  The cloud computing industry has grown rapidly over the last decade, and with
this growth there is a significant increase in demand for compute resources.
Demand is manifested in the form of Virtual Machine (VM) requests, which need
to be assigned to physical machines in a way that minimizes resource
fragmentation and efficiently utilizes the available machines. This problem can
be modeled as a dynamic version of the bin packing problem with the objective
of minimizing the total usage time of the bins (physical machines). Earlier
works on dynamic bin packing assumed that no knowledge is available to the
scheduler and later works studied models in which lifetime/duration of each
"item" (VM in our context) is available to the scheduler. This extra
information was shown to improve exponentially the achievable competitive
ratio.
  Motivated by advances in Machine Learning that provide good estimates of
workload characteristics, this paper studies the effect of having extra
information regarding future (total) demand. In the cloud context, since demand
is an aggregate over many VM requests, it can be predicted with high accuracy
(e.g., using historical data). We show that the competitive factor can be
dramatically improved by using this additional information; in some cases, we
achieve constant competitiveness, or even a competitive factor that approaches
1. Along the way, we design new offline algorithms with improved approximation
ratios for the dynamic bin-packing problem.
