  In current large-scale distributed key-value stores, a single end-user
request may lead to key-value access across tens or hundreds of servers. The
tail latency of these key-value accesses is crucial to the user experience and
greatly impacts the revenue. To cut the tail latency, it is crucial for clients
to choose the fastest replica server as much as possible for the service of
each key-value access. Aware of the challenges on the time varying performance
across servers and the herd behaviors, an adaptive replica selection scheme C3
is proposed recently. In C3, feedback from individual servers is brought into
replica ranking to reflect the time-varying performance of servers, and the
distributed rate control and backpressure mechanism is invented. Despite of
C3's good performance, we reveal the timeliness issue of C3, which has large
impacts on both the replica ranking and the rate control, and propose the Tars
(timeliness-aware adaptive replica selection) scheme. Following the same
framework as C3, Tars improves the replica ranking by taking the timeliness of
the feedback information into consideration, as well as revises the rate
control of C3. Simulation results confirm that Tars outperforms C3.
