Modeling IaaS Usage Patterns for the Analysis of Cloud Optimization Policies

Abstract

Infrastructure as a Service (IaaS) operators need to balance multiple adversarial goals, such as data center performance and energy efficiency. Automated resource management policies implemented in IaaS Cloud middleware allow the operators to automate trade-off decisions. Simulation-based analyses are viable means to validate that the utilized policies achieve the goals of the operator. For an IaaS operator to perform meaningful analyses, changes in the workload mix of active VMs need to be considered. Current Cloud simulation approaches neglect the influence of VM submissions and terminations, or require the workload to be specified with little abstraction. In this paper, we present a unified approach for modeling IaaS workloads. Our workload model describes the IaaS workload as a sequence of time-triggered, eventdriven external influences. We implement our model as an extension to Palladio and SimuLizar. Finally, we illustrate how historical real-world measurements are leveraged to evaluate resource management policies.