February 22, 2011
Proctor and Gamble, Microsoft, Chevron, Mitre and Ford are a few of the big-name companies that are using a cloud-based tool to mitigate operational risks via prediction markets. Prediction markets serve as aggregators of shifting opinion and operate much in the same way the stock market does. They have been used for a wide range of purposes, including determining who will win an election, how well a movie will perform at the box office, and what products are destined to be flops.
One company delivering such a solution is Inkling, which was founded in 2006 by Accenture alums, Adam Siegel and Nathan Kontry following seed funding from yCombinator. Ford Motor Company, after using their own predictive analytics solutions, turned to the Chicago-based company to improve their ability to make decisions that would be in line with customer interests.
According to David Needle, “the simulated stock market, being used by more than 1,300 Ford employees in the United States and Europe, encourages members to comment on various topics and issues through stock market-like trading.” Ford is using Inkling’s cloud-based solution to replace their own similar but very resource-heavy predictive markets solution.
As Tom Montgomery from Ford’s Research and Advanced Engineering Group told Needle, Ford moved from its own solution to Inkling’s in 2009. “It’s their software and their servers; they host everything but we brand the interface…the important thing for us is that the information we collect is proprietary and they offered the security and guarantees we wanted.”
Ford has found the cloud-based solution satisfactory as it has led to the abandonment of a number of ideas before development dollars were wasted, including the (dropped) plan for an in-car vacuum—an item that the analytics software decided would not be worth the effort.
The use of predictive markets is going beyond product and stock market predictions to play a role in science, according to Emily Badger from Miller-McCune. The Woodrow Wilson Center, with assistance from the National Science Foundation, will be making use of prediction markets for science and research.
The project will revolve around synthetic biology and will pull together experts that generally do not collaborate, including engineers and computer scientists as well as biology researchers. Badger notes that “the field of biology lends itself well to the quantifiable questions necessary to run a prediction market: How many genes can scientists put together? Can they create a synthetic organism that will allow us to produce hydrogen fuel? When will that happen?”
Full story at InternetNews
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.
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