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.

A Fuzzy Approach Based on Heterogeneous Metrics for Scaling Out Public Clouds

Grimaldi, Domenico;
2017

Abstract

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.
autoscaling; Cloud computing; cloud control; cloud monitoring; fuzzy logic; PID; QoS/QoE metrics; Signal Processing; Hardware and Architecture; Computational Theory and Mathematics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/285288
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