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Cloud Computing Technology provides computing resources as a utility service. The objective is to achieve maximum resource utilization with minimum service delivery time and cost. The main challenge is to balance the virtual machines (VM) load in cloud environment and it requires distributing the load between many virtual machines while avoiding underflow and overflow conditions, which depend on capacity of VMs. In this paper, load balancing of VMs have been done based on Ant Colony Optimization (ACO) and Bat algorithm for underflow and overflow VM identifications respectively. As cloud applications involve huge computations and are highly dynamic in nature, so Directed Acyclic Graph (DAG) files of various scientific workflows have been used as input data during implementation of the proposed methodology. Workflows used for experiments are Cybershake, Genome, Ligo, Montage, Sipht …