Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider

TL;DR

This paper analyzes the real-world workload of Azure Functions, revealing diverse invocation patterns, and proposes a resource management policy that reduces cold starts and resource usage.

Contribution

It provides the first comprehensive characterization of production FaaS workloads and introduces a practical policy to optimize resource provisioning.

Findings

Most functions are invoked infrequently

Invocation frequencies vary over 8 orders of magnitude

Proposed policy reduces cold starts and resource costs

Abstract

Function as a Service (FaaS) has been gaining popularity as a way to deploy computations to serverless backends in the cloud. This paradigm shifts the complexity of allocating and provisioning resources to the cloud provider, which has to provide the illusion of always-available resources (i.e., fast function invocations without cold starts) at the lowest possible resource cost. Doing so requires the provider to deeply understand the characteristics of the FaaS workload. Unfortunately, there has been little to no public information on these characteristics. Thus, in this paper, we first characterize the entire production FaaS workload of Azure Functions. We show for example that most functions are invoked very infrequently, but there is an 8-order-of-magnitude range of invocation frequencies. Using observations from our characterization, we then propose a practical resource management policy that significantly reduces the number of function coldstarts,while spending fewerresources than state-of-the-practice policies.