This paper presents two fast, best-effort real-time scheduling algorithms called MDASA and MLBESA. MDASA and MLBESA are novel in the way that they heuristically, yet accurately, mimic the behavior of the DASA and LBESA scheduling algorithms, but are faster with On and Onlg n worst-case complexities, respectively. Experimental results show that the performance of MDASA and MLBESA, in general, is close to that of DASA and LBESA, respectively, for a broad range of realistic workloads. However, for a highly bursty workload, MLBESA is found to perform worse than LBESA. Furthermore, the task response times under MDASA and MLBESA are very close to the values under their counterpart scheduling algorithms. Thus, MDASA and MLBESA can substitute for DASA and LBESA algorithms, respectively, in adaptive resource allocation techniques for asynchronous real-time distributed systems where DASA and LBESA have previously been serious bottlenecks on computational costs.

We present an autonomous, mobile, robotics application that requires dynamic adjustments of task execution rates to meet the demands of an unpredictable environment. The Robotic Safety Marker (RSM) system consists of one lead robot, the foreman, and a group of guided robots, called robotic safety markers (a.k.a., barrels). An extensive analysis is conducted of two applications running on the foreman. Both applications require adjusting task periods to achieve desired performance metrics with respect to the speed at which a system task is completed, the accuracy of RSM placement, or the number of RSMs controlled by the foreman. A static priority scheduling solution is proposed that takes into consideration the strict deadline requirements of some of the tasks and their dynamic periods. Finally, a schedulability analysis is developed that can be executed online to accommodate the dynamic performance requirements and to distinguish between safe operating points and potentially unsafe operating points. 1.