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True essence of the technology of virtualization is the ability to allow one or more workloads to share the underlying physical resources, thereby bringing about significant cost saving. However, in order to maximize the cost savings from this disruptive technology, it is essential to adopt optimal resource management techniques. These techniques broadly encompass approaches to virtual machine (VM) sizing and placement in a manner that maximizes the physical infrastructure utilization, alongside ensuring that the desired service-level objectives of the candidate workloads are met. In this paper, we propose a novel workload analysis approach for VM placement, which relies on examining the time varying processing demands and variability of the workloads to determine the most optimal placement. Such a solution will result in maximizing infrastructure utilization and ensure that the SLAs of the candidate workloads are met after placement. The technique has been effectively applied to real-life workloads that pertain to SaaS based business platforms offered to clients spread across different geographical locations. A paper based assessment reported over 25% improvement in the overall infrastructure utilization by using the proposed algorithm as compared to other well-known approaches.