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arXiv · 1910.08494

DLB: Deep Learning Based Load Balancing

Xiaoke Zhu·Qi Zhang·Taining Cheng·Ling Liu·Wei Zhou·and Jing He
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Abstract

In this paper, we introduce DLB, a Deep Learning based load Balancing mechanism, to effectively address the data skew problem. The key idea of DLB is to replace hash functions in the load balancing mechanisms with deep learning models, which are trained to be able to map different distributions of workloads and data to the servers in a uniform manner. We implemented DLB and deployed it on a practical Cloud environment using CloudSim. Experimental results using both synthetic and real-world data sets show that compared with traditional hash function-based load balancing methods, DLB is able to achieve more balanced mappings, especially when the workload is highly skewed.

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Xiaoke Zhu, Qi Zhang, Taining Cheng, Ling Liu, Wei Zhou, and Jing He. 2019-10-18. DLB: Deep Learning Based Load Balancing. https://doi.org/10.1109/cloud53861

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