Purpose
Summary
Godon addresses the complex challenge of continuous dinfrastructure optimization in dynamic environments through multi-objective combinatorial optimization with an emphasis on meta-heuristic algorithms. It augments human operators by mechanizing performance tuning, optimizing, rearranging, calibrating of technology settings across stacks The overall focus is on open technologies first.
Conception
Modern infrastructure operations—whether on-premise, cloud-native, or hybrid—face an inherently dynamic environment where traffic patterns change, loads are volatile, and service interactions evolve rapidly.
This complexity often leads to neglected performance tuning due to:
- Intractable complexity as the main challenge
- Lacking resources in terms of engineering knowledge or capacity
This complexity is not less pronounced in cloud environments despite its managed nature because there:
- Elastic scaling creates constantly changing optimization targets
- Multi-cloud deployments introduce heterogeneous infrastructure challenges
- Managed services limit configuration visibility while requiring optimization
- Cost-performance tradeoffs become more critical with pay-as-you-go models
Godon approaches this as a continuous multi-objective combinatorial optimization problem that spans all infrastructure environments:
- On-premise systems requiring traditional performance tuning
- Cloud deployments with elastic, distributed architectures
- Hybrid setups bridging local and cloud resources
Further Godon makes stand out that:
- Meta-heuristics (e.g. Evolutionary Algorithms) excel in such complex optimization problem fields
- Optimization spans the entire lifecycle of technology instances
Capabilities and Caveats
What godon is - Capabilities
| Capability | Description |
|---|---|
| Human Augmentation | Augments operations engineers in achieving performance improvements through standardization and industrialization |
| Knowledge Simplification | Reduces prior knowledge needed about configuration changes and their implications |
| Toil Reduction | Decreases engineering hours spent on manual tuning tasks |
| Performance Optimization | Addresses the widespread neglect of broader performance tuning initiatives |
| Operational Complement | Serves as a pragmatic operations engineering complementing instrument |
| Open Technology Focus | Prioritizes open technologies in optimization approaches |
| Dynamic Adaptation | Approximates optimal states in continuously changing environments |
| Algorithm Exploration | Leverages metaheuristics algorithms of all kinds to explore combinatorial configuration spaces |
| Performance Acceleration | Utilizes parallelization and acceleration techniques for metaheuristics |
| Acceleration Hardware Optionality | Acceleration Hardware like GPUs might be leveraged for acceleration, but are not a mandatory requirement, unlike many ML or generative ML approaches |
What godon is not - Caveats
| Caveat | Description |
|---|---|
| Human Supervision Required | Not fully hands-off automation - requires human setup, supervision, and planning |
| ML-Light Approach | Not primarily a machine learning or data analysis oriented technology |
| Minimal ML Usage | Machine learning components kept to minimum |
| Targeted ML Implementation | ML ideally only used when needed in metaheuristic implementation details |
| No Global Optimum Guarantee | Does not guarantee finding global optimum in configuration search space |
| Approximate Solutions | Focuses on approximating better-than-untouched states rather than perfect optimization |
| Application, Not Framework | Functions as an application of metaheuristics rather than providing a metaheuristics framework |