Int. J. of Big Data Intelligence   »   2016 Vol.3, No.3

 

 

Title: Auto-scale: automatic scaling of virtualised resources using neuro-fuzzy reinforcement learning approach

 

Authors: T. Veni; S. Mary Saira Bhanu

 

Addresses:
Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India
Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India

 

Abstract: Auto-scaling is an indispensable mechanism which facilitates automatic provisioning and de-provisioning of computing resources on-the-fly in accordance with fluctuating cloud service demands. The success of cloud computing necessitates that auto-scaling should improve both resource utilisation and end-user's quality-of-service. The dynamic and bursty workloads, interferences among virtual machines complicate the resource scaling process. In existing literature, threshold and reinforcement learning-based approaches are employed to enforce autoscaling policy. The threshold-based approach requires expertise knowledge of service-domains and reinforcement learning based approach suffers from the problem of 'curse-ofdimensionality'. In order to address these issues, a neuro-fuzzy reinforcement learning-based resource scaling approach is proposed to automatically adapt resource-scaling process to workload dynamics by considering both SLA constraints and resource utilisation. This approach requires no prior knowledge of service domains and is implemented on Xen-virtualised environment and tested with highly dynamic benchmark workload-RUBiS. Experimental results demonstrate that the proposed approach outperforms existing approaches.

 

Keywords: auto-scaling; cloud computing; reinforcement learning; virtualisation; neuro-fuzzy function approximation; workload dynamics; automatic scaling; virtualised resources; neural networks; fuzzy logic; cloud services.

 

DOI: 10.1504/IJBDI.2016.078400

 

Int. J. of Big Data Intelligence, 2016 Vol.3, No.3, pp.145 - 153

 

Available online: 10 Aug 2016

 

 

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