# Accelerating Serverless Computing by Harvesting Idle Resources

arXiv: https://arxiv.org/abs/2108.12717 · HTML: https://mlsanity.com/paper/2108.12717/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 28 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Learning Scheduling Algorithms for Data Processing Clusters](https://mlsanity.com/paper/1810.01963/) (2018)
- [Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider](https://mlsanity.com/paper/2003.03423/) (2020)
- [Occupy the Cloud: Distributed Computing for the 99%](https://mlsanity.com/paper/1702.04024/) (2017)
- [SeBS: A Serverless Benchmark Suite for Function-as-a-Service Computing](https://mlsanity.com/paper/2012.14132/) (2020)
- [Lambada: Interactive Data Analytics on Cold Data using Serverless Cloud Infrastructure](https://mlsanity.com/paper/1912.00937/) (2019)
- [Fifer: Tackling Underutilization in the Serverless Era](https://mlsanity.com/paper/2008.12819/) (2020)

## Abstract (arXiv)

Serverless computing automates fine-grained resource scaling and simplifies the development and deployment of online services with stateless functions. However, it is still non-trivial for users to allocate appropriate resources due to various function types, dependencies, and input sizes. Misconfiguration of resource allocations leaves functions either under-provisioned or over-provisioned and leads to continuous low resource utilization. This paper presents Freyr, a new resource manager (RM) for serverless platforms that maximizes resource efficiency by dynamically harvesting idle resources from over-provisioned functions to under-provisioned functions. Freyr monitors each function's resource utilization in real-time, detects over-provisioning and under-provisioning, and learns to harvest idle resources safely and accelerates functions efficiently by applying deep reinforcement learning algorithms along with a safeguard mechanism. We have implemented and deployed a Freyr prototype in a 13-node Apache OpenWhisk cluster. Experimental results show that 38.8% of function invocations have idle resources harvested by Freyr, and 39.2% of invocations are accelerated by the harvested resources. Freyr reduces the 99th-percentile function response latency by 32.1% compared to the baseline RMs.