We present a framework for scheduling multifunction serverless applications over a hybrid public-private cloud. A set of serverless jobs is input as a batch, and the objective is to schedule function executions over the hybrid platform to minimize the cost of public cloud use, while completing all jobs by a specified deadline. As this scheduling problem is NP-Hard, we propose a greedy algorithm that dynamically determines both the order and placement of each function execution using predictive models of function execution time and network latencies. We present a prototype implementation of our framework that uses AWS Lambda and OpenFaaS, for the public and private cloud, respectively. We evaluate our prototype in live experiments using a mixture of compute and I/O heavy serverless applications. Our results show that our framework can achieve a speedup in batch processing of up to 1.92 times that of an approach that uses only the private cloud, at 40.5% the cost of an approach that uses only the public cloud.
@article{arxiv.2006.03720,
title = {Skedulix: Hybrid Cloud Scheduling for Cost-Efficient Execution of Serverless Applications},
author = {Anirban Das and Andrew Leaf and Carlos A. Varela and Stacy Patterson},
journal= {arXiv preprint arXiv:2006.03720},
year = {2020}
}
Comments
10 pages, 5 figures, 2020 IEEE 13th International Conference on Cloud Computing (CLOUD)