AI-POWERED INSIGHTS
Man-Made Heuristics Are Dead. Long Live Code Generators!
This paper re-imagines policy design via automated search using LLM-driven code generation, introducing POLICYSMITH. It synthesizes instance-optimal heuristics for systems like web caching and congestion control, demonstrating superior performance on open-source traces and safe integration into the Linux kernel.
Executive Impact: Revolutionizing Policy Design
Automated heuristic generation significantly boosts system performance, reduces manual effort, and enhances adaptability across diverse environments.
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Faster Policy Iteration
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Avg. Performance Gain
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Code Safety Compliance
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Policy Design
Web Caching
Congestion Control
LLM & AI
Our approach re-imagines policy design as an automated search problem, solvable with generative models to produce instance-optimal policy code.
This means that 'intelligence' in systems no longer needs to reside in hard-coded rules or opaque neural network weights. Instead, it could reside in the process by which policies are generated.
The framework, POLICYSMITH, uses LLMs to synthesize instance-optimal policies, generating, evaluating, and evolving policy code offline.
POLICYSMITH discovered cache eviction heuristics that outperform 14 established baselines on standard open-source traces.
For instance, Heuristic A, generated using trace w89, outperformed all baselines on 48% of CloudPhysics traces. Heuristic X did so for 64% of MSR traces.
The framework generated 500 heuristics over 20 iterations, costing approximately $7 for OpenAI API access for 8 runs.
POLICYSMITH generated congestion control heuristics that integrate directly into the Linux kernel, despite highly constrained and safety-critical environments.
The process involved using an eBPF probe for dynamic execution and verification, ensuring 63% of candidates passed the eBPF verifier on the first try.
This demonstrates the feasibility of LLM-driven policy generation in sensitive system components.
Recent advancements in coding agents like FunSearch and AlphaEvolve present a powerful new opportunity for policy design.
These agents use LLM-guided evolutionary computation to synthesize expressive code that maximizes quantitative reward functions.
POLICYSMITH leverages this by using an LLM-based GENERATOR to produce candidate heuristics, guided by a TEMPLATE and CHECKER, and optimized via an EVALUATOR.
48%
Improvement on CloudPhysics Traces
Heuristic A, generated by POLICYSMITH, demonstrated superior performance.
POLICYSMITH Policy Search Loop
Template
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Generator
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Checker
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Evaluator
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Heuristic DB
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Context Specific
The POLICYSMITH framework outlines a clear, iterative process for heuristic discovery.
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Comparing traditional policy design with the POLICYSMITH approach highlights key advantages.
Congestion Control in Linux Kernel
Problem: Generating safe and efficient congestion control algorithms for the Linux kernel is challenging due to strict constraints (e.g., no floating-point ops, specific access patterns) and high safety requirements.
Solution: POLICYSMITH addresses this by isolating decision logic from state management and exposing it to the LLM. It uses an eBPF probe for dynamic execution and verification, ensuring generated code passes the eBPF verifier.
Results: 63% of candidate heuristics compiled successfully on the first try, with an additional 19% after feedback. The policies showed varied performance (23%-98% bandwidth utilization), illustrating the diverse policy space explored safely within the kernel environment.
POLICYSMITH successfully navigates the complexities of kernel-space policy generation.
Quantify Your AI Impact
Our AI-driven policy synthesis can significantly reduce operational overhead and improve system performance. Use this calculator to estimate your potential annual savings and reclaimed human hours.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your Path to Automated Policies
Our structured roadmap ensures a seamless transition to AI-driven policy design, from initial context definition to validated deployment.
Phase 1: Context Definition
Collaborate to define specific workload, hardware, and objective constraints for your initial target system. This includes setting up the TEMPLATE and EVALUATOR.
Phase 2: Automated Policy Synthesis
Leverage POLICYSMITH to generate and iterate on candidate heuristics. We fine-tune the LLM prompts and evolutionary search parameters for optimal results.
Phase 3: Validation & Benchmarking
Thoroughly test synthesized policies against your specific datasets and performance metrics in a simulated or staging environment to ensure robustness and desired outcomes.
Phase 4: Deployment Strategy
Plan for phased deployment, runtime monitoring for context shifts, and a strategy for incremental policy updates or re-synthesis as your system evolves.
Ready to Transform Your System Policies?
Schedule a personalized strategy session with our AI specialists to explore how POLICYSMITH can automate your heuristic design, boost performance, and reduce operational overhead.
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