  The Age-of-Information (AoI) has recently been proposed as an important
metric for investigating the timeliness performance in information-update
systems. Prior studies on AoI optimization often consider a Push model, which
is concerned about when and how to "push" (i.e., generate and transmit) the
updated information to the user. In stark contrast, in this paper we introduce
a new Pull model, which is more relevant for certain applications (such as the
real-time stock quotes service), where a user sends requests to the servers to
proactively "pull" the information of interest. Moreover, we propose to employ
request replication to reduce the AoI. 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 at the servers and
exponentially distributed response time, we derive a closedform formula for
computing the expected AoI and obtain the optimal number of responses to wait
for to minimize the expected AoI. Finally, we conduct numerical simulations to
elucidate our theoretical results. Our findings show that waiting for more than
one response can significantly reduce the AoI in most scenarios.
