# Fifer: Tackling Underutilization in the Serverless Era

arXiv: https://arxiv.org/abs/2008.12819 · HTML: https://mlsanity.com/paper/2008.12819/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [ServerlessLoRA: Enabling Low-Latency Serverless Multi-LoRA Serving](https://mlsanity.com/paper/2505.14468/) (2025)
- [Accelerating Serverless Computing by Harvesting Idle Resources](https://mlsanity.com/paper/2108.12717/) (2021)

## Abstract (arXiv)

Datacenters are witnessing a rapid surge in the adoption of serverless functions for microservices-based applications. A vast majority of these microservices typically span less than a second, have strict SLO requirements, and are chained together as per the requirements of an application. The aforementioned characteristics introduce a new set of challenges, especially in terms of container provisioning and management, as the state-of-the-art resource management frameworks, employed in serverless platforms, tend to look at microservice-based applications similar to conventional monolithic applications. Hence, these frameworks suffer from microservice-agnostic scheduling and colossal container over-provisioning, especially during workload fluctuations, thereby resulting in poor resource utilization. In this work, we quantify the above shortcomings using a variety of workloads on a multi-node cluster managed by Kubernetes and Brigade serverless framework. To address them, we propose Fifer -- an adaptive resource management framework to efficiently manage function-chains on serverless platforms. The key idea is to make Fifer (i) utilization conscious by efficiently bin packing jobs to fewer containers using function-aware container scaling and intelligent request batching, and (ii) at the same time, SLO-compliant by proactively spawning containers to avoid cold-starts, thus minimizing the overall response latency. Combining these benefits, Fifer improves container utilization and cluster-wide energy consumption by 4x and 31%, respectively, without compromising on SLO's, when compared to the state-of-the-art schedulers employed by serverless platforms.