  Network virtualization has caught the attention of many researchers in recent
years. It facilitates the process of creating several virtual networks over a
single physical network. Despite this advantage, however, network
virtualization suffers from the problem of mapping virtual links and nodes to
physical network in most efficient way. This problem is called virtual network
embedding ("VNE"). Many researches have been proposed in an attempt to solve
this problem, which have many optimization aspects, such as improving embedding
strategies in a way that preserves energy, reducing embedding cost and
increasing embedding revenue. Moreover, some researchers have extended their
algorithms to be more compatible with the distributed clouds instead of a
single infrastructure provider ("ISP"). This paper proposes energy aware
particle swarm optimization algorithm for distributed clouds. This algorithm
aims to partition each virtual network request ("VNR") to subgraphs, using the
Heavy Clique Matching technique ("HCM") to generate a coarsened graph. Each
coarsened node in the coarsened graph is assigned to a suitable data center
("DC"). Inside each DC, a modified particle swarm optimization algorithm is
initiated to find the near optimal solution for the VNE problem. The proposed
algorithm was tested and evaluated against existing algorithms using extensive
simulations, which shows that the proposed algorithm outperforms other
algorithms.
