Model predictive control: theory and practice—a survey
Automatica (Journal of IFAC)
Behavioural Skeletons in GCM: Autonomic Management of Grid Components
PDP '08 Proceedings of the 16th Euromicro Conference on Parallel, Distributed and Network-Based Processing (PDP 2008)
Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud
IEEE Transactions on Parallel and Distributed Systems
Empirical prediction models for adaptive resource provisioning in the cloud
Future Generation Computer Systems
Analyzing the impact of elasticity on the profit of cloud computing providers
Future Generation Computer Systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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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.