Online graph based latency estimation of microservice applications in a FAAS environment

Sammanfattning: Function-as-a-Service (FaaS) is an increasingly common platform for many kinds of applications and services, replacing the need to maintain and setup hardware or virtual machines to host functionality in the cloud. The billing model for FaaS is commonly based on actual usage, which makes the ability to estimate the performance and latency of an application before invoking it valuable. This thesis evaluates if previously defined algorithms for offline latency estimation, can be adapted to work with online data. Performing online estimation of latency potentially enables cheaper estimations, as no extra executions are neccessary, and latency estimation of applications and functions that can not be executed spu- riously. The experiments show that for a set of test applications, the previously defined algorithms can achieve greater than 95% accuracy, and that a non-graph based estimation using exponential moving average can achieve greater than 98% accuracy.