MCP Server for Linux Scheduler Management and Auto optimization
{
"mcpServers": {
"schedcp": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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> WIP: We are building a benchmark for evaluating the optimizations for OS!
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WIP: We are building a benchmark for evaluating the optimizations for OS!
SchedCP is an experimental project that enables AI optimization of Linux kernel schedulers using the sched-ext framework. It provides e2e automatic scheduler selection/synthesis, workload profiling, and performance optimization without any human intervention or guidance.
Paper: SchedCP: Towards Agentic OS
The future is not just about letting AI write code for you; the AI agent should act as your system administrator, able to optimize anything for you automatically, without requiring any manual intervention!
It includes the following tools:
Start optimize any workload with AI by simply run:
autotune/target/release/autotune cc "<your workload command>"
# example for linux build
autotune/target/release/autotune cc "make -C workloads/linux-build-bench/linux clean -j && make -C workloads/linux-build-bench/linux -j"
# example for schbench
autotune/target/release/autotune cc workloads/basic/schbench/schbench
Allow LLM Agent to auto select and config the best scheduler:

Allow LLM Agents to write new schedulers:

The current MCP tools include:
The major dependencies are the dependencies for the sched-ext framework. You can check the github.com/sched-ext/scx for more details.
You also need to install the deps for the workloads you want to optimize.
# Clone with submodules
git clone https://github.com/eunomia-bpf/schedcp
cd schedcp
git submodule update --init --recursive scheduler/scx
# Build schedulers
cd scheduler && make && make install && cd ..
# Build autotune
cd autotune && cargo build --release && cd ..
# Build MCP server
cd mcp && cargo build --release && cd ..
USAGE_GUIDE.md - Complete guide on how to use schedulers
PROJECT_STRUCTURE.md - Detailed project organization
AI_AGENTS.md - AI agent implementation