August 24, 2010
Private clouds are gaining far more attention than they did at the beginning of the cloud buzz phase, in part due to the perceived security issues involved with public clouds such as Amazon’s EC2, among others. However, while private clouds are gaining traction in the enterprise, not to mention in the media directed at IT decision makers, the reality of building a private cloud is far from simple, even if on the surface it seems like it shouldn’t be a complex, time-consuming and resource-heavy investment.
While most conversations around cloud computing hinge on the concept that it is a way to save money, some find themselves in over their heads when they actually make the leap into a private cloud, and not only from a cost perspective. Bill Claybrook from Computerworld noted that in addition to the surprising number of financial and time costs involved with the process, other concerns such as integration with public clouds, working with legacy systems, addressing reconfiguration needs, and even dealing with resistance to change on the part of IT are all factors that can make this process difficult.
Claybrook states that “indeed, transitioning from a traditional data center—even one with some servers virtualized—to a private cloud architecture is no easy task, particularly given that the entire data center won’t be cloud-enabled, at least not right away.”
While a large number of vendors are doing quite well by advertising their diverse abilities to make private clouds more approachable, many conversations that generate from these quarters dismiss the challenge that private cloud adoption can present.
Full story at PCWorld
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.
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/02/2012 | AMD | Developers today are just beginning to explore the potential of heterogeneous computing, but the potential for this new paradigm is huge. This brief article reviews how the technology might impact a range of application development areas, including client experiences and cloud-based data management. As platforms like OpenCL continue to evolve, the benefits of heterogeneous computing will become even more accessible. Use this quick article to jump-start your own thinking on heterogeneous computing.