Towards Serverless Sky Computing: An Investigation on Global Workload Distribution to Mitigate Carbon Intensity, Network Latency, and Cost

The high demand for energy consumption and the resulting carbon footprint of the cloud pose significant sustainability challenges, as cloud data centers consume vast amounts of energy. The emergence of serverless cloud computing platforms has opened up new avenues for more sustainable cloud computing. Serverless Function-as-a-Service (FaaS) cloud computing platforms facilitate deploying applications as decoupled microservices to leverage automatic rapid scaling, high availability, fault tolerance, and on-demand pricing. The absence of always-on hosting costs associated with virtual machines enables serverless functions to be deployed with many different function configurations and cloud regions to achieve high performance, low network latency, and reduced costs. In this paper, we investigate the utility of a global sky computing platform where serverless resources are aggregated between up to 19 distinct cloud regions. We prototype a serverless load distribution system to distribute client requests across serverless aggregations to minimize performance objectives, including network latency, runtime, hosting costs, and carbon footprint. To evaluate our serverless distribution system's ability to meet performance objectives, we continuously executed large experiments across 19 regions around the world from November 2022 through March 2023. Our serverless load distribution approach using aggregated resources reduced the carbon intensity of a globally distributed serverless application by up to 99.8%, network latency by 65%, or hosting costs by 58% by optimizing function routing to deployments with optimal hardware configurations.