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Serverless computing enables a new way of building and scaling cloud applications by allowing developers to write fine-grained serverless or cloud functions. The execution duration of a cloud function is typically short---ranging from a few milliseconds to hundreds of seconds. However, due to resource contentions caused by public clouds' deep consolidation, the function execution duration may get significantly prolonged and fail to accurately account for the function's true resource usage. We observe that the function duration can be highly unpredictable with huge amplification of more than 50× for an open-source FaaS platform (OpenLambda). Our experiments show that the OS scheduling policy of cloud functions' host server can have a crucial impact on performance. The default Linux scheduler, CFS (Completely Fair Scheduler), being oblivious to workloads, frequently context-switches short functions, causing a turnaround time that is much longer than their service time. We propose SFS (Smart Function Scheduler), which works entirely in the user space and carefully orchestrates existing Linux FIFO and CFS schedulers to approximate Shortest Remaining Time First (SRTF). SFS uses two-level scheduling that seamlessly combines a new FILTER policy with Linux CFS, to trade off increased duration of long functions for significant performance improvement for short functions. We implement SFS in the Linux user space and port it to OpenLambda. Evaluation results show that SFS significantly improves short functions' duration with a small impact on relatively longer functions, compared to CFS.
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This content will become publicly available on July 10, 2025
ALPS: An Adaptive Learning, Priority OS Scheduler for Serverless Functions
FaaS (Function-as-a-Service) workloads feature unique patterns. Serverless functions are ephemeral, highly concurrent, and bursty, with an execution duration ranging from a few milliseconds to a few seconds. The workload behaviors pose new challenges to kernel scheduling. Linux CFS (Completely Fair Scheduler) is workload-oblivious and optimizes long-term fairness via proportional sharing. CFS neglects the short-term demands of CPU time from short-lived serverless functions, severely impacting the performance of short functions. Preemptive shortest job first—shortest remaining process time (SRPT)—prioritizes shorter functions in order to satisfy their short-term demands of CPU time and, therefore, serves as a best-case baseline for optimizing the turnaround time of short functions. A significant downside of approximating SRPT, however, is that longer functions might be starved. In this paper, we propose a novel application-aware kernel scheduler, ALPS (Adaptive Learning, Priority Scheduler), based on two key insights. First, approximating SRPT can largely benefit short functions but may inevitably penalize long functions. Second, CFS provides necessary infrastructure support to implement user-defined priority scheduling. To this end, we design ALPS to have a novel, decoupled scheduler frontend and backend architecture, which unifies approximate SRPT and proportional-share scheduling. ALPS’ frontend sits in the user space and approximates SRPT-inspired priority scheduling by adaptively learning from an SRPT simulation on a recent past workload. ALPS’ backend uses eBPF functions hooked to CFS to carry out the continuously learned policies sent from the frontend to inform scheduling decisions in the kernel. This design adds workload intelligence to workload-oblivious OS scheduling while retaining the desirable properties of OS schedulers. We evaluate ALPS extensively using two production FaaS workloads (Huawei and Azure), and results show that ALPS achieves a reduction of 57.2% in average function execution duration compared to CFS.
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- PAR ID:
- 10556517
- Publisher / Repository:
- 2024 USENIX Annual Technical Conference (ATC 2024)
- Date Published:
- ISBN:
- 978-1-939133-41-0
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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