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A Networking Approach to Grid Computing explores the practical advantages of grid computing and explains what is needed in order to migrate successfully to this new computing paradigm and exploit the business opportunities afforded by grid computing. This book offers the knowledge and skills needed to architect and deploy a grid computing environment that contributes directly to key business goals, including: The benefits of grid computing and the status of the technology Standards supporting grid computing including OGSI and OGSA Deployment and management of computing grids The economics of grid systems Communication systems for local, national, and global grids Familiarizing readers with the services, reference architectures, storage, and software necessary for successful implementation of a grid system, A Networking Approach to Grid Computing shows how to set up an organization's grid environment faster, easier, and at a lower cost than its competitors.

Grid Benefits and Status of Technology. Grid Technology.


Scientific Instruments. Thanks to the acquisition, Sun released a general-purpose grid-computing production that allows any organization to reap the benefits of such an approach, for example, the following benefits:. Clear resource utilization benefits can be achieved with products like Gridware. If an organization can better aggregate the compute power of existing servers and desktop nodes, a highly scalable clusterlike resource which can include thousands of processors is the result. Many organizations have made heavy investments in compute resources; many of those nodes remain idle much of the time.

Estimates of electricity consumed by Internet-connected nodes in the United States range from 2 percent to 8 percent, [29] which may not seem substantial, but any waste of processor capability is a waste of resources. The grid computing fitscape of NDC will help address better resource utilization in the aggregate. Interestingly, the Gridware product from Sun also reflects the trend toward ephemeralization with respect to software product cost; the basic engine is free.

First, we quantified, through the Pearson correlation coefficient, the potential of several measurements from complex networks to predict the efficiency of BA networks, finding out that the closeness provided the best alternative. Then, we checked out the influence of the networks size i. Despite a quantization effect appearing for large sizes, all tried situations yielded very high Pearson coefficients.

Finally, we also found that extremely good predictions can be obtained even for other network topologies such as ER and WS. The high values of Pearson correlation coefficients obtained for most tested cases allow us to choose the best master node from which to distribute the task, i. It should be observed that the developments and results reported in this work, although related to grid computing, can be extended to many other real-world situations and systems.


For instance, basic parts supply request by industries could be modelled by using our approach by understanding the production as a task and the requests as messages. A similar approach could be used for investigating task distribution among employees of a company or office. Future studies could investigate task distribution schemes other than the master—slave configuration addressed in the present work.

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A Networking Approach to Grid Computing

It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Sign In. Advanced Search. Article Navigation. Close mobile search navigation Article Navigation. Volume 1.

Distributed Computing

Article Contents. Results and discussion. Predicting efficiency in master—slave grid computing systems G. Oxford Academic. Google Scholar.

Edited by: Ernesto Estrada. Cite Citation. Permissions Icon Permissions. Consider a node i. Figure 1.

Open in new tab Download slide. At the start of the execution, a slave that lies at a distance d from the master will need to wait for a task for 2 d time units d units for its request to reach the master and another d for the task to arrive. Suppose the node takes c time units to compute the task. If the total number of tasks is sufficiently large, all slaves at distance d from the master will receive approximately the same number of task to compute; we represent this number of tasks as m d , as it depends on the distance. Again if the total number of tasks is sufficiently large, all slaves will end processing approximately at the same time, such that we can consider T d in Equation 2.

Figure 2. Table 1. Open in new tab. Figure 3. Figure 4. Search ADS. Google Preview. Published by Oxford University Press.

Grid Computing: A Practical Guide To Technology and Applications

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