ARTE: An Application-specific Run-Time managEment framework for multi-cores based on queuing models

• We propose a solution (named ARTE) for the runtime resource allocation in multicores.
• ARTE allows to meet system-level requirements while minimizing the response time.
• ARTE uses a run-time queuing model to evaluate different resource distributions.
• ARTE clearly outperforms state-of-the-art approaches such as maxT and PHPL.
• The introduced overhead is significantly lower than RL presenting comparable results.

This paper presents an Application-specific Run-Time managEment (ARTE) framework to tackle the problem of managing computational resources in an application specific multi-core system. The ARTE framework run-time goal is to minimize applications’ response times while meeting the applications’ computational demands and fitting within the available power budget.The approach addresses application specific embedded systems assuming that the set of target applications is known at design-time. In addition, it considers run-time scenarios that are unpredictable due to variable user activity and/or interaction with the external environment. ARTE takes decisions about resource distribution to the active applications at run-time once the system state is known.ARTE leverages an analytical queuing model at run-time to predict the applications’ response times. The accuracy of this model is enhanced by accounting for contention overhead on the resources shared among the active applications. The analytical nature of the queuing model allows an estimation of the system performance with negligible overhead.Finally we compared the proposed ARTE framework to state-of-the-art techniques to assess its benefits in terms of systems performance and run-time overhead for the selected set of parallel benchmarks.