Waiting but not Aging: Age-of-Information and Utility Optimization Under the Pull Model
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The Age-of-Information (AoI) has recently been proposed as an important metric for investigating the timeliness performance in information-update systems. In this paper, we introduce a new Pull model and study the AoI optimization problem under replication schemes. Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different levels of information freshness and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side. Specifically, assuming Poisson updating process for the servers and exponentially distributed response time, we derive a closed-form formula for computing the expected AoI and obtain the optimal number of responses to wait for to minimize the expected AoI. Then, we extend our analysis to the setting where the user aims to maximize the AoI-based utility, which represents the user's satisfaction level with respect to freshness of the received information. Furthermore, we consider a more realistic scenario where the user has no knowledge of the system. In this case, we reformulate the utility maximization problem as a stochastic Multi-Armed Bandit problem with side observations and leverage the unique structure of the problem to design learning algorithms with improved performance guarantees. Finally, we conduct extensive simulations to elucidate our theoretical results and compare the performance of different algorithms. Our findings reveal that under the Pull model, waiting for more than one response can significantly reduce the AoI and improve the AoI-based utility in most scenarios.
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