QoS and efficiency for FaaS platforms

Serverless computing or function-as-a-service (FaaS) provides a way to write applications composed of scalable and manageable independent tasks communicating seamlessly without developer involvement. Strict performance guarantees or service-level agreements (SLAs) provided by cloud vendors demand predictable performance of serverless applications. Performance predictability in a datacenter environment suffers due to contention for hardware resources. In this study, we evaluate the effects of contention on two FaaS platforms; AWS Lambda, an industry leader in serverless, and the open-source OpenFaaS serverless stack. We develop a complete set of microbenchmarks as well as end-to-end applications composed of multiple functions as a benchmark suite to facilitate our study.

We quantify baseline system costs of these applications across both stacks given traditional orchestration mechanisms in an isolated system. We also quantify the same with co-located workloads in datacenter-like setting with Kubernetes orchestration. We show, via experiments, that significant performance slack exists at low to moderate loads and we can intelligently colocate workloads to maximize hardware utilization while still meeting QoS target latencies. Finally, we present a contention-aware static scheduling solution for FaaS platforms with predictable performance and compare it to static versions of baseline related works. We find that an intelligent FaaS orchestrator can be based along similar lines (similar hardware-level features) as a microservices one.