Computer Science and Engineering, Department of


Date of this Version



2013 IEEE International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing, Pages: 2006 - 2013, DOI: 10.1109/HPCC.and.EUC.2013.289


Copyright © 2013 IEEE. Used by permission.


Recent years, there is an increasing interest of integrating mixed-criticality functionalities onto a shared computing platform in automotive, avionics and the control industry. The benefits of such an integration include reduced hardware cost and high computational performance. Also, new challenges appear as a result of the integration since interferences across tasks with different criticalities are introduced and these interferences could potentially lead to catastrophic results. Failures are likely to be more frequent due to the interferences. Hence, it is becoming increasingly important to deal with faults in mixed-criticality systems. Although several approaches have been proposed to handle failures in mixed-criticality systems, they come either with a high cost due to a hardware replication (spatial redundancy) or with a poor utilization due to reexecution (time redundancy).

In this paper, we study a scheme that provides fault recovery through task reallocations in response to permanent faults in multiprocessor mixed-criticality systems. We present an algorithm to minimize the number of task reallocations while retaining the promise that the most critical applications continue to meet their deadlines. The performance evaluation of the proposed algorithm is carried out by comparing it with two baseline algorithms. In order to evaluate the performance of algorithms from the perspective of mixed-criticality systems, we choose the state of art metric called ductility to formally measure the effects of deadline misses for tasks with different criticality levels. Under this metric, a high-criticality task is considered more important than all low-criticality tasks combined. The simulation results confirm the effectiveness of our proposed algorithm in both minimizing the number of task reallocations and retaining the promised performance of highcriticality tasks.