An adaptive auto-scaling framework for cloud resource provisioning
Spyridon Chouliaras, Stelios Sotiriadis
Abstract
Cloud computing emerged as a technology that offers scalable access to computing resources in conjunction with low maintenance costs. In this domain, cloud users utilize virtualized resources to benefit from on-demand and long-term pricing strategies. Although the latter consists of a more cost-efficient solution, it requires accurate estimations of future workload demands, which is a challenging task. Furthermore, clouds offer threshold-based auto-scaling rules that need to be manually controlled by the users according to application requirements. Still, tuning scaling parameters is not trivial, since it is mainly based on static scaling rules that may lead to unreasonable costs and quality of service violations. In this work we introduce ADA-RP, an adaptive auto-scaling framework for reliable resource provisioning in the cloud. ADA-RP uses historical time series data for training K-means and convolutional neural networks (CNN) to categorize future workload demands as High, Medium or Low based on CPU utilization. We auto-scale cloud resources in real-time based on the predicted workload demand to reduce costs and improve application performance. The experimental analysis is based on TPC-C runs on MySQL containers deployed on the Google Cloud Platform. Experimental results are prosperous, demonstrating the ability of ADA-RP (i) to reduce MySQL deployment costs by 48% in a single-tenant environment, and (ii) to double the executed queries per second in a multi-tenant environment considering user’s budget requirements.