The development of 5G enables communication systems to satisfy heterogeneous service requirements of novel applications. For instance, ultra-reliable low latency communication (uRLLC) is applicable for many safety-critical and latency-sensitive scenarios. Many research papers aim to convert the stringent reliability and latency factors to a static data rate requirement. However, in most industrial scenarios, the communication traffic presents short-term/long-term dependency, burst, and non-stationary characteristics. This makes it more challenging to obtain a tight upper bound for the rate requirement of uRLLC. In this work, we introduce a novel solution based on decentralized model predictive control (MPC), where the dynamic incoming communication traffic and the users’ quality of service (QoS) requirements are reformulated into an up-to-date data rate constraint. Under such assumptions, we consider a use case of the resource allocation problem for a single uRLLC network slice. The allocation task is solved by the successive convex approximation (SCA) algorithm for a more in-depth analysis. The simulation results show that the proposed algorithm can deal with non-stationary communication traffic in real-time, as well as provide good performance with guaranteed delay and reliability requirements.