rtos_bench: benchmarking suite to analyse real-time (RT) performance of an operating system

A comprehensive Python framework for benchmarking, analyzing and validating real-time (RT) performance of operating systems. Combines Docker-containerized benchmarks with statistical analysis tools based on Extreme Value Theory (EVT) to determine if a system meets real-time requirements.

Key Features

• Containerized Benchmarks: Run reproducible RT benchmarks (Caterpillar, Cyclictest, iperf3, CODESYS) in Docker or on your host system
• Intel RDT Integration: Full support for Cache Allocation Technology (CAT) and Memory Bandwidth Allocation (MBA)
• Statistical RT Validation: EVT-based analysis with Region of Acceptance (RoA) for probabilistic WCET estimation
• BIOS Collection via Redfish: Automatically capture BIOS settings from BMC/iDRAC before benchmarks
• Jupyter Analysis Notebooks: Interactive reports for analyzing benchmark results and RT readiness

Prerequisites:

1. Install git-lfs link

2. Install uv package manager

curl -LsSf https://astral.sh/uv/install.sh | sh

3. Install packages needed for repository

uv sync

4. Additional system requirements:
   • Docker (for containerized execution)
   • intel-cmt-cat package (for pqos/Intel RDT support)
   • Root access (required for pqos, IRQ affinity, and some metrics)

Quick Start

# Install dependencies and virtual environment (venv)
uv sync
# Build all Docker images first
sudo .venv/bin/python3 main.py run.command=build
# Run a benchmark (e.g. caterpillar)
sudo .venv/bin/python3 main.py run.command=caterpillar
# Analyze results in Jupyter
uv run jupyter-lab

Note: Benchmarks require root access for pqos, IRQ affinity configuration, and hardware monitoring. Use sudo .venv/bin/python3/main.py instead of uv run main.py

How to run jupyter notebook (analysis software)

uv run jupyter-lab

It will prompt you to jupyter lab tab using your default browser, in case it won't you can find link to copy-paste in your browser somewhere here

Then you should open notebooks tab, that's where all the notebooks are stored (analysis reports)

After that you can open any report and run it, just double-click on it like here

Repository structure

.
├── conf/
│ └── config.yaml # Hydra configuration file with experiment parameters
├── caterpillar/
├── cyclictest/
├── iperf3/
├── mega-benchmark/
├── codesys-jitter-benchmark/
├── outputs/ # Where we run experiment bundles ├── notebooks/ # Jupyter notebooks to analyse data ├── src/ # libraries │
├── main.py # Main Python script to launch benchmarks
└── README.md

Configuration

All experiment parameters are controlled via Hydra’s configuration file at:

conf/config.yaml

You can override any configuration parameter from the command line:

sudo .venv/bin/python3 main.py run.command=cyclictest run.t_core="3,5"

Run Configuration

run:
command: "caterpillar"# Benchmark to runt_core: "9,11"# Target CPU coresnuma_node: "1"# NUMA node for cpuset-mems (should be same as NUMA node for t_core)stressor: true # Enable stress workloadmetrics: true # Enable metrics monitoringdocker: true # Run inside Docker containercat_clos_pinning:
enable: true # Pin test PID to CLOSclos: 1# CLOS ID to use

Parameter	Type	Description
run.command	str	Benchmark to run: caterpillar, cyclictest, iperf3, mega-benchmark, codesys-jitter-benchmark, codesys-opcua-pubsub, or build.
run.t_core	str	Target CPU cores for running the benchmark (e.g., "3,5,7,9" or "9,11")
run.numa_node	str	NUMA node for cpuset-mems (should be same as NUMA node for t_core)
run.stressor	bool	Enables additional stress workload during the benchmark
run.metrics	bool	Enable real-time metrics monitoring (CPU temp, IRQs, memory, etc.)
run.docker	bool	Run benchmark inside Docker container (if false, runs on host)
run.cat_clos_pinning.enable	bool	Enable pinning test PID to specified CLOS (caterpillar/cyclictest only)
run.cat_clos_pinning.clos	int	CLOS ID to pin the test process to

Intel RDT/CAT Configuration (pqos)

