Function-as-a-Service (FaaS) is becoming increasingly prevalent, and the number of daily service requests in our system ByteDance has even reached 49.6 billion. These requests exhibit “3H” characteristics:high concurrency,high burstiness, andhigh fluctuation, causing the instance scheduling system to face severe bottlenecks even earlier than the data plane.In this paper, inspired by the idea of on-demand scaling of FaaS applications, we proposeGalaxy, a highly scalable instance scheduling system for FaaS platforms.Galaxydecouples the instance scheduling system into functions and introduces a top controller to dynamically adjust the number of instances for each function based on the request load, effectively eliminating bottlenecks under high concurrent requests. We evaluateGalaxywith real-world workloads and the experimental results show thatGalaxycan complete the scaling of over 89,935 instances within 1 second, excluding cold start overhead.Galaxyincreases the scheduling throughput by 8.6 ~ 12.7× compared to the existing control plane.

Logging-based fault-tolerant methods have recently been widely used in serverless computing, which can provide exactly-once execution semantics for stateless functions. However, this would also introduce considerable system overhead in serverless platforms, especially for the function workflow scenarios. How to build an efficient fault-tolerant logging system for latency-sensitive stateful functions is still challenging. We find the prior synchronous logging design causing the workflow block on waiting for every log request, hinders the performance of workflow execution. In this paper, we present DoubleTrack, a serverless runtime system for fault-tolerant workflows. The key of DoubleTrack is the asynchronous shared log, which decouples fault-tolerant logging and workflow execution, thus mitigating the overhead of synchronous logging. Our evaluation with real-world workloads shows that compared to the synchronous logging approach, DoubleTrack achieves up to 45.8% end-to-end latency and 46.5% logging overhead reduction.