July 30, 2010
According to a recent report from Cisco Systems entitled, “Cloud: Powered by the Network” the company suggests that the two main drivers behind cloud adoption are cost and agility. The cost factor is one of the most-discussed aspects of the cloud as a more viable business model for companies who need increasing infrastructure but without the capital investment upfront, but agility is one element that seems to get the least amount of airtime from publications driven by an enterprise or business reader base.
When discussing what agility means in the cloud, there are just as many definitions as one could provide for the term “cloud” itself. In the context of Cisco’s understanding of agility (which is implied in the title of their report—“powered by the network”) it means the ability to refine and tailor a could environment. The many aspects of cloud computing that serve as enhancements or what Cisco calls “hot spots” of innovation, include “extended memory in servers, cache-enabling routers, solid-state storage systems, converged infrastructure, stateless infrastructure provisioning, ultra-scale distributed databases, real-time analytics, multi-channel content delivery…seamlessly integrated online and offline” and all coupled with a reduction in power consumption.
If cost and agility are the two main drivers guiding cloud adoption, then vendors should take note and begin to work to make their pricing structures more open to scrutiny and clearer. Most do a decent job of presenting their own view of agility through detailed descriptions of how their offering is powered and can be molded, but far too often the emphasis in cloud literature is on cost and the narrower, more service-specific ideas of agility are not described in depth quite enough. For some, agility might mean the network, for others it’s the automation, and for another class of cloud service providers it might mean the performance—how the agility is defined will depend on what the user needs, of course, but as a defining factor in cloud adoption, we need to see richer discussions on what agility really means in a cloud environment.
Full story at TMCnet
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).
Frank Ding, engineering analysis & technical computing manager at Simpson Strong-Tie, discussed the advantages of utilizing the cloud for occasional scientific computing, identified the obstacles to doing so, and proposed workarounds to some of those obstacles.
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