  Function-as-a-Service (FaaS) introduces a lightweight, function-based cloud
execution model that finds its relevance in applications like IoT-edge data
processing and anomaly detection. While CSP offer a near-infinite function
elasticity, these applications often experience fluctuating workloads and
stricter performance constraints. A typical CSP strategy is to empirically
determine and adjust desired function instances, "autoscaling", based on
monitoring-based thresholds such as CPU or memory, to cope with demand and
performance. However, threshold configuration either requires expert knowledge,
historical data or a complete view of environment, making autoscaling a
performance bottleneck lacking an adaptable solution.RL algorithms are proven
to be beneficial in analysing complex cloud environments and result in an
adaptable policy that maximizes the expected objectives. Most realistic cloud
environments usually involve operational interference and have limited
visibility, making them partially observable. A general solution to tackle
observability in highly dynamic settings is to integrate Recurrent units with
model-free RL algorithms and model a decision process as a POMDP. Therefore, in
this paper, we investigate a model-free Recurrent RL agent for function
autoscaling and compare it against the model-free Proximal Policy Optimisation
(PPO) algorithm. We explore the integration of a LSTM network with the
state-of-the-art PPO algorithm to find that under our experimental and
evaluation settings, recurrent policies were able to capture the environment
parameters and show promising results for function autoscaling. We further
compare a PPO-based autoscaling agent with commercially used threshold-based
function autoscaling and posit that a LSTM-based autoscaling agent is able to
improve throughput by 18%, function execution by 13% and account for 8.4% more
function instances.
