Towards a systematic approach to the dynamic adaptation of structured parallel computations using model predictive control

Adaptiveness is an essential feature for distributed parallel applications executed on dynamic environments like Grids and Clouds. Being adaptive means that parallel components can change their configuration at run-time (by modifying their parallelism degree or switching to a different parallel variant) to face irregular workload or to react to uncontrollable changes of the execution platform. A critical problem consists in the definition of adaptation strategies able to select optimal reconfigurations (minimizing operating costs and reconfiguration overhead) and achieve the stability of control decisions (avoiding unnecessary reconfigurations). This paper presents an approach to apply Model Predictive Control (a form of optimal control studied in Control Theory) to adaptive parallel computations expressed according to the Structured Parallel Programming methodology. We show that predictive control is amenable to achieve stability and optimality by relying on the predictability of structured parallelism patterns and the possibility to express analytical cost models of their QoS metrics. The approach has been exemplified on two case-studies, providing a first assessment of its potential and feasibility.