Configure Intel Resource Director Technology (Cache Allocation Technology, Memory Bandwidth Allocation):

pqos:
interface: "os"# 'os' for resctrl (recommended), 'msr' for direct accessreset_before_apply: true # Reset all allocations before applying new onesclasses:
- id: 1description: "real-time workload"l3_mask: "0x00ff"# L3 cache mask (8 cache ways)l2_mask: "0x00ff"# L2 cache maskmba: 100# Memory Bandwidth Allocation (%)pids: [] # PIDs to assign to this classcores: [] # CPU cores to assign to this class
- id: 0description: "background worker"l3_mask: "0x7f00"# Different cache ways for isolationl2_mask: "0xff00"mba: 10cores: [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]pids: [115, 118]

Parameter	Type	Description
pqos.interface	str	Interface mode: os (resctrl, required for PIDs) or msr (direct)
pqos.reset_before_apply	bool	Reset all allocations before applying new configuration
pqos.classes[].id	int	Class of Service (CLOS) ID
pqos.classes[].l3_mask	str	Hexadecimal L3 cache way mask
pqos.classes[].l2_mask	str	Hexadecimal L2 cache way mask
pqos.classes[].mba	int	Memory Bandwidth Allocation percentage (10-100)
pqos.classes[].cores	list	CPU cores to assign to this CLOS leave empty if not used
pqos.classes[].pids	list	Process IDs to assign to this CLOS leave empty if not used

IRQ Affinity Configuration

Configure IRQ and RCU task affinity to isolate real-time cores:

irq_affinity:
enabled: truehousekeeping_cores: "0-1"# Cores for handling IRQs and RCU

BIOS Settings Collection via Redfish

Automatically collect BIOS settings from servers with Redfish-enabled BMC (e.g., Dell iDRAC) before running benchmarks:

bios:
enable: trueredfish:
host: "192.168.1.100"# BMC/iDRAC IP addressusername: "root"password: "YOUR_PASSWORD"verify_ssl: false # Set to true for valid SSL certificatestimeout: 15output:
format: "json"# Output format: json, yaml, or textfile: "${hydra:run.dir}/bios.json"pretty: true

Parameter	Type	Description
bios.enable	bool	Enable/disable BIOS settings collection
bios.redfish.host	str	BMC/iDRAC hostname or IP address
bios.redfish.username	str	Username for Redfish API authentication
bios.redfish.password	str	Password for Redfish API authentication
bios.redfish.verify_ssl	bool	Verify SSL certificates (set false for self-signed certs)
bios.redfish.timeout	int	Connection timeout in seconds
bios.output.format	str	Output format: json, yaml, or text
bios.output.file	str	Path to save BIOS settings (supports Hydra interpolation)
bios.output.pretty	bool	Enable pretty-printing for JSON output

Test-Specific Configuration

Caterpillar

caterpillar:
n_cycles: 7200# Number of measurement cycles

Cyclictest

cyclictest:
loops: 100000# Number of test loops

Metrics Monitoring

When run.metrics: true, the following monitors collect data during benchmark execution:

Monitor	Output File	Description
CPU Monitor	cpu_monitor.csv	Per-core CPU temperatures
IRQ Monitor	irq_monitor.csv	Interrupt counts per CPU
MemInfo Monitor	meminfo_monitor.csv	Memory statistics from /proc/meminfo
SoftIRQ Monitor	softirq_monitor.csv	Software interrupt statistics
CPUStat Monitor	cpustat_monitor.csv	CPU usage statistics
PQOS Monitor	pqos_monitor.csv	Intel RDT monitoring data

Configure monitoring intervals in the config:

cpu_monitor:
path: "${hydra:run.dir}/cpu_monitor.csv"interval: 1.0

Output Files

Each benchmark run creates a timestamped directory in outputs/ containing:

• output.csv - Benchmark results
• sysinfo.json - System information snapshot (includes Hydra configuration)
• bios.json - BIOS settings (if enabled)
• *_monitor.csv - Various metrics logs (if enabled)
• .hydra/ - Hydra configuration logs

Security Note

⚠️Important: The Redfish password is stored in the configuration file. Consider:

• Using environment variables for sensitive credentials
• Restricting file permissions on config.yaml
• Not committing passwords to version control

References

• Dealing with Uncertainty in pWCET Estimations - Region of Acceptance methodology
• Probabilistic-WCET Reliability - EVT hypothesis validation