  With the establishment of cloud computing as the environment of choice for
most modern applications, auto-scaling is an economic matter of great
importance. For applications like stream computing that process ever changing
amounts of data, modifying the number and configuration of resources to meet
performance requirements becomes essential. Current solutions on auto-scaling
are mostly rule-based using infrastructure level metrics such as
CPU/memory/network utilization, and system level metrics such as throughput and
response time. In this paper, we introduce a study on how effective
auto-scaling can be using data generated by the application itself. To make
this assessment, two algorithms are proposed that use a priori knowledge of the
data stream and use sentiment analysis from soccer-related tweets, triggering
auto-scaling operations according to rapid changes in the public sentiment
about the soccer players that happens just before big bursts of messages. Our
application-based auto-scaling was able to reduce the number of SLA violations
by up to 95% and reduce resource requirements by up to 33%.
