Multi-dimensional Admission Control and Capacity Planning for IaaS Clouds with Multiple Service Classes

Abstract

Infrastructure as a Service (IaaS) providers typically offer multiple service classes to deal with the wide variety of users adopting this cloud computing model. In this scenario, IaaS providers need to perform efficient admission control and capacity planning in order to minimize infrastructure costs, while fulfilling the different Service Level Objectives (SLOs) defined for all service classes offered. However, most of the previous work on this field consider a single resource dimension -- typically CPU -- when making such management decisions. We show that this approach will either increase infrastructure costs due to over-provisioning, or violate SLOs due to lack of capacity for the resource dimensions being ignored. To fill this gap, we propose admission control and capacity planning methods that consider multiple service classes and multiple resource dimensions. Our results show that our admission control method can guarantee a high availability SLO fulfillment in scenarios where both CPU and memory can become the bottleneck resource. Moreover, we show that our capacity planning method can find the minimum capacity required for both CPU and memory to meet SLOs with good accuracy. We also analyze how the load variation on one resource dimension can affect another, highlighting the need to manage resources for multiple dimensions simultaneously.