BeeHive: Sub-second Elasticity for Web Services with Semi-FaaS Execution
Ziming Zhao, Mingyu Wu, Jiawei Tang, Binyu Zang, Zhaoguo Wang, Haibo Chen · 2023
Function-as-a-service (FaaS), an emerging cloud computing paradigm, is expected to provide strong elasticity due to its promise to auto-scale fine-grained functions rapidly. Although appealing for applications with good parallelism and dynamic workload, this paper shows that it is non-trivial to adapt existing monolithic applications (like web services) to FaaS due to their complexity. To bridge the gap between complicated web services and FaaS, this paper proposes a runtime-based Semi-FaaS execution model, which dynamically extracts time-consuming code snippets (closures) from applications and offloads them to FaaS platforms for execution. It further proposes BeeHive, an offloading framework for Semi-FaaS, which relies on the managed runtime to provide a fallback-based execution model and addresses the performance issues in traditional offloading mechanisms for FaaS. Meanwhile, the runtime system of BeeHive selects offloading candidates in a user-transparent way and supports efficient object sharing, memory management, and failure recovery in a distributed environment. The evaluation using various web applications suggests that the Semi-FaaS execution supported by BeeHive can reach sub-second resource provisioning on commercialized FaaS platforms like AWS Lambda, which is up to two orders of magnitude better than other alternative scaling approaches in cloud computing.