Toward Automated Hypervisor Scenario Generation Based on VM Workload Profiling for Resource-Constrained Environments

TL;DR

This paper introduces an automated framework for generating hypervisor configurations in automotive systems by profiling workloads and applying modeling techniques, enhancing resource allocation efficiency in resource-constrained environments.

Contribution

It presents a novel automated scenario generation tool that combines workload profiling, theoretical models, and heuristics to optimize hypervisor configurations for automotive virtualization.

Findings

Improved resource allocation efficiency in automotive hypervisors.

Reduced development time for system integration.

Effective deployment demonstrated in real-world automotive systems.

Abstract

In the automotive industry, the rise of software-defined vehicles (SDVs) has driven a shift toward virtualization-based architectures that consolidate diverse automotive workloads on a shared hardware platform. To support this evolution, chipset vendors provide board support packages (BSPs), hypervisor setups, and resource allocation guidelines. However, adapting these static configurations to varying system requirements and workloads remain a significant challenge for Tier 1 integrators. This paper presents an automated scenario generation framework, which helps automotive vendors to allocate hardware resources efficiently across multiple VMs. By profiling runtime behavior and integrating both theoretical models and vendor heuristics, the proposed tool generates optimized hypervisor configurations tailored to system constraints. We compare two main approaches for modeling target QoS based on profiled data and resource allocation: domain-guided parametric modeling and deep learning-based modeling. We further describe our optimization strategy using the selected QoS model to derive efficient resource allocations. Finally, we report on real-world deployments to demonstrate the effectiveness of our framework in improving integration efficiency and reducing development time in resource-constrained environments.