--- 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} } ```