Diploma Thesis Santiago Gómez Sáez - IAAS

7. Validation and Evaluation
problem in the evaluation, which leads us to discard the scenario 2.1.2.2 (see Table 7.1). The
throughput obtained in the scenario 2.1.1.2 (see Table 7.1) is in average 3,3 requests per minute,
and lasts approximately 10 hours. This fact makes us delete the last scenario execution, due to
the low performance obtained, which we assume that it is related with the Quality of Service
assigned to the student grants credits profile in Amazon RDS.
As it can be seen in Figure 7.3, utilizing ServiceMix-mt as the database layer component for
communicating with a MySQL database system locally deployed lowers the throughput in a
32,74 % for 2 users, 20 concurrent threads per user, and 100 requests per thread. However,
when the thread and request number are increased, the throughput exponentially decreases,
as we reach the limit of concurrent connections supported in the MySQL CDASMix Proxy.
When we compare it with a direct connection to a locally deployed MySQL database system,
we can see that the concurrent requests are better handled by the MySQL server. In case of
avoiding cashing support in ServiceMix-mt, the throughput would considerably decrease
when increasing the load. When the distance between the different layers of the application
increases, e.g. accessing a database layer deployed in a Cloud infrastructure A, and the
database system is located in a Cloud infrastructure B, the number of requests per second
decreases (see scenario 2.1.1.1 in Figure 7.3). However, the difference we obtain from hosting
the database layer on premise, but accessing a database system off-premise is 98,64 % worse, if
we compare if with the access through ServiceMix-mt. We must denote that in these scenarios
there is a high temporal proximity of equal requests. Therefore, the cashing mechanism in
the system increases considerably the throughput when accessing data hosted off-premise
through ServiceMix-mt.
Figure 7.3.: Throughput (requests per second) for the different scenarios described in Table
7.1.
The amount of KB per second transmitted in the different scenarios correlates with the
tendency which can be seen in throughput (see Figures 7.3 and 7.4).
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