| --- |
| license: mit |
| task_categories: |
| - other |
| language: |
| - en |
| size_categories: |
| - n<1K |
| tags: |
| - cloud-optimization |
| - aws |
| - telemetry |
| - benchmark |
| - agent-evaluation |
| - synthetic-data |
| - terraform |
| configs: |
| - config_name: default |
| data_files: |
| - split: scenarios |
| path: scenarios_summary.jsonl |
| --- |
| |
| # Synthesized Cloud-Optimization Recommendations |
|
|
| 18 scenarios that pair cloud telemetry with a hand-crafted optimization |
| recommendation. Use them to train models or to evaluate AI agents. |
|
|
| ## Summary |
|
|
| Each scenario has multi-tier telemetry, a Terraform file describing the |
| deployed infrastructure, and a gold-standard recommendation. |
|
|
| The dataset is built around a simple input-output mapping. The input is |
| telemetry plus the infrastructure. The output is an optimization |
| recommendation that says what to change and what the impact will be. |
|
|
| The dataset is synthesized. Telemetry was generated procedurally to match |
| each scenario's narrative. Gold recommendations were hand-crafted and |
| verified. |
|
|
| The dataset uses AWS vocabulary throughout. Instance types, service names, |
| and field names match AWS. This makes the scenarios concrete instead of |
| vendor-neutral. |
|
|
| ## Folder layout |
|
|
| ``` |
| README.md # this file |
| LICENSE # MIT |
| EVAL.md # what eval.py checks |
| eval.py # Floor sanity check (smoke test) |
| sample_predictions.json # worked example of submission shape |
| scenarios_summary.jsonl # one row per scenario (viewer table) |
| scenarios/ |
| 01/ |
| metadata.json # scenario summary + fixtures |
| main.tf # Terraform for the infra |
| compute_telemetry.json # CPU, memory, latency |
| database_telemetry.json # query rates, pool stats |
| cache_telemetry.json # hit rate, eviction |
| network_telemetry.json # bandwidth, packet loss |
| correlation_evidence.json # cross-tier correlations |
| handcrafted_recommendation.json # the gold answer |
| 02/ |
| ... |
| ``` |
|
|
| Each scenario covers a different optimization situation. Some are |
| single-tier (only compute is wrong). Some span tiers (database problem |
| that surfaces in compute). Some are no-action cases. Two are diagnostic |
| deferral cases. One asks for an SLA review instead of an infra change. |
|
|
| ## The summary table (`scenarios_summary.jsonl`) |
| |
| The Hugging Face Dataset Viewer renders `scenarios_summary.jsonl` as a |
| browsable table. Each row is one scenario and includes the headline fields |
| from that scenario's metadata and gold recommendation. |
|
|
| The summary is for discovery only. The full inputs (telemetry, Terraform, |
| correlation evidence) live in `scenarios/NN/`. Always train or evaluate on |
| the full files, not on the summary. |
|
|
| Columns in the summary table: |
|
|
| | Column | Source | |
| |--------------------------------|------------------------------------------------| |
| | `scenario_id` | folder name | |
| | `scenario_name` | metadata.scenario_name | |
| | `scenario_type` | metadata.scenario_type | |
| | `what_this_demonstrates` | metadata.narrative.what_this_demonstrates | |
| | `finding_type` | gold.finding_type | |
| | `primary_tier` | gold.primary_tier | |
| | `secondary_tier` | gold.secondary_tier | |
| | `action_category` | gold.action_category | |
| | `specific_change` | gold.specific_change | |
| | `savings_monthly_usd` | gold.cost_impact.savings_monthly_usd | |
| | `current_monthly_usd` | gold.cost_impact.current_monthly_usd | |
| | `projected_monthly_usd` | gold.cost_impact.projected_monthly_usd | |
|
|
| Some scenarios have negative `savings_monthly_usd`. That is expected. For |
| those scenarios the right action increases cost to fix a performance or |
| reliability problem (for example, adding a read replica). |
|
|
| ## Schema |
|
|
| ### Scenario inputs |
|
|
| Each `scenarios/NN/` folder has these files. |
|
|
| | File | What it is | |
| |-----------------------------------|---------------------------------------------| |
| | `metadata.json` | scenario name, narrative, fixtures | |
| | `main.tf` | Terraform for the deployed infra | |
| | `compute_telemetry.json` | per-window CPU, memory, latency | |
| | `database_telemetry.json` | per-window DB query rate, pool, slow queries| |
| | `cache_telemetry.json` | per-window hit rate, evictions | |
| | `network_telemetry.json` | per-window bandwidth, packet loss | |
| | `correlation_evidence.json` | cross-tier correlation pairs | |
| | `handcrafted_recommendation.json` | the gold answer | |
|
|
| ### Recommendation shape |
|
|
| ```json |
| { |
| "scenario_id": "01", |
