Proactive-Reactive Global Scaling, with Analytics
Abstract
In this work, we focus on by-design global scaling, a technique that, given a functional specification of a microservice architecture, or-
chestrates the scaling of all its components, avoiding cascading slowdowns typical of uncoordinated, mainstream autoscaling. State-of-the-art by-
design global scaling adopts a reactive approach to traffic fluctuations, undergoing inefficiencies due to the reaction overhead. Here, we tackle
this problem by proposing a proactive version of by-design global scaling able to anticipate future scaling actions. We provide four contributions
in this direction: i) a platform able to host both reactive and proactive global scaling; ii) a proactive implementation based on data analytics; iii)
a hybrid solution that mixes reactive and proactive scaling; iv) use cases and empirical benchmarks, obtained through our platform, that compare
reactive, proactive, and hybrid global scaling performance. From our comparison, proactive global scaling consistently outperforms reactive,
while the hybrid solution is the best-performing one.
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