The goal of this dissertation is to extend the state of the art in real-time scheduling algorithms to achieve energy efficiency. Currently, Pfair scheduling is one of the few scheduling frameworks which can optimally schedule a periodic real-time taskset on a multiprocessor platform. Despite the theoretical optimality, there exist large concerns about efficiency and applicability of Pfair scheduling in practical situations. This dissertation studies and proposes solutions to such efficiency and applicability concerns. This dissertation also explores temperature aware energy management in the domain of real-time scheduling. The thesis of this dissertation is: the implementation efficiency of Pfair scheduling algorithms can be improved. Further, temperature awareness of a real-time system can be improved while considering variation of task execution times to reduce energy consumption. This thesis is established through research in a number of directions. First, we explore the applicability of Dynamic Voltage and Frequency Scaling (DVFS) feature in the underlying platform, within Pfair scheduled systems. We propose techniques to reduce energy consumption in Pfair scheduling by using DVFS. Next, we explore the problem of quantum size selection in Pfair scheduled system so that runtime overheads are minimized. We also propose a hardware design for a central Pfair scheduler core in a multiprocessor system to minimized the overheads and energy consumption of Pfair scheduling. Finally, we propose a temperature aware energy management scheme for tasks with varying execution times.