March 04, 2011
This week Mike Vizzard discussed the convergence of grid and cloud computing with Nikita Ivanov, the CEO of GridGain, an application development platform company. In Ivanov’s view “it’s only a matter of time before cloud computing catches up to the data management principles that were first created for grid computing environments—and only a matter of time before the two concepts merge.”
While this might be happening at a rapid rate, few will suggest that grid computing is an “easy” concept in practice. It takes a great deal of legwork to make a grid computing system thrive and function as it’s supposed to. Despite a great deal of marketing pushing the concept of simple cloud computing models that can be implemented and spun out in seconds, Vizzard contends that as cloud computing continues to mature, “IT organizations are discovering just how hard it is to make data truly elastic…in reality, most existing databases and enterprise applications were never designed to dynamically scale.
Naturally, Ivanov’s view is that their own GridGain platform is the key to allow computational and data management possibilities under the umbrella of one architecture, but this is a message that is coming from a number of other companies with cloud offerings to bring to a still somewhat confused market.
Vizzard suggests that we are currently living in the “Stanley Steamer Age” of cloud computing. In this era, there are “lots of different services but most of them are not as automated or dynamic as most people think, which results in a lot of manual intervention to crank them up.”
There are likely to be parallels to grid computing as the cloud computing trend continues to evolve but as Vizzard reminds readers, grid is “by no means simple to deploy or master” and as the next generation of cloud possibilities emerge, these same challenges need to be met with realistic expectations. There is still no magic bullet for the cloud that completely removes or conceals complexity.
Full story at ITBusinessEdge
The ever-growing complexity of scientific and engineering problems continues to pose new computational challenges. Thus, we present a novel federation model that enables end-users with the ability to aggregate heterogeneous resource scale problems. The feasibility of this federation model has been proven, in the context of the UberCloud HPC Experiment, by gathering the most comprehensive information to date on the effects of pillars on microfluid channel flow.
Large-scale, worldwide scientific initiatives rely on some cloud-based system to both coordinate efforts and manage computational efforts at peak times that cannot be contained within the combined in-house HPC resources. Last week at Google I/O, Brookhaven National Lab’s Sergey Panitkin discussed the role of the Google Compute Engine in providing computational support to ATLAS, a detector of high-energy particles at the Large Hadron Collider (LHC).
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