DLB: Deep Learning Based Load Balancing
Published in The 14th International Conference on Cloud Computing (CLOUD), 2021
Recommended citation: Xiaoke Zhu, Qi Zhang, Taining Cheng, Ling Liu, Wei Zhou, and Jing He. 2021. DLB: Deep Learning Based Load Balancing. In CLOUD
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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 uniformed 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.