SIMPPO: A Scalable and Incremental Online Learning Framework for Serverless Resource Management

Haoran Qiu, Weichao Mao, **Archit Patke**, Chen Wang, Hubertus Franke, Zbigniew T. Kalbarczyk, Tamer Başar, Ravishankar K. Iyer

**SoCC 2022**


Serverless Function-as-a-Service (FaaS) offers improved programmability for customers, yet it is not server-“less” and comes at the cost of more complex infrastructure management (e.g., resource provisioning and scheduling) for cloud providers. To maintain function service-level objectives (SLOs) and improve resource utilization efficiency, recent research has been focused on applying online learning algorithms such as reinforcement learning (RL) to manage resources. Compared to rule-based solutions with heuristics, RL-based approaches eliminate humans in the loop and avoid the painstaking generation of heuristics.

Despite the initial success of applying RL, we first show in this paper that the state-of-the-art single-agent RL algorithm (S-RL) suffers up to 4.8x higher p99 function latency degradation on multi-tenant serverless FaaS platforms compared to isolated environments and is unable to converge during training. We then design and implement a scalable and incremental multi-agent RL framework based on Proximal Policy Optimization (SIMPPO). Our experiments on widely used serverless benchmarks demonstrate that in multi-tenant environments, SIMPPO enables each RL agent to efficiently converge during training and provides online function latency performance comparable to that of S-RL trained in isolation (which we refer to as the baseline for assessing RL performance) with minor degradation (<9.2%). In addition, SIMPPO reduces the p99 function latency by 4.5x compared to S-RL in multi-tenant cases.