Reproduce “SNAKE: A Sustainable and Multi-functional Traffic Analysis System utilizing Specialized Large-Scale Models with a Mixture of Experts Architecture” on OpenResearch
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About this paper
Tian Qin, Guang Cheng, Yuyang Zhou, Zihan Chen, Xing Luan
Researchers at Southeast University developed SNAKE, a multi-functional and sustainable traffic analysis system leveraging a Multi-gate Mixture of Experts (MMoE) architecture. This system effectively integrates eight diverse traffic classification tasks, maintaining performance comparable to original independent models while ensuring scalability and adaptability in dynamic network environments.
Key insights
- The MMoE architecture successfully minimizes task interference and mitigates catastrophic forgetting, enabling continuous knowledge expansion in dynamic network settings.
- Configurable gating mechanisms allow for flexible integration of tasks with different relationship patterns, such as independent tasks, category expansion, and category refinement.
- Adopting Transformer encoders as expert sub-models, combined with a unified preprocessing system, provides a robust and high-performing foundation for diverse traffic classification tasks.
Summary by alphaXiv.
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