The question requires tracking costs for distinct systems running on dedicated Compute Engine instances in the same GCP project. The optimal solution involves accurately associating costs with each system. Option B is correct because:

1. Labeling instances: Assigning labels (e.g., system=prod-app) to Compute Engine instances allows GCP to track resource usage metadata, including costs, based on these labels. Labels are designed for cost allocation and categorization.
2. BigQuery billing export: Exporting billing data to BigQuery captures detailed cost information, including labels. Querying BigQuery for costs grouped by the system-specific label provides precise, granular cost breakdowns per system.

Option A is incorrect because the Cost Breakdown section in the GCP Console relies on pre-existing labels or other dimensions but does not inherently group costs by a custom "system" without labels being applied first. It is a visualization tool, not a setup step.

Option C is incorrect because Stackdriver Logging (now Cloud Logging) exports logs, not billing data. Metadata on instances is not integrated with billing exports, making it ineffective for cost tracking.

Option D is suboptimal because:

• VM names are not designed for cost grouping; they lack the structured key-value format of labels.
• Querying costs based on VM names in BigQuery would require complex string parsing and is error-prone (e.g., inconsistent naming).
• Exporting to Cloud Storage first adds unnecessary complexity compared to direct BigQuery billing exports.

Thus, labeling and BigQuery billing export (Option B) is the GCP best practice for cost allocation.