GreenWhisk: Emission-Aware Computing for Serverless Platform

TL;DR

GreenWhisk is a novel serverless platform that reduces carbon footprint by integrating carbon-aware load balancing and operating in grid-connected and grid-isolated modes, addressing renewable intermittency.

Contribution

This paper introduces GreenWhisk, a carbon-aware serverless platform with new load balancing algorithms that minimize carbon emissions while maintaining performance.

Findings

GreenWhisk reduces carbon footprint without significant performance loss.

It effectively handles renewable intermittency through its dual operation modes.

The platform enables easy integration of new carbon-aware strategies.

Abstract

Serverless computing is an emerging cloud computing abstraction wherein the cloud platform transparently manages all resources, including explicitly provisioning resources and geographical load balancing when the demand for service spikes. Users provide code as functions, and the cloud platform runs these functions handling all aspects of function execution. While prior work has primarily focused on optimizing performance, this paper focuses on reducing the carbon footprint of these systems making variations in grid carbon intensity and intermittency from renewables transparent to the user. We introduce GreenWhisk, a carbon-aware serverless computing platform built upon Apache OpenWhisk, operating in two modes - grid-connected and grid-isolated - addressing intermittency challenges arising from renewables and the grid's carbon footprint. Moreover, we develop carbon-aware load balancing algorithms that leverage energy and carbon information to reduce the carbon footprint. Our evaluation results show that GreenWhisk can easily incorporate carbon-aware algorithms, thereby reducing the carbon footprint of functions without significantly impacting the performance of function execution. In doing so, our system design enables the integration of new carbon-aware strategies into a serverless computing platform.