A Design That Incorporates Adaptive Reservation into Mixed-Criticality Systems

Abstract

This paper presents a design and implementation of a Mixed-Criticality System (MCS) extended fromμC/OS III. It is based on a MCS model that uses an adaptive reservation mechanism to cope with the uncertainties in task execution times and to increase the resource utilization in MCS. The implementation takes advantage of the tasks’ recent execution records to predict their required computational resource in the near future and adjusts their reserved budget according to their criticality levels. The designed system focuses on soft real-time tasks. An overrun tolerance algorithm is used to limit the deadline miss ratios between a rise to the task’s actual consumption and the change to the amount of reservation. More than two criticality levels can be handled without introducing obvious additional overhead at each added level. The case study evaluation demonstrates that the reserved resource for each task is always close to its actual consumption; the tasks’ deadline misses are bounded by the different requirements specified by the criticality levels; during overload conditions, high-criticality tasks are guaranteed to have sufficient resource reservation. Although there is still room for improvement if it comes to processing overhead, this research brings some inspirations in both modelling and implementation aspects of MCS.

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Design and analysis of task models for mixed-criticality systems with practical considerations

“…some existing studies such as [51] and [52] consider the run-time allocation of budgets for high critical tasks in LO-crit phase of the system and have shown a significant improvement in schedulability and resource utilization. In dynamic mixed-criticality task and scheduling model [51], a combination of off-line and on-line techniques are used to improve the schedulability of tasks.…”

“…Off-line, they use a schedulability analysis to determine total LO-crit phase budget for all high critical tasks combined and at run-time, they use a strategy to allocate budgets to individual jobs of high critical tasks based on this total budget. In [52] an adaptive reservation mechanism is used to cope up with uncertainties in task execution times owing to the difficulty of determining the exact worst-case Execution Time for the tasks. In this study, tasks' execution history is used to predict their required computational resource in near future, and the reserved budgets are adjusted according to their criticality levels.…”