StreamSys: A Lightweight Executable Delivery System for Edge Computing

Edge computing brings several challenges when it comes to data movement. First, moving large data from edge devices to the server is likely to waste bandwidth. Second, complex data patterns (e.g., traffic cameras) on devices require flexible handling. An ideal approach is to move code to data instead. However, since only a small portion of code is required, moving the executable as well as their libraries to the devices can be an overkill. While loading code on demand from remote such as NFS can be a stopgap, but on the other hand leads to low efficiency for irregular access patterns. This article presents <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamSys</small>, a lightweight executable delivery system that loads code on demand by redirecting the local disk IO to the server through optimized network IO. We employ a Markov-based prefetch mechanism on the server side. It learns the access pattern of code and predicts the block sequence for the client to reduce the network round trip. Meanwhile, server-side <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamSys</small> asynchronously prereads the block sequence from the disk to conceal disk IO latency beforehand. Evaluation shows that the latency of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamSys</small> is up to 71.4% lower than the native Linux file system based on SD card and up to 62% lower than NFS in wired environments.

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Publication Details

Journal
IEEE Transactions on Cloud Computing
Published
2024-12-24
DOI
https://doi.org/10.1109/tcc.2024.3521978
Citations
1
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
0.78
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article

StreamSys: A Lightweight Executable Delivery System for Edge Computing

Ju Ren, Yinggang Gao, Zhenya Ma, Yaoxue Zhang et al.
1 citations
IEEE Transactions on Cloud Computing
Cloud Computing and Resource Management
0.78
article

StreamSys: A Lightweight Executable Delivery System for Edge Computing

Ju Ren, Yinggang Gao, Zhenya Ma, Yaoxue Zhang, Sheng Yue, Jun Lu
article en
1 citations

Abstract

Edge computing brings several challenges when it comes to data movement. First, moving large data from edge devices to the server is likely to waste bandwidth. Second, complex data patterns (e.g., traffic cameras) on devices require flexible handling. An ideal approach is to move code to data instead. However, since only a small portion of code is required, moving the executable as well as their libraries to the devices can be an overkill. While loading code on demand from remote such as NFS can be a stopgap, but on the other hand leads to low efficiency for irregular access patterns. This article presents <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamSys</small>, a lightweight executable delivery system that loads code on demand by redirecting the local disk IO to the server through optimized network IO. We employ a Markov-based prefetch mechanism on the server side. It learns the access pattern of code and predicts the block sequence for the client to reduce the network round trip. Meanwhile, server-side <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamSys</small> asynchronously prereads the block sequence from the disk to conceal disk IO latency beforehand. Evaluation shows that the latency of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamSys</small> is up to 71.4% lower than the native Linux file system based on SD card and up to 62% lower than NFS in wired environments.

IEEE Transactions on Cloud ComputingVol. 13(1)
Central South University (CN), Tsinghua University (CN)
Openalex Percentile: Top 18%
Cloud Computing and Resource Management
0.78
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StreamSys: A Lightweight Executable Delivery System for Edge Computing — Ju Ren, Yinggang Gao, et al. · IEEE Transactions on Cloud Computing (2024) | TGRS Research Map | TGRS