In an increasingly self-aware and knowledge-driven software world, understanding the execution behaviour of an application is mandatory for cost-effective delivery. This applies especially to cloud functions, because many complex applications are composed of those functions. Similar short-lived, event-driven processing models can be found inside databases and message brokers. This means systematic tracing of cloud functions should be conducted so that a large variety of applications benefits from rightsizing memory allocation and associated fair microbilling.
We recently reported on our upcoming presentation on generating static trace profiles for cloud functions. In general, tracing techniques help reporting with high precision how much memory an application requires. But in practice, the memory needs depend on a number of factors that change over time. Similar to how static environment variables have been replaced by dynamic queries to updateable key-value stores to increase dynamics, we should be able to produce dynamic traces that show at least a correlation to certain values within the function, like the number of rows in a table. This research blog post shows one approach to do that.
