Load Balancing for Machine Learning Platform in Heterogeneous Distribute Computing Environment

With the recent rapid development of computing power, interest in machine learning research on large data sets is increasing significantly. The machine learning is used in a wide variety of fields, from information retrieval, data mining, and speech recognition to human-computer interaction and application development by non-experts using machine learning platforms. However, there is not enough research on load balancing for distributed systems composed of heterogeneous servers with different performances and architectures that process machine learning tasks. Therefore, in this paper, we propose level hashing-based load balancing applicable to heterogeneous machine learning platforms. The proposed load balancing technique improves the execution time of all machine learning tasks in a machine learning platform by considering the characteristics of machine learning tasks and computing resources of each server.