We present a detailed evaluation and sensitivity analysis of an energy-conserving, highly scalable variant of the Hadoop Distributed File System (HDFS) called Green-HDFS. GreenHDFS logically divides the servers in a Hadoop cluster into Hot and Cold Zones and relies on insightful data-classification driven energy-conserving data placement to realize guaranteed, substantially long periods (several days) of idleness in a significant subset of servers in the Cold Zone. Detailed lifespan analysis of the files in a large-scale production Hadoop cluster at Yahoo points at the viability of GreenHDFS. Simulation results with real-world Yahoo HDFS traces show that GreenHDFS can achieve 24% energy cost reduction by doing power management in only one top-level tenant directory in the cluster and meets all the scale-down mandates in spite of the unique scale-down challenges present in a Hadoop cluster. If GreenHDFS technique is applied to all the Hadoop clusters at Yahoo (amounting to 38000 servers), $2.1million can be saved in energy costs per annum. Sensitivity analysis shows that energy-conservation is minimally sensitive to the thresholds in GreenHDFS. Lifespan analysis points out that one-size-fits-all energy-management policies won't suffice in a multi-tenant Hadoop Cluster.