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Electrical & Computer Engineering
Master of Science (M.S.)
Year Degree Awarded
Month Degree Awarded
precedence constraints, computational load, cost functions, reward, service, dynamic voltage scaling
Embedded real time applications are often subject to time and energy constraints. Real time applications are usually characterized by logically separable set of tasks with precedence constraints. The computational effort behind each of the task in the system is responsible for a physical functionality of the embedded system. In this work we mainly define theoretical models for relating the quality of the physical func- tionality to the computational load of the tasks and develop optimization problems to maximize the quality of the system subject to various constraints like time and energy. Specifically, the novelties in this work are three fold. This work deals with maximizing the final output quality of a set of precedence constrained tasks whose quality can be expressed with appropriate cost functions. We have developed heuristic scheduling algorithms for maximizing the quality of final output of embedded applications. This work also dealswith the fact that the quality of output of a task in the system has noticeable effect on quality of output of the other dependent tasks in the system. Finally run time characteristics of the tasks are also modeled by simulating a distribution of run times for the tasks, which provides for averaged quality of output for the system rather than un-sampled quality based on arbitrary run times. Many real-time tasks fall into the IRIS (Increased Reward with Increased Service) category. Such tasks can be prematurely terminated at the cost of poorer quality output. In this work, we study the scheduling of IRIS tasks on multiprocessors. IRIS tasks may be dependent, with one task feeding other tasks in a Task Precedence Graph (TPG). Task output quality depends on the quality of the input data as well as on the execution time that is allowed. We study the allocation/scheduling of IRIS TPGs on multiprocessors to maximize output quality. The heuristics developed can effectively reclaim resources when tasks finish earlier than their estimated worst-case execution time. Dynamic voltage scaling is used to manage energy consumption and keep it within specified bounds.
C Mani Krishna