FISH: Alleviating Cold Start for Serverless Computing with Memory Constraints

Xinyue Zhang, Guopeng Li, Haisheng Tan

Abstract: Within cloud computing, Function as a Service (FaaS) deploys computations to serverless backends without exposing the underlying server environment. This facilitates high scalability and flexibility for software development. This innovative paradigm introduces a notable challenge: serverless service providers have limited resources, and the computing environment may not always be ready when a stateless function invocation request arrives. Thus, the users have to suffer from an initial latency known as cold start. To alleviate the cold start problem with resource constraints, it is important to manage the life cycle of functions' execution environment to ensure the resources consumption of living containers are under-limit. We present FISH, a Factor-Integrated Scheduler with IAT-Histogram, which enhances the HIST algorithm to improve efficient resource management in serverless computing environments. By employing a histogram of inter-arrival times (IAT) for optimizing pre-warm and keep-alive strategies, and incorporating an IAT-aware eviction policy that considers memory usage and invocation frequency, FISH aims to mitigate the cold start problem under resource constraints. Besides, we implement a simulator and evaluate FISH mainly with a fixed-keep-alive method with Azure Function Invocation Trace and obtain a maximum 32% increase in warm start rate and reduce total cold start time by up to 125% compared to the baseline.