A Multi-Head Attention Approach for SLA Compliance Monitoring in Data Centers
For data center operators, this framework provides a practical tool to anticipate SLA breaches and minimize penalties, though it is an incremental application of existing transformer methods to a specific domain.
The paper presents a multi-head transformer model that predicts SLA violations in data centers 30 minutes in advance, enabling proactive remediation and reducing financial penalties.
Service level agreements (SLAs) in data center colocation contracts define precise thresholds for power, temperature, and humidity, with tiered violation penalties expressed as credits against monthly recurring charges. Traditional reactive monitoring detects breaches only after they occur, limiting remediation opportunities. We present a framework that encodes SLA rules as structured JSON objects to generate training data without manual annotation. We train a per-customer multi-head transformer model in which each attention head specializes in one SLA rule, learning temporal dependencies that precede violations by 30 minutes. Post-training, the inference service emits structured prediction events transformed into three role-specific views: finance schemas exposing credit liability, operations schemas surfacing risk scores and recommended interventions, and compliance schemas bundling predictions with immutable telemetry signatures for audit. By aligning model architecture directly with contractual obligations, this framework enables operators to anticipate SLA breaches, prioritize corrective actions, and minimize financial penalties.