  When tuning software configuration for better performance (e.g., latency or
throughput), an important issue that many optimizers face is the presence of
local optimum traps, compounded by a highly rugged configuration landscape and
expensive measurements. To mitigate these issues, a recent effort has shifted
to focus on the level of optimization model (called meta multi-objectivization
or MMO) instead of designing better optimizers as in traditional methods. This
is done by using an auxiliary performance objective, together with the target
performance objective, to help the search jump out of local optima. While
effective, MMO needs a fixed weight to balance the two objectives-a parameter
that has been found to be crucial as there is a large deviation of the
performance between the best and the other settings. However, given the variety
of configurable software systems, the "sweet spot" of the weight can vary
dramatically in different cases and it is not possible to find the right
setting without time-consuming trial and error. In this paper, we seek to
overcome this significant shortcoming of MMO by proposing a weight adaptation
method, dubbed AdMMO. Our key idea is to adaptively adjust the weight at the
right time during tuning, such that a good proportion of the nondominated
configurations can be maintained. Moreover, we design a partial duplicate
retention mechanism to handle the issue of too many duplicate configurations
without losing the rich information provided by the "good" duplicates.
  Experiments on several real-world systems, objectives, and budgets show that,
for 71% of the cases, AdMMO is considerably superior to MMO and a wide range of
state-of-the-art optimizers while achieving generally better efficiency with
the best speedup between 2.2x and 20x.
