A Hierarchical Receding Horizon Algorithm for QoS-driven control of Multi-IaaS Applications

Cloud Computing is emerging as a major trend in ICT industry. However, as with any new technology, new major challenges lie ahead, one of them con- cerning the resource provisioning. Indeed, modern Cloud applications deal with a dynamic context that requires a continuous adaptation process in order to meet sat- isfactory Quality of Service (QoS) but even the most titled Cloud platform provide just simple rule-based tools; the rudimentary autoscaling mechanisms that can be carried out may be unsuitable in many situations as they do not prevent SLA vio- lations, but only react to them. In addition, these approaches are inherently static and cannot catch the dynamic behavior of the application. This situation calls for advanced solutions designed to provide Cloud resources in a predictive and dy- namic way. This work presents capacity allocation algorithms, whose goal is to minimize the total execution cost, while satisfying some constraints on the average response time of Cloud based applications. We propose a receding horizon con- trol technique, which can be employed to handle multiple classes of requests. An extensive evaluation of our solution against an Oracle with perfect knowledge of the future and well-known heuristics presented in the literature is provided. The analysis shows that our solution outperforms the heuristics producing results very close to the optimal ones, and reducing the number of QoS violations (in the worst case we violated QoS constraints for only 8 minutes over a day versus up to 260 minutes of other approaches). Furthermore, a sensitivity analysis over two differ- ent time scales indicates that finer grained time scales are more appropriate for spiky workloads, whereas smooth traffic conditions are better handled by coarser grained time scales. Our analytical results are validated through simulation, which shows also the impact on our solution of Cloud environment random perturbations. Finally, experiments on a prototype environment demonstrate the effectiveness of our approach under real workloads.