Energy Efficient Scheduling for Serverless Systems

TL;DR

This paper introduces an energy-efficient scheduling method for serverless systems that reduces energy consumption while meeting performance requirements, using real-time data and a novel DVFS technique.

Contribution

It presents a new scheduler for serverless computing that optimizes energy use by integrating real-time measurements, historical data, and a novel DVFS approach.

Findings

Significant energy savings demonstrated in experiments.

Scheduler maintains performance while reducing energy consumption.

Effective use of real-time and historical data in scheduling decisions.

Abstract

Serverless computing, also referred to as Function-as-a-Service (FaaS), is a cloud computing model that has attracted significant attention and has been widely adopted in recent years. The serverless computing model offers an intuitive, event-based interface that makes the development and deployment of scalable cloud-based applications easier and cost-effective. An important aspect that has not been examined in these systems is their energy consumption during the application execution. One way to deal with this issue is to schedule the function invocations in an energy-efficient way. However, efficient scheduling of applications in a multi-tenant environment, like FaaS systems, poses significant challenges. The trade-off between the server's energy usage and the hosted functions' performance requirements needs to be taken into consideration. In this work, we propose an Energy Efficient Scheduler for orchestrating the execution of serverless functions so that it minimizes energy consumption while it satisfies the applications' performance demands. Our approach considers real-time performance measurements and historical data and applies a novel DVFS technique to minimize energy consumption. Our detailed experimental evaluation using realistic workloads on our local cluster illustrates the working and benefits of our approach.