  Bin packing is a classic optimization problem with a wide range of
applications from load balancing in networks to supply chain management. In
this work we study the online variant of the problem, in which a sequence of
items of various sizes must be placed into a minimum number of bins of uniform
capacity. The online algorithm is enhanced with a (potentially erroneous)
prediction concerning the frequency of item sizes in the sequence. We design
and analyze online algorithms with efficient tradeoffs between their
consistency (i.e., the competitive ratio assuming no prediction error) and
their robustness (i.e., the competitive ratio under adversarial error), and
whose performance degrades gently as a function of the error. Previous work on
this problem has only addressed the extreme cases with respect to the
prediction error, and has relied on overly powerful and error-free prediction
oracles.
