Hardware Reference
In-Depth Information
Example
Calculate the cost of running the average MapReduce jobs in Figure 6.2 on page
437 on EC2. Assume there are plenty of jobs, so there is no significant extra cost
to round up so as to get an integer number of hours. Ignore the monthly storage
costs, but include the cost of disk I/Os for AWS's Elastic Block Storage (EBS).
Next calculate the cost per year to run all the MapReduce jobs.
Answer
The first question is what is the right size instance to match the typical server
at Google? Figure 6.21 on page 467 in Section 6.7 shows that in 2007 a typical
Google server had four cores running at 2.2 GHz with 8 GB of memory. Since
a single instance is one virtual core that is equivalent to a 1 to 1.2 GHz AMD
Opteron, the closest match in Figure 6.15 is a High-CPU Extra Large with eight
virtual cores and 7.0 GB of memory. For simplicity, we'll assume the average
EBS storage access is 64 KB in order to calculate the number of I/Os.
Figure 6.16 calculates the average and total cost per year of running the
Google MapReduce workload on EC2. The average 2009 MapReduce job would
cost a litle under $40 on EC2, and the total workload for 2009 would cost $133M
on AWS. Note that EBS accesses are about 1% of total costs for these jobs.
FIGURE 6.16 Estimated cost if you ran the Google MapReduce workload
( Figure 6.2 ) using 2011 prices for AWS ECS and EBS ( Figure 6.15 ) . Since
we are using 2011 prices, these estimates are less accurate for earlier years
than for the more recent ones.
Example
Given that the costs of MapReduce jobs are growing and already exceed $100M
per year, imagine that your boss wants you to investigate ways to lower costs.
 
 
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