Auto-scaling web applications in clouds: A cost-aware approach

•We design an auto-scaling mechanism based on the MAPE concept for Web applications.•We enhance the effectiveness of the execution phase of the control MAPE loop with a cost-aware approach.•We provide an innovative solution for overcoming the challenges of delayed VM startup.•We design an executor in order to mitigate oscillation and increase the stability of the mechanism.•We conduct experiments to evaluate the performance of our approach under real-world workload traces for different metrics.

The elasticity feature of cloud computing and its pay-per-use pricing entice application providers to use cloud application hosting. One of the most valuable methods, an application provider can use in order to reduce costs is resource auto-scaling. Resource auto-scaling for the purpose of preventing resource over-provisioning or under-provisioning is a widely investigated topic in cloud environments. The Auto-scaling process is often implemented based on the four phases of MAPE loop: Monitoring (M), Analysis (A), Planning (P) and Execution (E). Hence, researchers seek to improve the performance of this mechanism with different solutions for each phase. However, the solutions in this area are generally focused on the improvement of the performance in the three phases of the monitoring, analysis, and planning, while the execution phase is considered less often. This paper provides a cost saving super professional executor which shows the importance and effectiveness of this phase of the controlling cycle. Unlike common executors, the proposed solution executes scale-down commands via aware selection of surplus virtual machines; moreover, with its novel features, surplus virtual machines are kept quarantined for the rest of their billing period in order to maximize the cost efficiency. Simulation results show that the proposed executor reduces the cost of renting virtual machines by 7% while improves the final service level agreement of the application provider and controls the mechanism's oscillation in decision-making.