Serverless computing has seen widespread adoption in public cloud environments. However, it continues to suffer from long cold start latency, which remains a key performance bottleneck. We have conducted an in-depth investigation of existing cold start optimizations and evaluated their effectiveness in large-scale industrial deployments. Our study reveals several common limitations in prior research: (1) reliance on simplified assumptions that overlook the complexities of large-scale systems; (2) a narrow focus on optimizing isolated components of the cold start process, while ignoring end-to-end workflow interactions; and (3) insufficient attention to the challenges introduced by concurrent execution environments. As a result, despite incorporating prior techniques, cold start latency on the Ant Group serverless platform remains in the range of hundreds of milliseconds to several seconds.
This paper identifies three previously overlooked sources of latency: (1) control path latency, stemming from interactions within the serverless runtime; (2) resource contention latency, arising under high concurrency and sustained execution; and (3) user code initialization latency, which reflects the trade-off between resource efficiency and startup performance. To address these challenges, we propose a suite of novel techniques that overcome key limitations in existing approaches. These techniques are designed to be both adaptable to real-world workloads and scalable to large deployments. Our system, AFaaS (short for Ant FaaS), reduces cold start latency to the millisecond level. AFaaS has been deployed in production for over 18 months and has consistently demonstrated stable performance at scale.