Reconfiguration stability of adaptive distributed parallel applications through a cooperative predictive control approach

Distributed parallel applications executed on heterogeneous and dynamic environments need to adapt their configuration (in terms of parallelism degree and parallelism form for each component) in response to unpredictable factors related to the physical platform and the application semantics. On emerging Cloud computing scenarios, reconfigurations induce economic costs and performance degradations on the execution. In this context, it is of paramount importance to define smart adaptation strategies able to achieve properties like control optimality (optimizing the application global QoS) and reconfiguration stability, expressed in terms of number of reconfigurations and the average time for which a configuration is not modified. In this paper we introduce a methodology to address this issue, based on Control Theory and Optimal Control foundations. We present a first validation of our approach in a simulation environment, outlining its effectiveness and feasibility.