One of the major challenges in the High Performance Computing (HPC) clusters, Data Centers, and Cloud Computing paradigms is intelligent power management to improve energy efficiency. The key contribution of the presented work is the modeling of a Power Aware Job Scheduler (PAJS) for HPC clusters, such that the: (a) threshold voltage is adjusted judiciously to achieve energy efficiency and (b) response time is minimized by scaling the supply voltage. The key novelty in our work is utilization of the Dynamic Threshold-Voltage Scaling (DTVS) for the reduction of cumulative power utilized by each node in the cluster. Moreover, to enhance the performance of the resource scheduling strategies in first part of the work, independent tasks within a job are scheduled to most suitable Computing Nodes (CNs). First, our research analyzes and compares eight scheduling techniques in terms of energy consumption and makespan. Primarily, the most suitable Dynamic Voltage Scaling (DVS) level adhering to the deadline is identified for each of the CNs by the scheduling heuristics. Afterwards, the DTVS is employed to scale down the static, as well as dynamic power by regulating the supply and bias voltages. Finally, the per node threshold scaling is used attain power saving. Our simulation results affirm that the proposed methodology significantly reduces the energy consumption using the DTVS. The work is further extended and the effect of task consolidation is studied and analyzed. By consolidating the tasks on a fewer number of servers the overall power consumed can be significantly reduced. The tasks are first allocated to suitable servers until all the tasks are exhausted. The idle servers are then turned off by using DTVS. The Virtual Machine (VM) monitor checks for under-utilized, partially filled, over-utilized, and empty servers. The VM monitor then migrates the tasks to suitable servers for execution if a set of conditions is met. By this way, many servers those were under-utilized get free and are turned off by using DTVS to save power. Simulations results confirm our study and a substantial reduction in the overall power consumption of the cloud data center is observed.