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529b5a7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | """Medium task: Mid-size audit — 15 resources, mix of prod (skip) and dev/staging (optimize)."""
from env.models import ActionType
from env.tasks.base_task import BaseTask
class MidSizeAuditTask(BaseTask):
task_id = "mid-size-audit"
difficulty = "medium"
max_steps = 20
description = (
"Audit a mid-size company's mixed prod/dev AWS account with 15 resources. "
"Must identify which resources to optimize and which to leave alone. "
"Prod resources are critical and should be skipped."
)
data_file = "medium_account.json"
optimal_cost = 1700.0 # approximate after optimizations
def get_correct_actions(self) -> dict[str, ActionType]:
return {
# --- PROD: SKIP (5 resources) ---
# prod-web-primary: 5% CPU but seasonal spikes, critical -> skip
"res-med-001": ActionType.skip_resource,
# prod-db-replica: read replica for failover, critical -> skip
"res-med-002": ActionType.skip_resource,
# prod-db-primary: active prod DB, critical -> skip
"res-med-003": ActionType.skip_resource,
# prod-api-gateway: in ASG, 65% CPU, critical -> skip
"res-med-004": ActionType.skip_resource,
# prod-main-alb: active with 4 targets, critical -> skip
"res-med-005": ActionType.skip_resource,
# --- DEV/STAGING: OPTIMIZE (10 resources) ---
# dev-ml-training: r5.2xlarge @ 3% -> rightsize to t3.medium
# saves $367.92 - $30.37 = $337.55
"res-med-006": ActionType.rightsize_resource,
# staging-batch-worker: m5.2xlarge @ 6% -> rightsize to t3.medium
# saves $280.32 - $30.37 = $249.95
"res-med-007": ActionType.rightsize_resource,
# dev-k8s-cluster: 8 nodes @ 10% util, runs 24/7 -> schedule weekday-only
# saves ~28% of $633.64 = $177.42
"res-med-008": ActionType.schedule_uptime,
# dev-test-db: db.r5.xlarge + 500GB @ 2%, 5GB used -> rightsize to db.t3.micro + 20GB
# saves $407.90 - ($12.41 + 20*0.10) = $407.90 - $14.41 = $393.49
"res-med-009": ActionType.rightsize_resource,
# staging-data-lake: 20TB S3 standard rarely accessed -> lifecycle policy
# saves ~70% of $460.00 = $322.00
"res-med-010": ActionType.add_lifecycle_policy,
# staging-unused-alb: 0 targets -> terminate
# saves $22.27
"res-med-011": ActionType.terminate_resource,
# dev-jenkins-ci: m5.xlarge @ 15% -> rightsize to t3.medium
# saves $140.16 - $30.37 = $109.79
"res-med-012": ActionType.rightsize_resource,
# dev-monitoring: t3.xlarge @ 8% -> rightsize to t3.small
# saves $121.47 - $15.18 = $106.29
"res-med-013": ActionType.rightsize_resource,
# staging-cache-server: RI expiring, still needed -> purchase_reservation
# saves ~30% of $91.98 = $27.59 (RI discount)
"res-med-014": ActionType.purchase_reservation,
# cloudwatch-log-exports: 3TB S3 never accessed -> lifecycle policy
# saves ~70% of $69.00 = $48.30
"res-med-015": ActionType.add_lifecycle_policy,
}
def get_critical_resources(self) -> set[str]:
return {"res-med-001", "res-med-002", "res-med-003", "res-med-004", "res-med-005"}
def get_action_savings(self) -> dict[str, float]:
return {
"res-med-001": 0.0, # skip (prod)
"res-med-002": 0.0, # skip (prod)
"res-med-003": 0.0, # skip (prod)
"res-med-004": 0.0, # skip (prod)
"res-med-005": 0.0, # skip (prod)
"res-med-006": 337.55, # r5.2xlarge -> t3.medium
"res-med-007": 249.95, # m5.2xlarge -> t3.medium
"res-med-008": 177.42, # schedule K8s weekday-only
"res-med-009": 393.49, # db.r5.xlarge -> db.t3.micro
"res-med-010": 322.00, # S3 lifecycle
"res-med-011": 22.27, # terminate unused ALB
"res-med-012": 109.79, # m5.xlarge -> t3.medium
"res-med-013": 106.29, # t3.xlarge -> t3.small
"res-med-014": 27.59, # purchase reservation
"res-med-015": 48.30, # S3 lifecycle
}
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