TY - ABST

T1 - Robust and flexible mapping for real-time distributed applications during the early design phases

A1 - Gan,Junhe

A1 - Pop,Paul

A1 - Gruian,Flavius

A1 - Madsen,Jan

AU - Gan,Junhe

AU - Pop,Paul

AU - Gruian,Flavius

AU - Madsen,Jan

PB - Association for Computing Machinery

PY - 2012

Y1 - 2012

N2 - We are interested in mapping hard real-time applications on distributed heterogeneous architectures. An application is modeled as a set of tasks, and we consider a fixed-priority preemptive scheduling policy. We target the early design phases, when decisions have a high impact on the subsequent implementation choices. However, due to a lack of information, the early design phases are characterized by uncertainties, e.g., in the worst-case execution times (wcets), or in the functionality requirements. We model uncertainties in the wcets using the “percentile method”. The uncertainties in the functionality requirements are captured using “future scenarios”, which are task sets that model functionality likely to be added in the future. In this context, we derive a mapping of tasks in the application, such that the resulted implementation is both robust and flexible. Robust means that the application has a high chance of being schedulable, considering the wcet uncertainties, whereas a flexible mapping has a high chance to successfully accommodate the future scenarios. We propose a Genetic Algorithm-based approach to solve this optimization problem. Extensive experiments show the importance of taking into account the uncertainties during the early design phases.

AB - We are interested in mapping hard real-time applications on distributed heterogeneous architectures. An application is modeled as a set of tasks, and we consider a fixed-priority preemptive scheduling policy. We target the early design phases, when decisions have a high impact on the subsequent implementation choices. However, due to a lack of information, the early design phases are characterized by uncertainties, e.g., in the worst-case execution times (wcets), or in the functionality requirements. We model uncertainties in the wcets using the “percentile method”. The uncertainties in the functionality requirements are captured using “future scenarios”, which are task sets that model functionality likely to be added in the future. In this context, we derive a mapping of tasks in the application, such that the resulted implementation is both robust and flexible. Robust means that the application has a high chance of being schedulable, considering the wcet uncertainties, whereas a flexible mapping has a high chance to successfully accommodate the future scenarios. We propose a Genetic Algorithm-based approach to solve this optimization problem. Extensive experiments show the importance of taking into account the uncertainties during the early design phases.

UR - http://www.date-conference.com/front

SN - 9781457721458

BT - DATE '12:Proceedings of the Conference on Design, Automation and Test in Europe

T2 - DATE '12:Proceedings of the Conference on Design, Automation and Test in Europe

SP - 935

EP - 940

ER -