  In cloud environments, load balancing task scheduling is an important issue
that directly affects resource utilization. Unquestionably, load balancing
scheduling is a serious aspect that must be considered in the cloud research
field due to the significant impact on both the back end and front end.
Whenever an effective load balance has been achieved in the cloud, then good
resource utilization will also be achieved. An effective load balance means
distributing the submitted workload over cloud VMs in a balanced way, leading
to high resource utilization and high user satisfaction. In this paper, we
propose a load balancing algorithm, Binary Load Balancing-Hybrid Particle Swarm
Optimization and Gravitational Search Algorithm (Bin-LB-PSOGSA), which is a
bio-inspired load balancing scheduling algorithm that efficiently enables the
scheduling process to improve load balance level on VMs. The proposed algorithm
finds the best Task-to-Virtual machine mapping that is influenced by the length
of submitted workload and VM processing speed. Results show that the proposed
Bin-LB-PSOGSA achieves better VM load average than the pure Bin-LB-PSO and
other benchmark algorithms in terms of load balance level.
