Document Type



Doctor of Philosophy (PhD)


Electrical Engineering

First Advisor's Name

Gang Quan

First Advisor's Committee Title

Associate Professor

Second Advisor's Name

Malek Adjouadi

Second Advisor's Committee Title


Third Advisor's Name

Jean H. Andrian

Third Advisor's Committee Title

Associate Professor

Fourth Advisor's Name

Nezih Pala

Fourth Advisor's Committee Title

Assistant Professor

Fifth Advisor's Name

Deng Pan

Fifth Advisor's Committee Title

Associate Professor


Real-Time Scheduling, Multi-Core Platform, Fixed-Priority, Feasibility Analysis, Temperature Formulation, Energy Estimation

Date of Defense



For the past several decades, we have experienced the tremendous growth, in both scale and scope, of real-time embedded systems, thanks largely to the advances in IC technology. However, the traditional approach to get performance boost by increasing CPU frequency has been a way of past. Researchers from both industry and academia are turning their focus to multi-core architectures for continuous improvement of computing performance. In our research, we seek to develop efficient scheduling algorithms and analysis methods in the design of real-time embedded systems on multi-core platforms. Real-time systems are the ones with the response time as critical as the logical correctness of computational results. In addition, a variety of stringent constraints such as power/energy consumption, peak temperature and reliability are also imposed to these systems. Therefore, real-time scheduling plays a critical role in design of such computing systems at the system level.

We started our research by addressing timing constraints for real-time applications on multi-core platforms, and developed both partitioned and semi-partitioned scheduling algorithms to schedule fixed priority, periodic, and hard real-time tasks on multi-core platforms. Then we extended our research by taking temperature constraints into consideration. We developed a closed-form solution to capture temperature dynamics for a given periodic voltage schedule on multi-core platforms, and also developed three methods to check the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research by incorporating the power/energy constraint with thermal awareness into our research problem. We investigated the energy estimation problem on multi-core platforms, and developed a computation efficient method to calculate the energy consumption for a given voltage schedule on a multi-core platform. In this dissertation, we present our research in details and demonstrate the effectiveness and efficiency of our approaches with extensive experimental results.


FI14040815 (4439 kB)
Second round revision (4488 kB)



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