Prediction-Based Admission Control for IaaS Clouds with Multiple Service Classes.

There is a growing adoption of cloud computing services, attracting users with different requirements and budgets to run their applications in cloud infrastructures. In order to match users' needs, cloud providers can offer multiple service classes with different pricing and Service Level Objective (SLO) guarantees. Admission control mechanisms can help providers to meet target SLOs by limiting the demand at peak periods. This paper proposes a prediction-based admission control model for IaaS clouds with multiple service classes, aiming to maximize request admission rates while fulfilling availability SLOs defined for each class. We evaluate our approach with trace-driven simulations fed with data from production systems. Our results show that admission control can reduce SLO violations significantly, specially in underprovisioned scenarios. Moreover, our predictive heuristics are less sensitive to different capacity planning and SLO decisions, as they fulfill availability SLOs for more than 91% of requests even in the worst case scenario, for which only 56% of SLOs are fulfilled by a simpler greedy heuristic and as little as 0.2% when admission control is not used.