Practical Scheduling for Real-World Serverless Computing

TL;DR

This paper introduces Hermes, a novel scheduler for serverless computing that optimizes function execution and load balancing, significantly reducing delays and increasing throughput based on real-world trace analysis.

Contribution

The paper presents a first-principles design of a serverless scheduler, Hermes, incorporating insights from real-world traces to improve performance over existing policies.

Findings

Hermes reduces function slowdown by up to 85%.

Hermes achieves 60% higher throughput.

It effectively minimizes cold starts and head-of-line blocking.

Abstract

Serverless computing has seen rapid growth due to the ease-of-use and cost-efficiency it provides. However, function scheduling, a critical component of serverless systems, has been overlooked. In this paper, we take a first-principles approach toward designing a scheduler that caters to the unique characteristics of serverless functions as seen in real-world deployments. We first create a taxonomy of scheduling policies along three dimensions. Next, we use simulation to explore the scheduling policy space for the function characteristics in a 14-day trace of Azure functions and conclude that frequently used features such as late binding and random load balancing are sub-optimal for common execution time distributions and load ranges. We use these insights to design Hermes, a scheduler for serverless functions with three key characteristics. First, to avoid head-of-line blocking due to high function execution time variability, Hermes uses a combination of early binding and processor sharing for scheduling at individual worker machines. Second, Hermes uses a hybrid load balancing approach that improves consolidation at low load while employing least-loaded balancing at high load to retain high performance. Third, Hermes is both load and locality-aware, reducing the number of cold starts compared to pure load-based policies. We implement Hermes for Apache OpenWhisk and demonstrate that, for the case of the function patterns observed both in the Azure and in other real-world traces, it achieves up to 85% lower function slowdown and 60% higher throughput compared to existing policies.