In our modern life, embedded systems are playing an essential role. An embedded system is a computer system embedded into a certain device, in order to achieve computing functions. Beyond all doubt, as a validated system, the functional correctness must be guaranteed. However, for many embedded systems, timeliness also plays an important role in addition to the correctness of the functionalities. For example, in an automotive braking system, the braking function needs to be processed within a limited time duration in order to avoid accidents. Such systems are known as real-time embedded systems.

In these systems, there can be plenty of software programs (called tasks) sharing limited computing resources (e.g. processors, memories). If the system executes tasks in a random way, the whole system will become unpredictable. As a result, the system designers will not be able to verify if the system design can fulfill all the timing requirements or not. In other words, the system is not guaranteed to be safe. Therefore, system designers need to carefully implement algorithms to schedule all the tasks in a predictable manner. Regarding each scheduling algorithm, schedulability analyses are proposed which are used to check if the requirements can be satisfied.

Unfortunately, many real-time systems reserve too much computing resource for the sake of fulfilling timing requirements, without taking into account resource utilization. As a result, system resources cannot be efficiently utilized, which can cause significant resource waste in reality. Therefore, in this thesis, we aim to improve resource utilization in modern distributed real-time embedded systems. We try to tackle this problem from the following two aspects.

1. Investigating tighter timing analyses. Due to the difficulty in performing precise mathematical schedulability analyses, most of the existing analyses include varying degrees of pessimism. In other words, the actual performance of the system can be much better than the predictions. If we can reduce the pessimism in schedulability analyses, we can then admit more workload into the system.
2. Proposing new scheduling frameworks. It is difficult to find a scheduling algorithm which is suitable for all the situations. Therefore, we need different mechanisms to handle specific system characteristics in order to improve the resource utilization.