On the Information Freshness and Tail Latency Trade-Off in Mobile Networks

With the advent of emerging mission-critical applications, sampling information updates and scheduling mobile traffic in a timely manner are very challenging. In addition, maintaining fresh information and low latency communication is important to these applications. To that end, in this paper, we first derive closed form expressions for an upper bound on the latency tail probability (LTP) and the average age of information (AoI) in M/G/1 systems, where shifted exponential service time is considered. Different from the majority of existing work in this domain, our analysis is derived under different update size assumption with different priority levels. Next, we have developed novel policies for sampling and scheduling the information updates over the choice of one of the parallel links, e.g., WiFi and LTE links. Then, a joint minimization of AoI and LTP is formulated and efficient algorithms are provided. Our evaluation results show that our proposed approaches outperform the state-of-the-art algorithms and some competitive baselines.