other special equipment. For applications that are grid-enabled, the grid can offer a resourcebalancing effect by scheduling grid jobs on machines with low utilization.

    This feature can proveinvaluable for handling occasional peak loads of activity in parts of a larger organization. Without a grid infrastructure, such balancing decisions are difficult to prioritize and execute. Occasionally, a project may suddenly rise in importance with a specific deadline. A grid cannotperform a miracle and achieve a deadline when it is already too close. However, if the size of thejob is known, if it is a kind of job that can be sufficiently split into subjobs, and if enoughresources are available after preempting lower priority work, a grid can bring a very largeamount of processing.

    Theyare built using chips with redundant circuits that vote on results, and contain logic to achievegraceful recovery from an assortment of hardware failures. The machines also use duplicateprocessors with hot pluggability so that when they fail, one can be replaced without turning theother off.

    Power supplies and cooling systems are duplicated. The systems are operated onspecial power sources that can start generators if utility power is interrupted. All of this builds areliable system, but at a great cost, due to the duplication of expensive components. A grid is just the beginning of such technology.

    The systems in a grid can be relativelyinexpensive and geographically dispersed. Thus, if there is a power or other kind of failure at onelocation, the other parts of the grid are not likely to be affected. Grid management software canautomatically resubmit jobs to other machines on the grid when a failure is detected.

    In critical,real-time situations, multiple copies of important jobs can be run on different machinesthroughout the grid. Their results can be checked for any kind of inconsistency, such as computerfailures, data corruption, or tampering. It will be easier to visualize capacity and utilization, making it easier for ITdepartments to control expenditures for computing resources over a larger organization.

    Language: English. Brand new Book. Computational and Data Grids: Principles, Applications and Design offers critical perspectives on theoretical frameworks, methodologies, implementations, and cutting edge research in grid computing, bridging the gap between academia and the latest achievements of the computer industry. Useful for professionals and students involved or interested in the study, use, design, and development of grid computing, this book highlights both the basics of the field and in depth analyses of grid networks.

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    View all copies of this ISBN edition:. Synopsis About this title "This book provide relevant theoretical frameworks covering the latest empirical research findings in the area of grid computing, with a critical perspective bridging the gap between academia and the latest achievements of the computer industry"--Provided by publisher. About the Author : Nikolaos P. Review : "This reference is one the recent defining works on grid computing and contains an excellent collection of articles on technology"[ Buy New Learn more about this copy. Customers who bought this item also bought.

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