September 02, 2010
Dr. Will Venters from the London School of Economics is currently conducting a research project with research group Horses for Sources that looks at how CERN’s scientists are making us of the grid to handle huge volumes of data and how on the flipside, business can examine this model as they make critical decisions about cloud computing. While the scale is certainly far different, for large enterprises, examining such comparisons in light of their own mission-critical needs might be a valuable exercise in showing what is possible in a distributed computing environment.
As background for those who are not familiar with the CERN model, Phil Fersht from Horses for Sources notes that, “The purpose is to provision a data storage and analysis infrastructure for the entire high-energy physics community—not too dissimilar from a private cloud environment where users can plug in to the shared environment and access the applications they need, without stacks of IT hardware in the basement to house the data or IT personnel on site needed to maintain and support the infrastructure.”
When asked about what he’s observed at CERN that would carry over to the enterprise domain, Dr. Will Venters stated that the cloud and distributed computing environments provide great opportunities for collaboration, even if there are still the ages-old problems with collaboration across domains and interest areas to contend with. On a higher level, Venters contends that “the knowledge infrastructure around their [CERN’s] work is the key part of it and perhaps something the businesses would benefit from learning.”
Dr. Will Venters has shared some in-depth insights about his observations at the CERN site in this must-read article for those in the scientific and enterprise computing spaces alike.
Full story at Horses for Sources
Researchers from the Suddhananda Engineering and Research Centre in Bhubaneswar, India developed a job scheduling system, which they call Service Level Agreement (SLA) scheduling, that is meant to achieve acceptable methods of resource provisioning similar to that of potential in-house systems. They combined that with an on-demand resource provisioner to ensure utilization optimization of virtual machines.
Experimental scientific HPC applications are continually being moved to the cloud, as covered here in several capacities over the last couple of weeks. Included in that rundown, Co-founder and CEO of CloudSigma Robert Jenkins penned an article for HPC in the Cloud where he discussed the emergence of cloud technologies to supplement research capabilities of big scientific initiatives like CERN and ESA (the European Space Agency)...
When considering moving excess or experimental HPC applications to a cloud environment, there will always be obstacles. Were that not the case, the cost effectiveness of cloud-based HPC would rule the high performance landscape. Jonathan Stewart Ward and Adam Barker of the University of St. Andrews produced an intriguing report on the state of cloud computing, paying a significant amount of attention to the problems facing cloud computing.
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.