A multi-model framework to implement self-managing control systems for QoS management
Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
Enacting SLAs in clouds using rules
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
CloudOpt: multi-goal optimization of application deployments across a cloud
Proceedings of the 7th International Conference on Network and Services Management
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Journal of Systems and Software
On estimating actuation delays in elastic computing systems
Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
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Continuously adjusting the horizontal scaling of applications hosted by data centers appears as a good candidate to automatic control approaches allocating resources in closed-loop given their current workload. Despite several attempts, real applications of these techniques in cloud computing infrastructures face some difficulties. Some of them essentially turn back to the core concepts of automatic control: controllability, inertia of the controlled system, gain and stability. In this paper, considering our recent work to build a management framework dedicated to automatic resource allocation in virtualized applications, we attempt to identify from experiments the sources of instabilities in the controlled systems. As examples, we analyze two types of policies: threshold-based and reinforcement learning techniques to dynamically scale resources. The experiments show that both approaches are tricky and that trying to implement a controller without looking at the way the controlled system reacts to actions, both in time and in amplitude, is doomed to fail. We discuss both lessons learned from the experiments in terms of simple yet key points to build good resource management policies, and longer term issues on which we are currently working to manage contracts and reinforcement learning efficiently in cloud controllers.