In recent years, the SDN (Software-Defined Networking) paradigm emerged as an easy way to manage large-scale network infrastructures through programmability brought out and its control plane/data plane decoupling logic. This enables infrastructure and service providers to have a global view of the network and track traffic flows from a remote controller. However, congestion control remains a concern due to the evolution of increasingly complex and resource-intensive user requirements (virtual reality, metaverse, Internet of Things (IoT), Artificial Intelligence (AI), Cloud, ...) on network infrastructures. This server state leads to high latency in request processing and data loss. This paper proposes in such controller-supervised environment, a congestion management scheme within network service servers to maintain acceptable quality of service. The strategy relies on work stealing to ensure better workload balancing. Simulations show that the proposed solution can reduce congestion load into the servers by up to 22%, depending on request grain size, within a shorter latency than other works in the literature. Moreover, the proposed solution allows stolen tasks to be completed within a shorter timeframe.