The Linux kernel offers nearly 20,000 configuration options, making it highly customizable but also extremely challenging to manually optimize for performance. The diversity of operating environments and workloads further limits the effectiveness of static or expert-crafted configurations. This paper introduces KconfigTune, an automated tuning system that jointly optimizes Linux kernel configuration options and system-level parameters (e.g., procfs entries). We propose extconfig to expand the parameter search space and, for the first time, address configuration dependencies that arise during automated tuning. To ensure valid and bootable kernels, we design a dependency automatic fix tool and a GRUB-based mechanism. KconfigTune models the tuning process as a machine learning problem and applies Bayesian optimization with a random forest model to efficiently explore the vast and interdependent configuration space. Unlike prior approaches that focus solely on either kernel options or system parameters, KconfigTune achieves deeper integration by jointly tuning both compile-time and runtime behaviors. Experimental results show significant performance improvements, with gains of 18.62% and 19.92% over the default configuration in UnixBench and LEBench tests, respectively. Compared to the state-of-the-art, KconfigTune outperforms by 2.9% and 12.69% in these benchmarks. Ablation studies further confirm that these gains primarily stem from the combined tuning of kernel configurations and system-level parameters.

Hypervisor is a VMM (Virtual Machine Monitor) that creates and runs multiple VMs (Virtual Machines) through abstracting resources from a physical machine. Hypercall is a special and crucial call used in virtualized systems as it serves as a main communication channel between VMs and the hypervisor. However, hypercall attacks occur when an attacker manipulates the communication channel, and it could cause abnormal VM status, potentially leading to the abnormal resource allocation of the host OS (Operating System) and crash of VMs. Therefore, the virtualized system should execute abnormal VM status detection to identify potential abnormal behaviors to protect VMs and the host OS; however, existing works are either for reconstructing the hypervisor or hardware isolation, not for the VM status detection for abnormal hypercall.This study develops a hypercall-oriented abnormal VM status detection system called HypercallDetector based on the following three innovations: 1) we implement a hypercall tracing based on eBPF to obtain the hypercall-related running status (including CPU usage, memory usage, network traffic, etc.) of each VM; 2) we implement a window division technology to divide the VM status into multiple status windows of the same size, and appropriate window size with balanced detection precision (95.0%) and latency (within 8.8 ms) obtained by proposing the window regulator; and 3) we implement a CS-H algorithm (Compressing Sensing for Hypercall) to distinguish whether the VM status is abnormal. HypercallDetector shows higher precision and lower latency than its opponent and consumes only 8.6% CPU of single core and 0.3% memory usage when starting 240 VMs.