Tail Latency Optimized Resource Allocation in Fogbased 5G Networks
Shaowen Zheng, Zhenxiang Gao, Shan Hua Xu, Weihua Zhou, Yongming Wang · 2018
Meeting the stringent latency requirements for delay-sensitive scenarios is a challenge in 5G Networks. Fog computing can reduce the end to end latency considerably by extending data processing capabilities to the network edge. However, the “soft real-time” characteristic of fog-based 5G networks may intensify the effect of long-tail on latency-critical scenarios, which are very sensitive to tail latency. To alleviate the long-tail latency, an online resource allocation algorithm with low complexity is proposed in this paper. With this algorithm, latency based dynamic resource allocation can be achieved through the cooperation of monitor and orchestrator of fog controllers. The simulation results show the effectiveness of this algorithm on latency optimization, tail latency reduction and resource utilization improvement.