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.

In serverless scenario, function invocation runs in an individual container. Lightweight container technology has significantly reduced the startup latency of container. The library loading process now becomes a critical performance bottleneck of serverless function cold startup. The state-of-the-art approaches leverage the process fork operation to reduce the cold startup latency in serverless computing by reusing the loaded libraries. However, the fork operation can only share libraries between parent process and forked process. For security, the libraries loaded by the parent process should be a subset of those required by the forked process, which limits opportunities to eliminate library loading overhead. To address this problem, we propose theLASSsystem, which enables multiple processes to share initialized libraries in a composable and efficient manner.LASSallows a process to securely reuse libraries loaded by multiple processes, thereby reducing library loading latency to the millisecond level. Compared to the state-of-the-art approaches,LASScan improve average library loading speed by more than 10.3×, and reduce 99thpercentile end-to-end latency by 34%–57%.