Queue-Length-Based Offloading for Delay Sensitive Applications in Federated Cloud-Edge-Fog Systems

Delay-sensitive applications demand ultra-low latency, which can be achieved by leveraging edge and fog computing to provide computation services closer to users. However, the server capacity limitation of edge and fog computing necessitates offloading to balance the computation load across cloud, edge, and fog servers. While previous works typically focus on the average delay of users' requests and employ probabilistic offloading schemes based on certain probabilities, our study introduces a novel approach that considers the QoS violating probability and offloads users' requests based on the queue length of computing servers. We propose an approximate model with closed-form solutions to determine the near-optimal offloading thresholds of the queue lengths at fog and edge servers. Although the performance results of the approximate queueing model do not precisely match the simulation results, our numerical findings demonstrate that they share the same trend, and the approximate queueing model can provide the optimal queue length threshold in most cases. Moreover, our numerical results reveal that the QoS violating probability of the queue-length-based offloading is significantly lower than that of the probabilistic offloading scheme, with potential reductions of up to 60% in QoS violations in large-scale network scenarios.