| "finding_type": "issue_found", |
| "specific_change": "...", |
| "primary_tier": "compute", |
| "secondary_tier": null, |
| "action_category": "rightsizing", |
| "conclusion": { ... }, |
| "evidence": { |
| "telemetry_observations": [ ... ], |
| "infrastructure_context": [ ... ], |
| "correlation_observations": [ ... ] |
| }, |
| "reasoning": "...", |
| "projected_state": { ... }, |
| "cost_impact": { ... }, |
| "risk_assessment": { ... } |
| } |
| ``` |
|
|
| ### Allowed values |
|
|
| - `finding_type`: `issue_found`, `no_issue_found`, `diagnostic_deferral`, |
| `insufficient_data` |
| - `primary_tier`: `compute`, `database`, `cache`, `network`, `deferred`, |
| or null |
| - `secondary_tier`: same set as `primary_tier` |
| - `action_category`: `rightsizing`, `scaling_policy_change`, |
| `query_cache_optimization`, `cache_capacity_adjustment`, `pool_sizing`, |
| `replica_adjustment`, `load_balancer_reconfiguration`, |
| `network_topology_change`, `sla_review`, or null |
|
|
| The `deferred` tier sentinel is used in diagnostic-deferral scenarios |
| where the agent explicitly cannot pick a tier yet (scenarios 15 and 17). |
| `insufficient_data` is reserved for future scenarios where the dataset |
| is too sparse to support any finding; no current scenario uses it. |
|
|
| ## Scenario coverage |
|
|
| | ID | Type | Description | |
| |----|-------------------|----------------------------------------------------| |
| | 01 | single-tier | compute over-provisioned | |
| | 02 | single-tier | compute peak windows, needs scheduled scaling | |
| | 03 | single-tier | database over-provisioned | |
| | 04 | single-tier | slow queries plus exhausted pool | |
| | 05 | single-tier | ALB round-robin causing uneven CPU | |
| | 06 | no-action | all tiers healthy | |
| | 07 | single-tier | cache hit ratio degraded | |
| | 08 | cross-tier | slow DB queries cascade to compute | |
| | 09 | cross-tier | weekday bimodal peaks, needs scheduled scaling | |
| | 10 | cross-tier | network latency cascades to compute | |
| | 11 | cross-tier | all three tiers over-provisioned | |
| | 12 | mixed | healthy compute, over-provisioned database | |
| | 13 | cross-tier | compute spike strains database | |
| | 14 | cross-tier | compute and database both over-provisioned | |
| | 15 | reliability | 99.99% SLA via over-provisioning | |
| | 16 | mild | partial compute optimization | |
| | 17 | deferral | all tiers rise in lockstep, need more diagnosis | |
| | 18 | mostly healthy | minor compute inefficiency | |
|
|
| ## How to use it |
|
|
| You can use this dataset two ways. |
|
|
| **Train or fine-tune.** Treat each scenario's telemetry plus metadata as |
| input. Use the `handcrafted_recommendation.json` as the target output. |
|
|
| **Evaluate AI agents.** Run your agent on the scenario inputs. Compare its |
| output to the hand-crafted recommendation in that scenario's folder. |
|
|
| ## Quick sanity check |
|
|
| ```bash |
| python eval.py --predictions sample_predictions.json |
| ``` |
|
|
| This runs the bundled Floor sanity check. It confirms your predictions |
| parse, have the required fields, and use allowed category values. It does |
| NOT score recommendation quality. See `EVAL.md` for what is checked. |
|
|
| ## Prediction shape |
|
|
| See `sample_predictions.json` for a worked example. Required fields per |
| prediction: `scenario_id`, `finding_type`, `specific_change`, |
| `primary_tier`, `action_category`. Optional but useful for deeper |
| scoring: `secondary_tier`, `reasoning`, `evidence`, `projected_state`, |
| `cost_impact`, `risk_assessment`. |
|
|
| ## How to score beyond the Floor check |
|
|
| The dataset ships gold answers and a Floor sanity check. It does not ship |
| a quality scorer. Beyond the Floor check, the scoring method is up to |
| you. Common options: |
|
|
| - Exact match on the enum fields (`finding_type`, `primary_tier`, |
| `action_category`). |
| - Keyword or substring checks on `specific_change`. |
| - Semantic similarity on the prose fields. |
| - A custom rubric per scenario, comparing prediction fields against the |
| matching `handcrafted_recommendation.json`. |
|
|
| ## Intended uses |
|
|
| - Train or fine-tune a model that maps cloud telemetry to an optimization |
| recommendation. |
| - Evaluate AI agents on cloud-optimization reasoning. |
| - Compare single-shot vs orchestrated agent designs. |
|
|
| ## License |
|
|
| MIT. See `LICENSE`. |
|
|
| ## Citation |
|
|
| ``` |
| @misc{synthesized_cloud_optimization_recommendations_2026, |
| title = {Synthesized Cloud-Optimization Recommendations}, |
| author = {Alexander Meau}, |
| year = {2026}, |
| version = {1.0.0} |
| } |
| ``` |
|
|
