  Due to the highly variable execution context in which edge services run,
adapting their behavior to the execution context is crucial to comply with
their requirements. However, adapting service behavior is a challenging task
because it is hard to anticipate the execution contexts in which it will be
deployed, as well as assessing the impact that each behavior change will
produce. In order to provide this adaptation efficiently, we propose a
Reinforcement Learning (RL) based Orchestration for Elastic Services. We
implement and evaluate this approach by adapting an elastic service in
different simulated execution contexts and comparing its performance to a
Heuristics based approach. We show that elastic services achieve high precision
and requirement satisfaction rates while creating an overhead of less than 0.5%
to the overall service. In particular, the RL approach proves to be more
efficient than its rule-based counterpart; yielding a 10 to 25% higher
precision while being 25% less computationally expensive.
