Data-driven scheduling in serverless computing to reduce response time

TL;DR

This paper proposes data-driven heuristics for scheduling functions in serverless computing to reduce response times, demonstrating significant improvements over traditional scheduling methods through simulation on real Azure Functions data.

Contribution

It introduces new scheduling heuristics based on local call frequency and execution time data, tailored for FaaS environments, improving response times.

Findings

Significant response time reduction compared to FIFO and round-robin.

Effective adaptation of theoretical scheduling algorithms like SEPT and SERPT.

Validation using real Azure Functions trace data.

Abstract

In Function as a Service (FaaS), a serverless computing variant, customers deploy functions instead of complete virtual machines or Linux containers. It is the cloud provider who maintains the runtime environment for these functions. FaaS products are offered by all major cloud providers (e.g. Amazon Lambda, Google Cloud Functions, Azure Functions); as well as standalone open-source software (e.g. Apache OpenWhisk) with their commercial variants (e.g. Adobe I/O Runtime or IBM Cloud Functions). We take the bottom-up perspective of a single node in a FaaS cluster. We assume that all the execution environments for a set of functions assigned to this node have been already installed. Our goal is to schedule individual invocations of functions, passed by a load balancer, to minimize performance metrics related to response time. Deployed functions are usually executed repeatedly in response to multiple invocations made by end-users. Thus, our scheduling decisions are based on the information gathered locally: the recorded call frequencies and execution times. We propose a number of heuristics, and we also adapt some theoretically-grounded ones like SEPT or SERPT. Our simulations use a recently-published Azure Functions Trace. We show that, compared to the baseline FIFO or round-robin, our data-driven scheduling decisions significantly improve the performance.