Cloud computing is used as a backbone infrastructure to meet exponentially increasing computational and storage demands. This increase in service demands in smart cities will result in escalating energy consumption in cloud datacenters. Such a rise in energy consumption will result in upsurge of operational costs and emission of greenhouse gases. In this work, a green cloud computing algorithm named “Cost-based Energy Efficient Scheduling Technique for Dynamic Volage Frequency Scaling (DVFS) Systems (CEEST)” is proposed. The proposed algorithm reduces energy consumption without compromising the quality of service (QoS). The goal of this algorithm is optimization and management of servers in the datacenters by utilizing maximum resources of the servers and powering off the underutilized servers. CEEST utilizes the scaling of virtual machines to finish jobs in the deadlines to reduce violations of service level agreement (SLA). Simulation results prove that the proposed algorithm outperforms the existing algorithms in terms of execution time, energy consumption, resource utilization, and SLA violations. The proposed algorithm saves energy up to 30% in comparison to existing algorithms. The utilization of resources is also significantly increased by to 30%. In terms of SLA violations, the proposed algorithm reduced SLA violations up to 50%.