TrEnv-X: Transparently Share Serverless Execution Environments Across Different Functions and Nodes

Fuente: arXiv

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Bibliographic Details
Main Authors:	Huang, Jialiang, Ma, Teng, Liu, Zheng, Lin, Sixing, Chen, Kang, Jiang, Jinlei, Liao, Xia, Shan, Yingdi, Wu, Yongwei, Zhang, Ning, Lu, Mengting, Ma, Tao, Gong, Haifeng, Zhang, Mingxing
Format:	Preprint
Published:	2025
Subjects:	Distributed, Parallel, and Cluster Computing Operating Systems
Online Access:	Acceder al recurso
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_version_	1866918400758906880
author	Huang, Jialiang Ma, Teng Liu, Zheng Lin, Sixing Chen, Kang Jiang, Jinlei Liao, Xia Shan, Yingdi Wu, Yongwei Zhang, Ning Lu, Mengting Ma, Tao Gong, Haifeng Zhang, Mingxing
author_facet	Huang, Jialiang Ma, Teng Liu, Zheng Lin, Sixing Chen, Kang Jiang, Jinlei Liao, Xia Shan, Yingdi Wu, Yongwei Zhang, Ning Lu, Mengting Ma, Tao Gong, Haifeng Zhang, Mingxing
contents	Serverless computing is renowned for its computation elasticity, yet its full potential is often constrained by the requirement for functions to operate within local and dedicated background environments, resulting in limited memory elasticity. To address this limitation, this paper introduces TrEnv-X, a co-designed integration of the serverless platform with the operating system and CXL/RDMA-based remote memory pools. TrEnv-X's core innovations are repurposable sandboxes, which can be shared across different functions to decrease the associated creation overhead, and OS-level memory templates, which enable rapid state restoration from CXL/RDMA-based remote memory pools. To further demonstrate TrEnv-X's versatility, we generalize its design from traditional containers for microVM-based agent workloads and introduce new optimizations, including browser sharing and a page cache bypassing mechanism. Our evaluation shows that TrEnv-X achieves up to 7x reduction in P99 latency and 48% memory savings for container-based functions. When applied to LLM agents, it reduces the P99 latency by up to 58% and memory usage by 61% compared to state-of-the-art systems like E2B.
format	Preprint
id	arxiv_https___arxiv_org_abs_2509_09525
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	TrEnv-X: Transparently Share Serverless Execution Environments Across Different Functions and Nodes Huang, Jialiang Ma, Teng Liu, Zheng Lin, Sixing Chen, Kang Jiang, Jinlei Liao, Xia Shan, Yingdi Wu, Yongwei Zhang, Ning Lu, Mengting Ma, Tao Gong, Haifeng Zhang, Mingxing Distributed, Parallel, and Cluster Computing Operating Systems Serverless computing is renowned for its computation elasticity, yet its full potential is often constrained by the requirement for functions to operate within local and dedicated background environments, resulting in limited memory elasticity. To address this limitation, this paper introduces TrEnv-X, a co-designed integration of the serverless platform with the operating system and CXL/RDMA-based remote memory pools. TrEnv-X's core innovations are repurposable sandboxes, which can be shared across different functions to decrease the associated creation overhead, and OS-level memory templates, which enable rapid state restoration from CXL/RDMA-based remote memory pools. To further demonstrate TrEnv-X's versatility, we generalize its design from traditional containers for microVM-based agent workloads and introduce new optimizations, including browser sharing and a page cache bypassing mechanism. Our evaluation shows that TrEnv-X achieves up to 7x reduction in P99 latency and 48% memory savings for container-based functions. When applied to LLM agents, it reduces the P99 latency by up to 58% and memory usage by 61% compared to state-of-the-art systems like E2B.
title	TrEnv-X: Transparently Share Serverless Execution Environments Across Different Functions and Nodes
topic	Distributed, Parallel, and Cluster Computing Operating Systems
url	https://arxiv.org/abs/2509.09525