A Self-Training Algorithm for Load Balancing in Cluster Computing

This paper proposes a self-training algorithm for load balancing in cluster computing. It uses load information including CPU load, memory usage and network traffic to decide the load of each node and combines this information with properties of each job, including CPU bound, memory bound and I/O bound features, that are extracted from the previous runs of these jobs. The proposed algorithm is compared to another algorithm that only uses the load information of each node for the purpose of load balancing. The performance evaluation results show that the proposed algorithm performs well.