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Figure 8.7 Apparent Networks provides a free service that monitors and compares the performance of various cloud providers as measured from geographical locations across the U.S. The interface includes a user-definable scorecard that can be configured with different weighting factors to rank the providers. The company also has an advisory service that documents outages of major cloud providers (www.apparentnetworks.com/CPC).
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A cloud application is different from a conventionally deployed application in the level of control that s ceded to a third party. In a traditional deployment of an application with a colocation provider, you cede control over physical security, power, and network connectivity to a third party, but you retain control over the server hardware, operating systems, and application software. In a cloud deployment, you extend the ceding of control to include the server hardware and the operating system (through the virtualized environment). In comparison, this should be a good thing, because there s less to worry about as long as the provider s systems operate at a sufficient level of reliability. The operating principle for these systems can be summed up using former President Reagan s approach to nuclear disarmament: Trust, but verify. You ll need the necessary
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data to measure the cloud provider s compliance to the committed service levels. A practical strategy for maintaining good performance starts by assessing availability and performance as experienced by end users through continuous monitoring of the deployed application. When you detect a problem, instrumentation of the cloud applications is useful in determining whether the problem is of your own creation or is due to a problem with a cloud provider. Independent third-party assessments of cloud availability and performance can serve as a useful check before you draw conclusions. Now that you ve spent some time looking at the practical operational issues involved in managing an application deployed to the cloud, it s time to switch gears and get back to the bigger picture. In the final chapter of the book, let s leave the world of cloud computing as it exists today and fast-forward to see what the long-term future of IT looks like and how cloud computing will impact it.
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Cloud 9: the future of the cloud
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This chapter covers The most significant transformation IT has ever undergone Ten predictions about how the cloud will evolve Ten predictions about how application development will evolve An overview of the book
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The previous chapters as well as the introduction have covered a broad range of topics defining who can best use this book, what the cloud is, and the what, when, and how of moving to the cloud. It s consistent with that progression to end with a set of predictions for the future of cloud computing. We ll do our best to paint both a medium- and long-term picture, keeping with the themes of the rest of this book to make them useful. We ll give you more information and perspective about what this means for you and your business, and prepare you to make informed decisions about how and when to begin utilizing some aspects of the cloud. You ll also see how we think IT will look a generation from now it will look like what people have for years been calling utility computing.
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The most significant transformation IT has ever undergone
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UTILITY COMPUTING The packaging of computing resources, such as computation
and storage, as a metered service similar to a traditional public utility (such as electricity, water, natural gas, or a telephone network). This system has the advantage of a low or no initial cost to acquire hardware; instead, you can rent computational resources. Customers with large computations or a sudden peak in demand can also avoid the delays that would result from physically acquiring and assembling a large number of computers. This term predates cloud computing; but as you can see, this is what cloud computing is becoming. Making predictions, particularly long-range ones, is a risky business, as the short list of failed past predictions at times both wrong and irrelevant in figure 9.1 shows.
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