Proactive Auto-Scaling Algorithm (PASA) for Cloud Application

Abstract

Application providers (APs) leave their application hosting to cloud with the aim of reducing infrastructure purchase and maintenance costs. However, variation in the arrival rate of user application requests on the one hand, and the attractive cloud resource auto-scaling feature on the other hand, has made APs consider further savings in the cost of renting resources. Researchers generally seek to select parameters for scaling decision making, while it seems that analysis of the parameter history is more effective. This paper presents a proactive auto-scaling algorithm (PASA) equipped with a heuristic predictor. The predictor analyzes history with the help of the following techniques: (1) double exponential smoothing - DES, (2) weighted moving average - WMA and (3) Fibonacci numbers. The results of PASA simulation in CloudSim is indicative of its effectiveness in a way that the algorithm can reduce the AP's cost while maintaining web user satisfaction.