Mode Transition for Online Scheduling of Adaptive Real-Time Systems on Multiprocessors

This paper presents a novel online scheduling algorithm for scheduling real-time adaptive systems in which tasks may have distinct resource requirements for each of thesystems' operating modes. Apart from prior work that considers only step-wise adaptation of tasks' resource utilization during mode transition, the proposed algorithm (named EAGLE-T)enables tasks to adapt their resource utilization progressively from one mode to another in a timely manner without causing any deadline miss. The upper bound of the delay and the drift between resource utilization achieved by EAGLE-T and the ideal scheduler during mode transition are provided. Performance evaluation shows that the progressive adaptation of EAGLE-Toffers improved performance over a step-wise approach (average maximal-utilization drift and mode-transition delay are reduced by up to 68.75% and 32.16%, respectively). As the probability of a mode change or the number of tasks vary, empirical results show that the resource utilization achieved by tasks scheduled using EAGLE-T is within 56% to 90% of the desired utilization(compared to 11%-81% when the step-wise scheme is used).