# No Provisioned Concurrency: Fast RDMA-codesigned Remote Fork for Serverless Computing

arXiv: https://arxiv.org/abs/2203.10225 · HTML: https://mlsanity.com/paper/2203.10225/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2022.

## Built on by (most cited first, 8 of 8)

- [ServerlessLLM: Low-Latency Serverless Inference for Large Language Models](https://mlsanity.com/paper/2401.14351/) (2024)
- [Beluga: A CXL-Based Memory Architecture for Scalable and Efficient LLM KVCache Management](https://mlsanity.com/paper/2511.20172/) (2025)
- [KVDirect: Distributed Disaggregated LLM Inference](https://mlsanity.com/paper/2501.14743/) (2024)
- [WarmServe: Enabling One-for-Many GPU Prewarming for Multi-LLM Serving](https://mlsanity.com/paper/2512.09472/) (2025)
- [PipeBoost: Resilient Pipelined Architecture for Fast Serverless LLM Scaling](https://mlsanity.com/paper/2503.17707/) (2025)
- [SpecBox: Speculative Sandbox Scheduling for Efficient LLM Agent Serving](https://mlsanity.com/paper/2607.23933/) (2026)
- [BootSeer: Analyzing and Mitigating Initialization Bottlenecks in Large-Scale LLM Training](https://mlsanity.com/paper/2507.12619/) (2025)
- [AgileLog: A Forkable Shared Log for Agents on Data Streams](https://mlsanity.com/paper/2604.14590/) (2026)

## Abstract (arXiv)

Serverless platforms essentially face a tradeoff between container startup time and provisioned concurrency (i.e., cached instances), which is further exaggerated by the frequent need for remote container initialization. This paper presents MITOSIS, an operating system primitive that provides fast remote fork, which exploits a deep codesign of the OS kernel with RDMA. By leveraging the fast remote read capability of RDMA and partial state transfer across serverless containers, MITOSIS bridges the performance gap between local and remote container initialization. MITOSIS is the first to fork over 10,000 new containers from one instance across multiple machines within a second, while allowing the new containers to efficiently transfer the pre-materialized states of the forked one. We have implemented MITOSIS on Linux and integrated it with FN, a popular serverless platform. Under load spikes in real-world serverless workloads, MITOSIS reduces the function tail latency by 89% with orders of magnitude lower memory usage. For serverless workflow that requires state transfer, MITOSIS improves its execution time by 86%.