Intelligent Red Deer Algorithm based Energy Aware Load Balancing Scheme for Data Fusion in Cloud Environment

Abstract

A cloud computing (CC) method was effectual if its sources were used in optimal way and an effectual consumption is attained by using and preserving proper management of cloud sources. Resource management can be attained through adoption of powerful source scheduling, allotment, and robust source scalability methods. The balancing of load in cloud is performed at VM level or physical machine level. A task use sources of VM and whenever a bunch of tasks reaches VM, the sources will be exhausted means no source is now existing for handling the extra task requests. This article develops an Intelligent Red Deer Algorithm based Energy Aware Load Balancing Scheme for data fusion in Cloud Environment, called IRDA-EALBS model. The presented IRDA-EALBS model majorly concentrates on the balancing of load among the virtual machines (VMs) in the cloud environment. The IRDA-EALBS model is mainly stimulated from the nature of red deers during a breading period. In addition, the IRDA-EALBS model derived an objective function to minimize energy consumption and maximize makespan. To demonstrate the enhanced performance of the IRDA-EALBS model, a wide range of experimental analyses is carried out. The simulation results highlighted the enhanced outcomes of the IRDA-EALBS model over other load balancers in the cloud environment.