• Anushka Kapure

A goal of Cloud service management is to design Self-Adaptive Auto-scalar to react to workload fluctuation and changing the work load assign. Auto-Scaling is the practice of automatically adding or removing resources for an application deployment to meet performance targets in response to changing Infrastructure as a Service (IaaS) provider's deliver threshold based auto-scaling techniques. However, setting up threshold with right values that minimize cast and achieve service level agreement is not an easy task, especially with variant and sudden work load changes. This thesis focus on full fledged Deep-Learning assisted Auto-scaling mechanism which is one of the successful technique as well as self configuring Deep-Learning techniques like Support Vector Machine (SVM), Convolution Neural Network (CNN), etc.

Hybrid auto-scaling on clouds, Threshold base Auto-scaling, Scheduling with SLAs, Machine Learning, Deep Learning, Convolution Neural Network, Support Vector Machine, Cost Effectiveness

"Deep Learning Assisted Auto-Scaling in Cloud Computing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.493-497, May-2019, Available :http://www.jetir.org/papers/JETIR1905P74.pdf

"Deep Learning Assisted Auto-Scaling in Cloud Computing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp493-497, May-2019, Available at : http://www.jetir.org/papers/JETIR1905P74.pdf