Liu et al. EURASIP Journal on Wireless Communications and
Networking
(2016) 2016:273
DOI 10.1186/s13638-016-0766-2
RESEAR CH Open Access
Optimal virtual network embedding
based on artificial bee colony
Xu Liu, Zhongbao Zhang
*
, Ximing Li and Sen Su
Abstract
As one of the key challenges in network virtualization, the problem of virtual network embedding has attracted
significant attention from researchers. In this problem, it needs to embed virtual networks with both node and link
demands into a shared physical network. The main goal of this problem is to embed more virtual networks to gain
more revenue. However, the prior approaches still suffer from low performance and await to be further optimized in
terms of this goal. In this paper, we design an artificial bee colony-based virtual network embedding algorithm, called
VNE-ABC, to solve this problem. The core idea of this algorithm is to leverage the iterations and intelligence of artificial
bee colony to achieve a more optimal solution for virtual network embedding. Through simulations, we show that our
proposed algorithm gains about 35.4% more revenue than the existing algorithm.
Keywords: Network virtualization, Virtual network embedding, Artificial bee colony, Optimization
1 Introduction
Recently, network virtualization has been proposed as the
key technology to reshape the future Internet. It allows
multiple heterogeneous virtual networks (VNs), requested
by service providers (SPs), to coexist on the same shared
physical network (PN) managed by the infrastructure
providers (InP). Each VN can run personalized proto-
col and designate its own topology with different node
and link constraints in the PN. This paper concerns the
problem of VN embedding, which is to embed or map
the VNs to the PN while satisfying its node and link
constraints.
The VN embedding problem has received significant
attention [2, 3, 10, 15, 17, 21, 26, 28, 32]. In these prior
studies, the authors either applied relaxation and round-
ing technique [7] or designed heuristic methods [3, 26]
to perform the virtual network embedding. In our pre-
vious studies [2, 28], we proposed to employ one of the
meta-heuristics, i.e., the particle swarm optimization, to
optimize the VN embedding problem. However, due to
the well-known reason that the particle swarm optimiza-
tion is easy to trap into local optimum, the performance
*Correspondence: zhongbaozb@bupt.edu.cn
State Key Laboratory of Networking and Switching Technology, Beijing
University of Posts and Telecommunications, 10 Xitucheng Road, Haidian
District, 100876 Beijing, China
of this algorithm is still awaiting to be optimized. In this
paper, we propose to leverage another meta-heuristic, i.e.,
artificial bee colony (ABC), to further optimize the VN
embedding problem.
In particular, the main idea of ABC algorithm is to sim-
ulate the process of a group of bees gathering honey. They
fully exploit individual intelligence to improve the effi-
ciency and effect of the overall work greatly. Specifically,
in ABC algorithm, three kinds of bees are involved, i.e.,
employed bee, onlooker bee, and scout bee. The employed
bee and onlooker bee are used to update solutions to bet-
ter ones in each iteration and the scout bee is applied to
avoid local optimal solutions.
However, in the process of applying the artificial bee
colony to the VN embedding problem, there are several
challenges for studying such problem.
•
Basic ABC only deals with continuous optimization
problem. However, the VN embedding problem is a
discrete optimization problem, and thus, it cannot be
used to our problem directly.
•
Naturally, the randomness of ABC may result in slow
convergence in our context.
•
The roulette method in basic ABC algorithm leads to
low performance for solving our problem.
Toward these ends, we conquer the above challenges as
follows:
© The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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