In this thesis, we have proposed an algorithm, named DNA, for dynamically scheduling arriving real-time tasks with PB-based fault-tolerant requirement in a heterogeneous multiprocessor system. Through the dynamic simulation, we have evaluated the performance of the proposed algorithm compared with distance myopic algorithm and FTMA. Finally, in this chapter, we make conclusions and describe some future work about our research.
5.1 Conclusion
The integrated heuristic function proposed in [1] is used by most algorithms which dynamically schedule arriving real-time tasks. The integrated heuristic function emphasizes whether a task could be executed earlier. Nevertheless, real-time tasks are not concerned about when to start computation but rather about meeting deadlines. We propose a new heuristic function, named density, which indicates the tightness of a task. The density function takes account of the schedulable time and the computation time. A task with the highest density means that it is the least flexible to be scheduled so that it will be selected first for scheduling. The simulation results show that the density function selects more appropriate tasks even with a heavy task load.
The MNO strategy for backup scheduling will minimize the processor time reserved for backups. This will also increase the schedulable time for new tasks. Obviously, MNO saves more time than overlapping as much as possible on heterogeneous multiprocessor. Moreover, though simulation, we can find that MNO save more and more time than the EFT strategy when the processor number increases.
Finally, DNA does not need to be adjudged by any input parameters, unlike the distance myopic and FTMA. Though the simulation, DNA gets better results than those of distance
myopic and FTMA which are the best among any combination of needed parameters. This means DNA is more general and suitable for any environment.
5.2 Future Work
In additional to the research results we have proposed, there are some issues in the future work.
First, the assumption of our scheduler model is a dedicated processor for scheduling, and the scheduling overhead is ignored. However, the scheduler may have a lot of idle time if the task load is low. This is not economic for a cost-sensitive system. The scheduler may be used for computation while it is idle as well as scheduling tasks. In this way, the scheduling overhead needs to be taken into account for those tasks scheduled on the scheduler. How to define and quantify the scheduling overhead is not trivial and becomes the next extension of this thesis.
Second, most algorithms assume deadlines of tasks are fixed after they are released, i.e.
deadlines do not vary with time. For some real-time applications whose high-level requirements may change with time, the model of variable deadlines is required. [26] has proposed a new workload model, called the state-dependent deadline model, for this kind of applications. How to modify the density function in our algorithm for the variable deadline model is another future extension of our research.
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