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imize these two factors on the one hand, and to re-
duce the cost of services on the other hand. To opti-
mize these objectives, we include our parameters in a
multi-objective genetic algorithm and we execute the
optimal solution in our Cloud which is tested as an
extension of the CloudSim framework.
The remainder of this paper is organized as fol-
lows. Section 2 discusses related work. A detailed
description of our solution is presented in section 3.
Then in section 4, we provide technical details of our
work. Section 5 deals with the experiments and the
results of the simulations which are produced with
comparisons and detailed analysis. Finally, the paper
ends with brief conclusive remarks and discussion on
future studies directions.
2 RELATED WORK
Cloud resources optimization is difficult to meet be-
cause of the uncertainty of future consumer demand
and resource prices. It has been a topic of research in-
terest and development for many years. To address the
growing challenge, techniques from many disciplines
were integrated synergistically. Next, we present the
state of the art of cloud resources optimization meth-
ods.
Cloud computing’s usage-based pricing model
creates an incentive for subscribers to optimize the
utilization of the rented resources. Borovskiy et al.
(Borovskiy et al., 2011) devise a formal approach for
distributing workload among a minimum number of
servers. They model this problem as a linear program-
ming problem and describe two solution approaches.
The first one generates a set of candidate blocks and
then composes an optimal partition by solving an in-
teger programming problem. The second approach
solves the set partitioning problem with column gen-
eration technique. The disadvantage of such method
is its difficulty to consider the purpose of Cloud re-
sources optimization because of the nonlinear charac-
teristics of users’ demands distribution.
Chaisiri et al. (Chaisiri et al., 2012) propose a
method to optimize Cloud resources cost. The under
provisioning problem can occur when the reserved re-
sources are unable to fully meet users’ demands due
to its uncertainty of the workload distribution. To
address this problem, the authors propose an opti-
mal cloud resource provisioning algorithm based on
stochastic programming model.
Regarding the problem of the description of the
Cloud resources characteristic with nonlinear equa-
tion, some researchers propose the use of a stochas-
tic optimization approach. For example, Li proposes
a model based on stochastic integer programming for
Cloud resources optimization (Li, 2012). He proposes
to address the SLA-aware resource composition prob-
lem. He defines a stochastic integer programming
model for resource composition and provides an algo-
rithm that implements Grbner based theory to solve
this problem (Buchberger, 2001).To solve the mini-
mization problem of Cloud infrastructure cost, Zhao
et al. developed a deterministic model for resource
reservation planning, using a mixed integer linear al-
gorithm, to generate optimal decisions given fixed pa-
rameters (Zhao et al., 2012). In addition, they pro-
posed a stochastic model of resource rental planning
which explicitly takes into account the uncertainty of
resources and users’ demand in the decision making
process. One major disadvantage of such approaches,
especially in dynamic environments where the opti-
mal solution changes over time, is that the parameter
estimation phase significantly delays the implementa-
tion of an optimal solution.
Other researchers use the constraint satisfaction
problem (CSP) approach to solve the problem of
Cloud resources optimization. Van et al. propose
a two-level based architecture that defines a clear
separation between application specific functions and
a generic global decision level (Nguyen Van et al.,
2009). They use utility functions to map the current
state of each application for a scalar value that quan-
tify the ”satisfaction” of each application in terms
of its performance targets. These utility functions
are also means of communication with the layer of
global decision which builds a global utility function
including the costs of resource management. The
stage of provisioning of virtual machines has been
separated from the stage of placement of virtual ma-
chines within the global decision layer loop and for-
mulates both problems as constraint satisfaction prob-
lem. Doughertya et al. propose a model driven ap-
proach to optimize the configuration, the energy con-
sumption and the cost of infrastructure for Cloud in-
frastructure self-scaling to create green IT environ-
ments that reduce emissions resulting from the use of
redundant resources unused (Dougherty et al., 2012),
(Dougherty et al., ). They proposed to decompose the
model to four sub-problems to ensure infrastructure
auto-scaling: explaining how virtual machine config-
urations can be captured; describe how these models
can be transformed in constraint satisfaction problems
for the configuration and optimization of energy con-
sumption; showing how optimal auto-scale configura-
tions can be derived from these CSPs with a constraint
solver and present a case study of energy consumption
and cost reduction of production of this model-driven
approach. The main drawback of these methods is
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