In the past decade, there has been a steady increase in the focus on green initiatives for data centers. Various energy efficiency measures have been proposed and adopted, however the optimal tradeoff between performance and energy efficiency of data centers is yet to be achieved. Addressing this issue, we present APEnergy, an Application Profile-based energy efficient framework for small to medium scale data centers. The proposed framework leverages information on the completed application with certain workloads in the data center to build profiles for workflows. The framework utilizes a novel scheduler to obtain a near-optimal mapping for placement of workflow tasks in the data center based on three criteria including CPU utilization, power cost and task completion time. We compare the performance of the proposed scheduler to similar RTC and HEFT schedulers. Extensive simulation studies are carried out to verify the scalability and efficiency of APEnergy framework. Results show that the proposed Scheduler is 2% and 14% more energy efficient than RTC and HEFT respectively.