CASA: A Framework for SLO- and Carbon-Aware Autoscaling and Scheduling in Serverless Cloud Computing

Serverless cloud computing is an emerging cloud computing paradigm that can reduce user costs but has stringent performance requirements, due to the need to execute short-duration functions in a timely manner and a growing carbon footprint. Traditional carbon-reducing techniques in serverless cloud platforms such as shutting down idle containers can cause higher violation rates of service level objectives (SLOs). Conversely, traditional latency-reduction methods of prewarming containers can improve performance but increase the associated carbon footprint of serverless cloud platforms. We propose a novel carbon-and SLO-aware framework called CASA to schedule and autoscale containers in a serverless cloud cluster. Experimental results indicate that CASA reduces the operational carbon footprint of a serverless cluster by up to 2.6× while reducing the average SLO violation rate by up to 1.4× compared to the state-of-the-art.