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Scale variability/volatility
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In the last section, what drove the cost savings for the cloud deployment was the finite time interval for which the application was needed. Let s look at another application characteristic where the scale and load variability requirement can make it advantageous to deploy using a cloud model. Scale requirements for an application may vary in several ways. Some types of capacity variability are predictable, and you can anticipate them. For example, the daily variations for financial or online trading applications that see the most traffic and hence require the largest amount of capacity are at the open and close of the market. These applications typically see variations in traffic load of 4X between the open and close and the low point of the day. Another example is the seasonal variation seen by online e-commerce sites in the days immediately following Thanksgiving, the traditional start of the Christmas shopping season. For these e-commerce applications, peak capacity requirements can be as much as 20X normal capacity requirements, as you can see in figure 3.3 for Target.com.
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Where does the cloud make sense
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Daily United States People 09/02/09- 02/28/10 3.1M
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Directly Measured
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Rough Estimate
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.7M Oct 09 target.com US GLOBAL 1.2M Max: 3.0M 11/29/09 Nov 09 Dec 09 Jan 10 Feb 10 Rough Estimate global stats not yet available for estimated data
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Figure 3.3 This chart shows the estimated daily number of U.S. visitors to Target.com as determined by Quantcast (www.quantcast.com/target.com) over a one-year interval. As it does for many e-commerce sites, peak utilization occurs predictably at the end of November, at the start of the Christmas shopping season.
If you consider designing and building infrastructure in an internal IT or colocation deployment model to handle peak capacity 4X or 20X normal capacity, you re looking at increasing the cost of the deployment by roughly the same factors. Granted, you ll gain some efficiency by purchasing larger volumes, but these gains are minimal compared to the overall size of the difference in required investment to scale. At peak utilization times, the entire infrastructure will be maximally taxed servicing the application. At other times, it will be severely underutilized. In the cloud model, you can manage predictable traffic peaks in a much more costeffective manner. You can bring additional instances online to handle the increased load during the hours or days of the increased capacity. For example, if you require 4X the normal capacity at 9:00 A.M., and at other times of the day only four instances are needed, you can spin up 12 additional instances for the hour they re needed and only pay for the additional instances when they re active. Other scale variations are less predictable. Event-driven spikes in scale requirements can hit with as little warning as a tsunami and have devastating effects on the operation of the application. One example illustrated in figure 3.4 is the spike in traffic on the TMZ.com website after they were the first to report the death of Michael Jackson on June 25, 2009.
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Daily Global People 10/27/09-04/21/10 4.0M
Directly Measured
Rough Estimate
3.0M
2.0M
1.0M
0 Nov 09 tmz.com GLOBAL CA NL GB FI US 1.7M 116K 4.3K 18.0K 1.1K 1.4M Max: Max: Max: Max: Max: 3.9M 290K 108K 187K 2.9M 12/21/09 12/21/09 12/21/09 12/21/09 12/21/09 12/21/09 Embed Dec 09 Jan 10 Feb 10 Mar 10 Apr 10
Directly Measured
Max: 86.8K
Figure 3.4 This chart shows the estimated daily number of U.S. visitors to TMZ.com as determined by Quantcast (www.quantcast.com/tmz.com) between 6/03/09 and 8/31/09. This is an example of an event-driven spike in demand on 6/25/09, when TMZ.com was the first to report the death of Michael Jackson.
If you have to rely solely on resources deployed in either an internal IT model or a colocation model, you literally have no recourse but to try to weather out high-demand periods. If you have the ability to use the cloud and can dynamically spin up capacity, handling all the unexpected traffic will happen seamlessly.
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