Skedulix: Hybrid Cloud Scheduling for Cost-Efficient Execution of Serverless Applications

TL;DR

Skedulix is a hybrid cloud scheduling framework for serverless applications that minimizes costs and meets deadlines by dynamically allocating functions across public and private clouds using predictive models.

Contribution

It introduces a greedy scheduling algorithm for hybrid serverless platforms, optimizing cost and performance with a practical prototype implementation.

Findings

Achieves up to 1.92x speedup over private cloud-only approaches.

Reduces public cloud costs to 40.5% compared to public cloud-only approaches.

Demonstrates effectiveness with live experiments on AWS Lambda and OpenFaaS.

Abstract

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.