Vadara: Predictive Elasticity for Cloud Applications

Elasticity is a key feature in cloud computing, and perhaps what distinguishes it from other computing paradigms. Despite the advantages of elasticity, realizing its full potential is hard due to multiple challenges stemming from the need to estimate workload demand. A desirable solution would require predicting system workload and allocating resources a priori, i.e., A predictive approach. Instead, what is mainly available are reactive solutions, requiring difficult parameter tuning. Since each Cloud Provider (CP) has its own implementation idiosyncrasies, it's impossible for developers to: (i) learn only about one platform and re-use that knowledge in others, (ii) migrate developed elasticity solutions between different CPs, and (iii) to develop reusable predictive elasticity rules or algorithms. This paper makes three contributions to provide an effective elasticity environment. First, Vadara, a totally generic elasticity framework, that transparently connects and abstracts several CPs API behaviour, and enables the use of pluggable CP-agnostic elasticity strategies. Second, it presents a predictive workload forecasting approach, which ensembles several individual forecasting methods, and introduces a padding system based on the most recent prediction errors for both under- and over-provisioning. Finally, results show (1) Vadara's successful connection to well-known CPs, (2) the improvements made regarding under- and over-provisioning due to our padding system, and (3) the effectiveness of our ensemble forecasting technique.