Towards Trusted Service Monitoring: Verifiable Service Level Agreements

TL;DR

This work tackles the problem of verifiably enforcing SLAs in service ecosystems where providers may misreport metrics. It combines Trusted Execution Environments (TEEs) for secure measurement with zero-knowledge proof (ZKP) based verification via zkVMs to produce auditable, privacy-preserving SLA violation claims anchored in blockchain/IPFS storage. The key contributions include a Verifiable SLA Monitoring Model (VSLAS) that extends WSLA, a system design integrating TEEs, decentralized storage, and zkVM-based evaluation, and a prototype demonstrating scalability to over $10^6$ events per hour with near-constant verification time for single violations. The results show strong integrity, authenticity, and validity guarantees, enabling automated, dispute-resistant SLA enforcement and enabling new cyber-insurance workflows through cryptographic evidence that does not reveal raw telemetry. This approach promises more transparent, resilient service ecosystems and prepares the field for broader adoption of cryptographic SLA evidence in automation and risk management.

Abstract

Service Level Agreement (SLA) monitoring in service-oriented environments suffers from inherent trust conflicts when providers self-report metrics, creating incentives to underreport violations. We introduce a framework for generating verifiable SLA violation claims through trusted hardware monitors and zero-knowledge proofs, establishing cryptographic foundations for genuine trustworthiness in service ecosystems. Our approach starts with machine-readable SLA clauses converted into verifiable predicates and monitored within Trusted Execution Environments. These monitors collect timestamped telemetry, organize measurements into Merkle trees, and produce signed attestations. Zero-knowledge proofs aggregate Service-Level Indicators to evaluate compliance, generating cryptographic proofs verifiable by stakeholders, arbitrators, or insurers in disputes, without accessing underlying data. This ensures three security properties: integrity, authenticity, and validity. Our prototype demonstrates linear scaling up to over 1 million events per hour for measurements with near constant-time proof generation and verification for single violation claims, enabling trustless SLA enforcement through cryptographic guarantees for automated compliance verification in service monitoring.

Figures (4)

• Figure 1: SLA Monitoring Model based on keller2003wsla with Passive and Active Monitor.
• Figure 2: SLI Measurements flow into verifiable SLA compliance claims.
• Figure 3: Stacked Heatmap for P50, P90 and P95 latency for Batch Size vs RPS.
• Figure 4: Verification time vs number of evidences.