TAQM: Guaranteeing Fairness and Minimizing SLA Violations for Self-Managing Datacenters

Modern cloud datacenters require per-tenant Service Level Agreement (SLA) enforcement for bandwidth allocation and latency guarantees, yet existing Active Queue Management (AQM) approaches guarantee only flow-level fairness, failing to enforce contracted bandwidth ratios. This architectural mismatch between per-tenant billing and per-flow fairness causes systematic SLA violations where tenants opening more flows receive disproportionate bandwidth, requiring manual intervention that cannot scale to thousands of tenants. We present TAQM (Tenant-Aware Active Queue Management), combining per-tenant queueing with weighted fair scheduling for superior tenant-level fairness, and Linear Quadratic Regulator (LQR) control using a novel CUBIC TCP model for autonomous queue optimization. NS-3 evaluation shows TAQM achieves up to 24% better tenant fairness (0.92 vs 0.73 weighted Jain's fairness index) through weight-based enforcement while LQR control autonomously reduces queue occupancy by 51% and delay by 61%. TAQM delivers an autonomous, tenant-aware AQM with superior fairness guarantees and performance isolation.