There is a common belief that Google, Facebook, Twitter and any of the newer Web 2.0 companies have it easier because they have homogeneous environments vs. a typical enterprise. Well, Google has a paper that discusses how its homogenous Warehouse-scale computers are actually heterogenous and there is opportunity for performance improvements of up to 15%.
In this table Google lists the number of micro architectures in 10 different data centers. Now Google has 13 WSCs so this could show how old this analysis was run (maybe 2-3 yrs ago.) Or it could have been more recently and they dropped 3 data centers out of the table. The 13th just came on line over the past year and would probably not have enough data.
The issue that is pointed out in the paper is that the job manager assumes the cores are homogenous.
When in fact they are not.
Here is the results summary.
Results Summary: This paper shows that there is a
significant performance opportunity when taking advantage
of emergent heterogeneity in modern WSCs. At the scale of
modern cloud infrastructures such as those used by companies
like Google, Apple, and Microsoft, gaining just 1% of
performance improvement for a single application translates
to millions of dollars saved. In this work, we show that largescale
web-service applications that are sensitive to emergent
heterogeneity improve by more than 80% when employing
Whare-Map over heterogeneity-oblivious mapping. When
evaluating Whare-Map using our testbed composed of key
Google applications running on three types of production
machines commonly found co-existing in the same WSC, we
improve the overall performance of an entire WSC by 18%.
We also find a similar improvement of 15% in our benchmark
testbed and in our analysis of production data from WSCs
hosting live services.
Here are three different microarchitectures used in the paper - Table 3 is production. Table 4 is a test bed.
Here are the range in performance for the three different micro architectures.
The new job scheduler is deployed at Google and here are results.
Figure 11 shows the calculated
performance improvement when using Whare-Map over the
currently deployed mapping in 10 of Google’s active WSCs.
Even though some major applications are already mapped
to their best platforms through manual assignment, we have
measured significant potential improvement of up to 15%
when intelligently placing the remaining jobs. This performance
opportunity calculation based on this paper is now
an integral part of Google’s WSC monitoring infrastructure.
Each day the number of ‘wasted cycles’ due to inefficiently
mapping jobs to the WSC is calculated and reported across
each of Google’s WSCs world wide.
There is more in the paper I need to digest, but I need to finish this post as it is long enough already.
