Enabling High-Utilization and Low-Contention FaaS: A Request-Level Resource Provisioning Approach

Abstract

Function-as-a-Service offers cost efficiency but often suffers from resource underutilization. This underutilization stems from the instance-level resource provisioning pattern, an issue that existing optimizations have failed to resolve fundamentally. The core problem is that static coarse-grained instance-level resource allocation cannot match the millisecond-level burstiness of dynamic requests. Consequently, it is difficult for current systems to achieve high resource utilization while maintaining high quality of service (QoS) guarantees. To address the problem, this paper advocates a shift to request-level resource provisioning, which redefines the individual request as the atomic unit for scheduling and resource management. We implement this approach in RRP, a scalable FaaS platform that enables efficient per-request resource allocation and release. RRP unifies instance placement and request routing with low-overhead, millisecond-level global visibility. Our evaluation shows that RRP significantly outperforms state-of-the-art instance-level platforms and algorithms. By matching resources to each request’s needs and isolating them from contention, RRP achieves low latency and high utilization. Specifically, on real-world Azure traces, RRP achieves speedups of 1.33 × –30.15 × for average end-to-end latency and 1.37 × –61.46 × for P99 latency, and raises CPU utilization from 44.80%–56.32% to 72.49% under bursty loads.