Serverless computing is an emerging cloud computing paradigm that can reduce costs for cloud providers and their customers. However, serverless cloud platforms have 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 such as shutting down idle containers can reduce performance by increasing cold-start latencies of containers required in the future. This can cause higher violation rates of service level objectives (SLOs). Conversely, traditional latency-reduction approaches of prewarming containers or keeping them alive when not in use can improve performance but increase the associated carbon footprint of the serverless cluster platform. To strike a balance between sustainability and performance, in this paper, we propose a novel carbon- and SLO-aware framework called CASA to schedule and autoscale containers in a serverless cloud computing cluster. Experimental results indicate that CASA reduces the operational carbon footprint of a FaaS cluster by up to 2.6x while also reducing the SLO violation rate by up to 1.4x compared to the state-of-the-art.

翻译：无服务器计算是一种新兴的云计算范式，能够为云服务提供商及其客户降低成本。然而，无服务器云平台具有严格的性能要求（由于需要及时执行短时函数），并且其碳足迹也在不断增长。传统的碳减排技术（例如关闭空闲容器）可能会降低性能，因为这会增加未来所需容器的冷启动延迟，从而导致服务级别目标（SLO）的违反率升高。相反，传统的延迟降低方法（如预热容器或在未使用时保持其存活状态）可以提高性能，但会增加无服务器集群平台相关的碳足迹。为了在可持续性与性能之间取得平衡，本文提出了一种新颖的碳与SLO感知框架CASA，用于在无服务器云计算集群中调度和自动扩缩容器。实验结果表明，与现有最先进方法相比，CASA可将FaaS集群的运行碳足迹降低高达2.6倍，同时将SLO违反率降低高达1.4倍。