Modern cloud infrastructures rely on sharing network resources among multiple independent clients, called tenants. Service providers contractually commit to guaranteeing each tenant a bandwidth allocation defined by a service level agreement (SLA). However, the dominant transport protocol in these environments ,which is TCP, ignores these contracts: it distributes bandwidth proportionally to the number of open flows, not to contractual allocations. A tenant that opens more connections mechanically captures a larger share of network capacity at the expense of its neighbors. This phenomenon, known as the noisy neighbor problem, can cause significant performance degradation in production environments. This thesis addresses the problem at its root, at the network bottleneck, through three contributions. The first derives per-tenant fluid-flow models for the two common TCP variants in modern datacenters - NewReno and CUBIC - directly from their RFC specifications, extending the classical Hollot-Misra framework to the multi-tenant setting and mathematically characterizing the coupling between tenant dynamics through the shared queue. The second contribution presents TAQM (Tenant-Aware Active Queue Management), an architecture that resolves this coupling by separating each tenant's traffic into independent queues with weighted fair scheduling, then synthesizes a per-tenant Linear Quadratic Regulator from the derived models - with mathematical decoupling analysis. The third contribution introduces a trust-based adaptive mechanism that monitors each tenant's traffic predictability online and automatically adjusts the regulator cost matrices, removing the need for any manual parameter configuration and making the system fully autonomous. By integrating control theory with confidence-based adaptation, this work achieves autonomous, tenant-aware network resource management in multi-tenant datacenters, providing mathematical stability guarantees and empirical performance improvements which represents a meaningful step toward self-managing datacenter infrastructure.