Online Request Replication for Obtaining Fresh Information Under Pull Model

Age of Information (AoI) has gained widespread usage and emerged as a pivotal metric for assessing timeliness performance in information-update systems. Such systems often entail service requirements for rapidly obtaining requested data in real-time. For instance, in the financial market, users rely on up-to-date and low latency information to make appropriate trading decisions to maximize profits within their financial budge. Much of the existing research on real-time services focuses on ensuring AoI or service-level latency, but there is a growing demand for joint optimization of these two metrics to accommodate a broader range of potential applications. Therefore, this paper investigates the problem of minimizing AoI within the context of statistical latency guarantees. To tackle the critical challenges posed by the joint modeling of AoI and statistical latency, system uncertainty, as well as trade-off between performance and user’s budget, we employ a replication scheme to ensure both AoI and statistical service-level latency. To address the critical challenges posed by the unknown distribution in data updating processes and response times across providers, we formulate the AoI minimization problem with statistical latency constraints as a combinatorial Multi-Armed Bandit problem utilizing the Lyapunov optimization theory. Subsequently, we propose an online learning-based request replication algorithm to address this problem. Our proposed algorithm achieves a cumulative regret of O(Tlog(T)) compared to the genie-aided algorithm. Simulation results demonstrate the superior performance of the proposed algorithm against benchmarks.