A Heuristic Clustering-Based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environment

摘要

Aiming at the current problems that most physical hosts in the cloud data center are so overloaded that it makes the whole cloud data center’ load imbalanced and that existing load balancing approaches have relatively high complexity, this paper has focused on the selection problem of physical hosts for deploying requested tasks and proposed a novel heuristic approach called Load Balancing based on Bayes and Clustering (LB-BC). Most previous works, generally, utilize a series of algorithms through optimizing the candidate target hosts within an algorithm cycle and then picking out the optimal target hosts to achieve the immediate load balancing effect. However, the immediate effect doesn’t guarantee high execution efficiency for the next task although it has abilities in achieving high resource utilization. Based on this argument, LB-BC introduces the concept of achieving the overall load balancing in a long-term process in contrast to the immediate load balancing approaches in the current literature. LB-BC makes a limited constraint about all physical hosts aiming to achieve a task deployment approach with global search capability in terms of the performance function of computing resource. The Bayes theorem is combined with the clustering process to obtain the optimal clustering set of physical hosts finally. Simulation results show that compared with the existing works, the proposed approach has reduced the failure number of task deployment events obviously, improved the throughput, and optimized the external services performance of cloud data centers.