TASADOR: A Machine Learning Framework for Network Bandwidth to CPU Translations

Abstract

In virtualized cloud environments, meeting the network bandwidth requirements of virtual machines (VMs) is challenging due to the lack of accurate methods for translating target network bandwidth into precise CPU allocations. This translation is non-trivial, as outcomes vary depending on factors such as workloads, message sizes, and hardware configurations. For instance, our experiments reveal that for the same CPU allocation, network bandwidth can vary between $0.4\times$ and $3.5\times$. In this paper, we introduce TASADOR, a machine learning (ML) framework for network bandwidth to CPU translations. TASADOR predicts “appropriate” CPU budgets for new workloads to meet their bandwidth requirements. We demonstrate that TASADOR completes data collection and training in ${\sim }16$ minutes while achieving target bandwidth accurately with ${\sim }6.5\times$ less CPU utilization compared to existing schemes. Our experiments also confirm that TASADOR is effective across diverse workloads, message sizes, and hardware configurations.