  Cloud computing datacenters provide thousands to millions of virtual machines
(VMs) on-demand in highly dynamic environments, requiring quick placement of
requested VMs into available physical machines (PMs). Due to the randomness of
customer requests, the Virtual Machine Placement (VMP) should be formulated as
an online optimization problem.
  The first part of this work analyzes alternatives to solve the formulated
problem, an experimental comparison of five different online deterministic
heuristics against an offline memetic algorithm with migration of VMs was
performed, considering several experimental workloads. Simulations indicate
that First-Fit Decreasing algorithm (A4) outperforms other evaluated heuristics
on average.
  This work presents a two-phase schema formulation of a VMP problem
considering the optimization of three objective functions in an IaaS
environment with elasticity and overbooking capabilities. The two-phase schema
formulation describes that the allocation of the VMs can be separated into two
sub-problems, the incremental allocation (iVMP) and the reconfiguration of a
placement (VMPr).
  To analyze alternatives to solve the formulated problem, an experimental
comparison of three different objective function scalarization methods as part
of the iVMP and VMPr was performed considering several experimental workloads.
Simulations indicate that the Euclidean distance to origin outperforms other
evaluated scalarization methods on average.
  In order to portray the dynamic nature of an IaaS environment a customizable
workload trace generator was developed to simulate uncertainty in the scenarios
with elasticity and overbooking of resources in VM requests.
  Experimental results proved that the Euclidean distance is preferable over
the other scalarizatiom methods to improve the values of the power consumption
objective function.
