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SplitPlace

Open source BSD-3-Clause Python
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Container Management Edge Computing ML Frameworks

About SplitPlace

SplitPlace is an AI-driven container orchestration framework for resource-constrained mobile edge environments. It dynamically schedules and places large-scale neural network applications across distributed edge setups with limited memory and legacy devices. The framework decides between semantic and layer-wise splitting strategies to meet latency and accuracy requirements. Built on the COSCO framework, SplitPlace leverages co-simulation and surrogate optimization techniques, supports multi-armed bandit-based decision making, and offers schedulers such as energy-latency optimization. It is implemented in Python, distributed under the BSD-3-Clause license, and published in IEEE Transactions on Mobile Computing.

Platforms

Web Self-hosted

Languages

Python

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Source Code
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imperial-qore
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README.md

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License Python 3.7, 3.8 Hits SplitPlace-Benchmarks
Docker pulls mnist_layer Docker pulls fashionmnist_layer Docker pulls cifar100_layer Docker pulls mnist_semantic Docker pulls fashionmnist_semantic Docker pulls cifar100_semantic

SplitPlace Framework

SplitPlace is a container orchestration framework for dynamic scheduling and decision making in resource constrained edge environments. SplitPlace decides whether to use semantic or layer wise splits of neural network applications with latency and accuracy critical user requirements on distributed setups with low memory legacy devices.

Quick Start Guide

SplitPlace is based on the COSCO Framework and uses the co-simulation and surrogate optimization primitives of COSCO. To run the framework, install required packages using

python3 install.py

To run the code with the required scheduler, modify lines 81 and 85 of main.py to one of the several options.

decider = MABDecider()
...
scheduler = GOBIScheduler('energy_latency_'+str(HOSTS))

To run the simulator, use the following command

python3 main.py

Wiki

Access the wiki for installation instructions and replication of results.

Links

Items Contents
Paper https://ieeexplore.ieee.org/document/9780535
Pre-print https://arxiv.org/pdf/2205.10635.pdf
Documentation https://github.com/imperial-qore/COSCO/wiki
Video (coming soon)
Contact Shreshth Tuli (@shreshthtuli)
Funding Imperial President's scholarship, H2020-825040 (RADON)

Cite this work

Our work is published in IEEE TMC journal. Cite using the following bibtex entry.

@article{tuli2021splitplace,
  author={Tuli, Shreshth and Casale, Giuliano and Jennings, Nicholas R.},
  journal={IEEE Transactions on Mobile Computing}, 
  title={{SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments}}, 
  year={2022}
}

License

BSD-3-Clause. Copyright (c) 2020, Shreshth Tuli. All rights reserved.

See License file for more details.