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 June 19, 2024
ACTS: Autonomous Cost-Efficient Task Orchestration for Serverless Analytics
Serverless computing has become increasingly popular
for cloud applications, due to its compelling properties of
high-level abstractions, lightweight runtime, high elasticity and
pay-per-use billing. In this revolutionary computing paradigm
shift, challenges arise when adapting data analytics applications
to the serverless environment, due to the lack of support
for efficient state sharing, which attract ever-growing research
attention. In this paper, we aim to exploit the advantages of task level
orchestration and fine-grained resource provisioning for
data analytics on serverless platforms, with the hope of fulfilling
the promise of serverless deployment to the maximum extent.
To this end, we present ACTS, an autonomous cost-efficient
task orchestration framework for serverless analytics. ACTS
judiciously schedules and coordinates function tasks to mitigate
cold-start latency and state sharing overhead. In addition, ACTS
explores the optimization space of fine-grained workload distribution
and function resource configuration for cost efficiency.
We have deployed and implemented ACTS on AWS Lambda,
evaluated with various data analytics workloads. Results from
extensive experiments demonstrate that ACTS achieves up to
98% monetary cost reduction while maintaining superior job
completion time performance, in comparison with the state-of-the-art baselines.
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- Award ID(s):
- 2019511
- NSF-PAR ID:
- 10431836
- Date Published:
- Journal Name:
- Proceedings of 31st IEEE/ACM International Symposium on Quality of Service (IWQoS 2023)
- Volume:
- 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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