Self-adaptive architecture for virtual machines consolidation based
on probabilistic model evaluation of data centers in Cloud computing
Reza Mohammadi Bahram Abadi
1
•
Amir Masoud Rahmani
2,3
•
Sasan Hossein Alizadeh
1
Received: 24 August 2016 / Revised: 30 August 2017 / Accepted: 30 April 2018
Ó Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract
By employing the virtual machines (VMs) consolidation technique at a virtualized data center, optimal mapping of VMs to
physical machines (PMs) can be performed. The type of optimization approach and the policy of detecting the appropriate time
to implement the consolidation process are influential in the performance of the consolidation technique. In a majority of
researches, the consolidation approach merely focuses on the management of underloaded or overloaded PMs, while a number
of VMs could also be in an underload or overload state. Managing an abnormal state of VM results in the postponement of PM
getting into an abnormal state as well and affects the implementation time of the consolidation process. For the aim of optimal
VM consolidation in this research, a self-adaptive architecture is presented to detect and manage underloaded and overloaded
VMs /PMs in reaction to workload changes in the data center. The goal of consolidation process is employing the minimum
number of active VMs and PMs, while guaranteeing the quality of service (QoS). Assessment criteria of QoS are two
parameters including average number of requests in the PM buffer and average waiting time in the VM. To evaluate these two
parameters, a probabilistic model of the data center is proposed by applying the queuing theory. The assessment results of the
probabilistic model form a basis for decision-making in the modules of the proposed architecture. Numerical results obtained
from the assessment of the probabilistic model via discrete-event simulator under various parameter settings confirm the
efficiency of the proposed architecture in achieving the aims of the consolidation process.
Keywords Cloud computing Á Data center Á Virtualization Á Probabilistic model Á Self-adaptive Á Reconfiguration
1 Introduction
Cloud computing has presented a new pattern for the
dynamic supply of computational services by developing a
service oriented architecture [1]. Special benefits and
features, which cloud computing offers the users via the
data centers, result in the fast development of this tech-
nology throughout various organizations. Along with an
increase in the number of cloud users and due to the
growing and unpredictable demands on resources, data
center infrastructures are developed in order to respond to
user requirements. Researches revealed that in 2010, the
energy consumption at data centers had been approxi-
mately 1.3% of the world total energy consumption [2]. It
is predicted that the world power demand for data centers
will be increased to over 66% during 2011–2035 [3]. A
substantial amount of consumed energy at data centers is
concerned with computational resources [4]. This is while
measurements showed that average server utilization was
10– 50% [5]. Therefore, data center management with the
aim of optimizing server resource utilization and accord-
ingly, energy consumption reduction is one of the great
challenges of cloud managers [6]. One approach to
improve resource utilization in the data center is the use of
& Amir Masoud Rahmani
amir.rahmani@uhd.edu.iq
Reza Mohammadi Bahram Abadi
mohamadi_re@yahoo.com
Sasan Hossein Alizadeh
sasan.h.alizadeh@qiau.ac.ir
1
Faculty of Computer and Information Technology
Engineering, Qazvin Branch, Islamic Azad University,
Qazvin, Iran
2
Computer Engineering Department, Science and Research
Branch, Islamic Azad University, Tehran, Iran
3
Computer Science, University of Human Development,
Sulaimanyah, Iraq
123
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