Affinity-Aware Modeling of CPU Usage for Provisioning Virtualized Applications

While virtualization-based systems become a reality, an important issue is that of virtual machine migration-enabled consolidation and dynamic resource provisioning. Mutually communicating virtual machines, as part of migration and consolidation strategies, may get colocated on the same physical machine or placed on different machines. In this work, we argue the need for network affinity-awareness not only in placement but also in resource provisioning for virtual machines. First, we empirically quantify the resource savings due to colocation of communicating virtual machines. We also discuss the increase in resource usage due to dispersion of previously colocated virtual machines. Next, we build models based on different resource-usage micro-benchmarks to predict the resource usages when transitioning from non-colocated placements to colocated placements and vice-versa. These resource usage prediction models are usable along-with consolidation and migration procedures to determine requirements of VMs in colocated and non colocated scenarios. Via extensive experimentation, we evaluate the applicability of our models for synthetic and benchmark application workloads. We find that the models have high prediction accuracy - 90th percentile prediction error within 3% absolute CPU usage for both synthetic and application workloads.