Abstract
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 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 assuming different update sizes with different priority levels. Next, we have developed novel policies for sampling and scheduling the information updates over the choice of one (or a set) 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. Unlike queue-based policies, our scheduling approach over parallel links enjoys two key advantages. First, the scheduling decision is independent of the queue length and is thus less complex. Second, it can differentiate the updates of the apps to further prioritize the very-timely sensitive information (e.g control signals) over other messages that can tolerate more delay (e.g., status updates). Our evaluation results show significant improvements of the proposed approaches as compared to the state-of-the-art algorithms.