Trident: A Provider-Oriented Resource Management Framework for Serverless Computing Platforms

Abstract

Serverless computing has become increasingly popular due to its flexible and hassle-free service, relieving users from traditional resource management burdens. However, the shift in responsibility has led to unprecedented challenges for serverless providers in managing virtual machines (VMs) and serving heterogeneous function instances. Serverless providers need to purchase, provision and manage VM instances from IaaS providers, aiming to minimize VM provisioning costs while ensuring compliance with Service Level Objectives (SLOs). In this paper, we propose Trident, a provider-oriented resource management framework for serverless computing platforms. Trident optimizes three major serverless computing provisioning problems for serverless providers: workload prediction, VM provisioning, and function placement. Specifically, Trident introduces a novel dynamic model selection algorithm for more accurate workload prediction. With the prediction results, Trident then carefully designs a hierarchical reinforcement learning (HRL)-based approach for VM provisioning with a mix of types and configurations. To further improve resource utilization, Trident employs an effective collocation placement strategy for efficient function container scheduling. Evaluations on the Azure Function dataset demonstrate that Trident maintains the lowest probability of violating SLOs while simultaneously achieving substantial cost savings of up to 71.8% in provisioning expense compared to state-of-the-art methods from industry and academia.