Summary of Adaptive Two-stage Cloud Resource Scaling Via Hierarchical Multi-indicator Forecasting and Bayesian Decision-making, by Yang Luo et al.
Adaptive Two-Stage Cloud Resource Scaling via Hierarchical Multi-Indicator Forecasting and Bayesian Decision-Making
by Yang Luo, Shiyu Wang, Zhemeng Yu, Wei Lu, Xiaofeng Gao, Lintao Ma, Guihai Chen
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes HRAMONY, an adaptive Hierarchical Attention-based Resource Modeling and Decision-Making System, to efficiently allocate cloud computing resources. HARMONY addresses challenges in capturing hierarchical indicator structures, modeling non-Gaussian distributions, and decision-making under uncertainty. It introduces a novel hierarchical attention mechanism that models complex inter-indicator dependencies, enabling accurate predictions that adapt to evolving environment states. The system leverages full predictive distributions in an adaptive Bayesian process, proactively incorporating uncertainties to optimize resource allocation while meeting SLA constraints. Extensive evaluations across four large-scale cloud datasets demonstrate HARMONY’s state-of-the-art performance, significantly outperforming nine established methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HARMONY is a new way to manage computer resources on the internet. It helps big companies save money and time by making smart decisions about how they use their computers. The system uses special tools to understand complex patterns and make predictions about what will happen next. This helps it make better choices about how to use the resources, like deciding when to turn off or turn on a computer. The paper shows that HARMONY works really well and can save companies a lot of money. |
Keywords
* Artificial intelligence * Attention
