  Current proprietary and open-source serverless platforms follow opinionated,
hardcoded scheduling policies to deploy the functions to be executed over the
available workers. Such policies may decrease the performance and the security
of the application due to locality issues (e.g., functions executed by workers
far from the databases to be accessed). These limitations are partially
overcome by the adoption of APP, a new platform-agnostic declarative language
that allows serverless platforms to support multiple scheduling logics.
Defining the "right" scheduling policy in APP is far from being a trivial task
since it often requires rounds of refinement involving knowledge of the
underlying infrastructure, guesswork, and empirical testing. In this paper, we
start investigating how information derived from static analysis could be
incorporated into APP scheduling function policies to help users select the
best-performing workers at function allocation. We substantiate our proposal by
presenting a pipeline able to extract cost equations from functions' code,
synthesising cost expressions through the usage of off-the-shelf solvers, and
extending APP allocation policies to consider this information.
