AQUATOPE is a QoS-and-uncertainty-aware resource manager designed for multi-stage serverless workflows.
The container pool scheduler (src/container_pool_scheduler) and container resource manager (src/container_resource_manager) are implemented in Python3. The following dependencies need to be installed: gevent, pytorch, botorch, gpytorch. For the container pool scheduler, run train_lstm_encoder_decoder.py and train_prediction_network.py to train the hybrid model before deployment. The container resource manager updates the resource configurations of registered workflow containers based on workflow_config file.
Benchmarks are located in benchmark directory. The serverless workflows are created with OpenWhisk Composer. We use Locust as the workload generator to create custom-shaped load based on Azure Function Dataset. The Locust scripts are located in locust directory.
By default, OpenWhisk only supports CPU sharing (in proportion to the allocated memory) and pre-defined static container pool configuration. To enable explicit CPU-limit-based resource scheduling, and dynamic adjustment of prewarmed container pool, #4648 and #4790 need to be merged.
AQUATOPE: QoS-and-Uncertainty-Aware Resource Management for Multi-Stage Serverless Workflows.
@inproceedings{10.1145/3567955.3567960,
author = {Zhou, Zhuangzhuang and Zhang, Yanqi and Delimitrou, Christina},
title = {AQUATOPE: QoS-and-Uncertainty-Aware Resource Management for Multi-Stage Serverless Workflows},
year = {2022},
isbn = {9781450399159},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3567955.3567960},
doi = {10.1145/3567955.3567960},
pages = {1–14},
numpages = {14},
keywords = {resource management, machine learning for systems, quality of service, resource allocation, datacenter, function-as-a-service, serverless computing, Cloud computing, resource efficiency},
location = {Vancouver, BC, Canada},
series = {ASPLOS 2023}
}