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Eli Lilly: large data set, high-compute scenarios
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As discussed earlier, cloud services offer a new capability to access large amounts of computing capacity in an on-demand fashion. It s therefore not surprising that one of the first tangible scenarios of successful use of public cloud services comes in this form. Eli Lilly is a global pharmaceutical company that requires vast amounts of computing resources as part of its drug development R&D process. In late 2007, the IT organization within Eli Lilly was frustrated at its inability to provision computing capacity for its scientists. According to David Powers, a long-time associate information consultant at Eli Lilly, it took more than 50 days to get a new machine up and running within existing corporate processes.
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The business case for cloud computing
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For a pharmaceutical company, time literally is money. When it files a new drug patent, the 20-year clock begins ticking. During that time, the drug must pass through several stages of clinical trials before being approved by the FDA. The faster the company can get a drug through that process, the longer it can market the drug exclusively and hence enjoy significant margins. The Eli Lilly IT team took the plunge and decided to use the Amazon AWS cloud as a platform for high-performance computing. They were able to set up a 64-node Linux cluster that could be brought online in only 5 minutes; formerly, it took 100 days for them to bring such a cluster online. The IT team made this resource available to hundreds of scientists within Eli Lilly and in the future hopes to extend its use to research partners for other projects.
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Washington Post: deadline-driven, large compute problems
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The next example is similar to the last in that it involves a problem that requires a vast computing infrastructure to perform. In this case, The Washington Post was looking for a fast way to make public the contents of Hillary Clinton s daily activity record from 1993 2001, the period that President Bill Clinton was in office. In response to a Freedom of Information Act request, the National Archives released this data at 10:00 A.M. on March 19, 2008, in the form of a 17,481-page low-quality, nonsearchable PDF. Peter Harkins, a senior engineer working at The Washington Post, used PDF-reading OCR software and devised a procedure to process the document at a rate of 1 page every 30 minutes. He moved his procedure over to the cloud, fired up 200 EC2 instances, and was able to process the entire document in 9 hours. Harkins immediately made the data available to reporters, and The Washington Post made the entire searchable document available to the public 26 hours after its release.1 The speed of accomplishing this task was phenomenal; but perhaps even more impressive is the fact that the 1,407 hours of virtual machine time cost the princely sum of $144.62. As a point of comparison, photocopying those pages at $0.05 a page would cost more than six times more: $874.05.
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The last enterprise example is somewhat different from the previous two; it s more similar to the cloud usage we discussed in section 3.5. It represents a shift from projectoriented enterprise usage models to one in which the enterprise relies on the cloud infrastructure day in and day out to provide services. Virgin Atlantic launched a new travel portal called Vtravelled.com and deployed it completely on a cloud infrastructure. It s a conventional website application that takes advantage of load balancing for improved reliability, performance, and scalability, as well as content delivery network (CDN) services provided by Amazon CloudFront to improve global service delivery. Because it s deployed in a cloud model, there was no
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Available at http://projects.washingtonpost.com/2008/clinton-schedule.
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Summary
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up-front capital expenditure, and the number of resources can be dialed up or dialed down in response to traffic patterns that may be driven by promotional campaigns. This deployment of a mainstream application by a large enterprise serves as a portent for the general adoption of cloud services by enterprises for everyday computing applications.
Summary
Cloud computing represents both a technology and an economic shift. This chapter introduced four common deployment models and examined the costs associated with operating a hypothetical e-commerce application in each. You saw that when the application workload is constant and an organization has existing internal data-center resources that can be used at no additional incremental cost, it can be more economical to deploy an application that way. The balance tips in favor of a cloud deployment model in cases where there are variations in the expected workload that the application must handle, or in cases where the application is needed for a short, fixed amount of time. We also looked at the application of the cloud by organizations of various sizes, from startups to large Fortune 500 enterprises. Having reviewed the business and economic implications of cloud computing, and when utilizing such an approach may be appropriate, we re now ready to look at issues of security in cloud computing. In the next chapter, we ll explore why this and other factors are driving some organizations toward private clouds. We ll also discuss how the trend toward a new concept called a virtual private cloud that runs on top of the public cloud may bring the best of both worlds together into a secure cloud computing solution.
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