Sustainable Serverless Computing With Cold-Start Optimization and Automatic Workflow Resource Scheduling

Shanxing Pan, Hongyu Zhao, Zinuo Cai, Dongmei Li, Ruhui Ma, Haibing Guan

Abstract: In recent years, serverless computing has garnered significant attention owing to its high scalability, pay-as-you-go billing model, and efficient resource management provided by cloud service providers. Optimal resource scheduling of serverless computing has become imperative to reduce energy consumption and enable sustainable computing. However, existing serverless platforms encounter two significant challenges: the cold-start problem of containers and the absence of an effective resource allocation strategy for serverless workflows. Existing pre-warm strategies are associated with high computational overhead, while current resource scheduling techniques inadequately account for the intricate structure of serverless workflows. To address these challenges, we present SSC, a pre-warming and automatic resource allocation framework designed explicitly for serverless workflows. We introduce an innovative gradient-based algorithm for pre-warming containers, significantly reducing cold start hit rates. Moreover, leveraging a critical path and priority queue-based algorithm, SSC enables efficient allocation of resources for serverless workflows. In our experimental evaluation, SSC reduces the cold start hit rate by nearly 50% and achieves substantial cost savings of approximately 30%.

External IDs:dblp:journals/tsusc/PanZCLMG24