A Q-learning based auto-scaling approach for provisioning big data analysis services in cloud environments

Currently, analyzing big data to capture hidden values in various fields is one of the most popular research directions. Analytics-as-a-Service (AaaS) providers typically construct a common platform by renting virtual machine (VM) resources from Infrastructure-as-a-Service (IaaS) providers instead of owning their own physical resources, to provision big data analysis services to end users. However, due to the fact that service demands from AaaS users tend to fluctuate over time in reality, how to dynamically adjust the type and quantity of VMs rented from IaaS providers has become an urgent problem to be solved. In this article, we assume that IaaS providers can offer VM resources in two charging modes, and service demand workloads arrive stochastically. We formulate the problem as a Markov decision process and propose a Q-Learning based auto-scaling method, which explores the trade-off between AaaS providers' cost and VM resource utilization. Eventually, we evaluate our method on both real traces and simulated traces and compare it with some existing methods. Experimental results demonstrate that our method can not only achieve the goal of reducing cost, but also ensure the high utilization of VM resources in the long run.