Date of Award


Document Type


Degree Name

Master of Science


Department of Electrical and Computer Engineering

First Advisor

Gregg Gunsch, PhD


While the cyclic executive and fixed-priority scheduling strategies have been sufficient to handle traditional real- time requirements. they are insufficient for dealing with the complexities of next-generation real-time systems. New methods of intelligent control must be developed for guaranteeing on-time task completion for real-time systems that are faced with unpredictable and dynamically changing requirements. Implementing real-time processes as partial-solution tasks is one technique that may be beneficial. This type of task. when combined with intelligent control, has the potential for increasing pre-runtime schedulability, system maintainability. and runtime robustness. This research investigates the benefits of partial-solution tasks by experimentally measuring the change in performance of 11 simulated real-time systems when converted from all-or-nothing tasks to partial- solution tasks. Results from the experiments indicate that partial-solution tasks have the potential to decrease missed deadlines and increase a systems' average solution quality. The results also suggest that best performance gains can be achieved using Optimistic partial-solution tasks where the bulk of solution quality is achieved early during task execution. The framework used in this research was developed to measure the general case performance characteristics of partial-solution tasks. As a by-product, the research resulted in a framework that can also be used to measure specific case characteristics.

AFIT Designator


DTIC Accession Number