In recent years, the volume of data on the internet has grown at an unprecedented rate, accompanied by increasing demands for the quality of network services. Simultaneously, this surge in data scale has brought about a substantial increase in concurrent data streams flowing through the network. This frequently results in concurrent resource contention and network congestion, potentially affecting the end users’ experience. While certain load balancing mechanisms can address concurrent competition, they often do so at the expense of time and may not adequately consider the quality of service (QoS). Hence, the primary challenge in contemporary load balancing lies in efficiently addressing concurrent competition issues while optimizing the end user’s terminal experience. In this paper, we present a novel load balancing solution called FaCa-QoS, which incorporates Inband Network Telemetry (INT), leveraging traffic transmission within the network to swiftly obtain a partial global view of network load. Additionally, we propose the Acrossing & Flowing algorithm to mitigate concurrent path competition by introducing an element of randomness to load balancing process while ensuring various traffic needs. Experimental results show that FaCa-QoS reduces congestion by 15% and increases throughput by 22.5%, outperforming existing load-balancing strategies while ensuring a 90% QoS satisfaction rate across various traffic types.