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

2025-08-12

AI Summary

To address the challenge of dynamic multi-VM scheduling under hardware resource constraints in software-defined vehicles, this paper proposes a workload-aware hypervisor scenario configuration auto-generation framework. Methodologically, it introduces the first integration of domain-knowledge-guided parameter modeling with deep learning to construct a dynamic QoS prediction model; further, it designs an optimization algorithm that synthesizes chip vendors’ BSPs, heuristic rules, and system-level constraints to generate customized resource allocation schemes. The key contributions are: (1) automated and adaptive VM-level resource allocation, and (2) significant improvements in resource utilization and integration efficiency of in-vehicle virtualization systems. Experimental evaluation on real automotive platforms demonstrates a 32% reduction in development cycle time and a 27% average increase in resource utilization.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Automating hypervisor configuration for resource-constrained automotive systems
• Optimizing VM resource allocation using workload profiling and modeling
• Reducing development time for virtualization in software-defined vehicles

Innovation

• Automated hypervisor scenario generation framework
• VM workload profiling for resource optimization
• Domain-guided and deep learning-based QoS modeling