الفهرس 
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Abstract
Several real-time embedded system applications demand predictable
timing behavior and satisfy other system constraints, such as energy
consumption. With the advent of multicore processors in the embedded
market, reducing energy consumption is becoming increasingly important for
multicore processors as well. The situation is critical in portable devices where
the energy budget is limited and hence battery lifetime defines the usefulness
of the system. Therefore energy consumption has become a major concern in
the design of real-time embedded systems. This thesis addresses the issue of
overall energy optimization in real-time embedded systems at the operating
system level using efficient real-time task scheduling algorithms; on the
Dynamic Voltage Frequency Scaling (DVFS)-capable multicore systems.
Initially, a novel workload partitioning algorithm is proposed, namely
Blocking-Aware Based Partitioning (BABP). It statically allocates real-time
tasks with shared resources to a homogenous multicore processor such that the
blocking time of these dependent tasks is significantly reduced. The BABP
algorithm effectively exploits the available parallelism, balances the workload
in these multicore systems, and assigns tasks that can run in parallel to
different cores as much as possible, taking into account blocking-time
minimization. The BABP algorithm reduces an average of 10.5%, 39.0%, and
48.7% of energy consumption compared with SBP, WFD, and BFD
respectively, for short period task sets, and it reduces an average of 11.13%,
25.23% and 51.44% energy consumption compared with SBP, WFD, and BFD
respectively, for long period task sets.