Real-time scheduling of embedded applications on multi-core platforms.
详细信息
文摘
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.