Thanks to resource elasticity, cloud systems allow to build high performance applications by dynamically adapting resources to workload dynamics. In this paper, we present a novel approach for horizontally scaling cloud resources. The approach is based on an optimized feedback control scheme that leverages fuzzy logic to self-adjust its parameters in order to cope with unpredictable and highly time-varying public-cloud operating conditions. The proposed approach takes as input heterogeneous monitoring metrics related to distinct aspects of interest (i.e., CPU and network load) merged through a fitness function. Therefore, it is able to accomplish the application needs from different viewpoints. The extensive experimental evaluation performed in the Amazon EC2 environment showed how the proposed approach is robust against a number of realistic workloads-also when VM failures happen- and that it is flexible, as being suitable for applications with different needs. Finally, it also achieves better performance when compared to previously proposed solutions.

Nowadays, more and more the industry and market depend on cloud-based infrastructures for delivering IT services. To this aim cloud-based infrastructures are changing continuously, increasing their complexity especially for the management of cloud resources. Control and management of resources (e.g., virtual machines, VMs) are of paramount importance to adjust resources automatically allocated to an application and for delivering quality-assured services to final users. In this paper, we propose a feedback-based control approach for the management of VMs in the AWS EC2 public cloud. First, we evaluate the proposed Gain Scheduling policy against different workloads. Second, we provide results on the robustness of the proposed Gain Scheduling policy in presence of failures. Finally, we compare our approach to state-of-the-art control approaches for cloud resources. Our results indicate that the proposed control strategy guarantees, without the need of a priori information on system dynamics or complex estimations of the operating conditions, high performance with respect to both constant and time- varying workloads as well as in spite of sudden VM failures.