FaaSt: Optimize makespan of serverless workflows in federated commercial FaaS
Sasko Ristov, Philipp Gritsch · 2022
Nowadays, scientists migrate workflow applications on serverless Function-as-a-Service (FaaS) platforms in a form of so called function choreographies (FCs) to benefit from FaaS high elasticity and instantly spawning numerous functions. How-ever, the heterogeneous nature of federated FaaS overburdens decisions for the most appropriate configuration setup. Unfor-tunately, related work mainly support either (i) scheduling of serverful workflow applications that run on virtual machines or (ii) container-based algorithms to schedule individual functions on specific container (executor). Either approach is hard to implement for FCs in federated FaaS; the former due to specifics of the FaaS deployment model, while the latter because they are primarily focused on bag of functions and reducing startup latency down to microseconds. Such optimization is negligible for scientific FCs whose functions may run hundreds of seconds due to enormous compute and I/O operations to distributed cloud storage. Instead, scientific FCs would benefit from schedulers that select the appropriate FaaS provider, cloud region, and memory settings. To bridge this gap in scheduling scientific FCs, this paper introduces FaaSt, a novel list-based FC scheduler that optimizes makespan of an FC that runs functions in federated FaaS. The evaluation with three other schedulers showed that FaaSt overcomes limitations of a single FaaS region and generates speedup of up to 2.82× when running FCs across four cloud regions compared to a single region. Moreover, FaaSt achieves speedup of up to 1.74 × compared to the other state-of-the-art FC schedulers across the same four regions.