Conference PaperPDF Available

An Experimental Comparison of Algorithms for Virtual Machine Placement Considering Many Objectives

Authors:
  • Universidad Internacional Tres Fronteras

Abstract and Figures

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. This work presents a formulation of a VMP problem considering the optimization of the following objective functions: (1) power consumption, (2) economical revenue, (3) quality of service and (4) resource utilization. To analyze alternatives to solve the formulated problem, an experimental comparison of fi�ve diff�erent online deterministic heuristics against an offl�ine 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. Experimental results prove that an offl�ine memetic algorithm improves the quality of the solutions with migrations of VMs at the expense of placement recon�gurations.
Content may be subject to copyright.
An Experimental Comparison of Algorithms for Virtual
Machine Placement Considering Many Objectives
Fabio López-Pires12, Benjamín Barán2,
Augusto Amarilla2, Leonardo Benítez2, Rodrigo Ferreira2, Saúl Zalimben2
fabio.lopez@pti.org.py, bbaran@pol.una.py,
{agu.amarilla, benitez.leonardo.py, rodrigofepy, szalimben93}@gmail.com
1Itaipu Technological Park
2National University of Asunción
Paraguay
ABSTRACT
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 random-
ness of customer requests, the Virtual Machine Placement
(VMP) should be formulated as an online optimization pro-
blem. This work presents a formulation of a VMP problem
considering the optimization of the following objective func-
tions: (1) power consumption, (2) economical revenue, (3)
quality of service and (4) resource utilization. To analyze
alternatives to solve the formulated problem, an experimen-
tal comparison of five different online deterministic heuris-
tics against an offline memetic algorithm with migration of
VMs was performed, considering several experimental work-
loads. Simulations indicate that First-Fit Decreasing algo-
rithm (A4) outperforms other evaluated heuristics on ave-
rage. Experimental results prove that an offline memetic
algorithm improves the quality of the solutions with migra-
tions of VMs at the expense of placement reconfigurations.
Keywords
Virtual Machine Placement; Cloud Computing; Online Al-
gorithms; Experimental Comparison; Optimization.
1. INTRODUCTION
A main concern of cloud datacenters design is to efficiently
manage available resources in order to improve performance
and reduce energy consumption of a given computational
infrastructure. Most of the time, servers operate in a very
low energy-efficiency region (i.e. between 10% and 50% of
resource utilization), even considering that workload peaks
rarely occur in practice [2]. Several methods were considered
for addressing this issue, being the virtualization of resources
the most studied for cloud computing datacenters.
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full cita-
tion on the first page. Copyrights for components of this work owned by others than
ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re-
publish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from permissions@acm.org.
LANC ’16, October 13-14, 2016, Valparaiso, Chile
c
2016 ACM. ISBN 978-1-4503-4591-0/16/10. . . $15.00
DOI: http://dx.doi.org/10.1145/2998373.2998374
In cloud computing datacenters, resources are allocated
and released dynamically trying to serve requested demands.
In this context, deciding the right allocation of virtual ma-
chines (VMs) into physical machines (PMs) is known in the
specialized literature as Virtual Machine Placement (VMP).
It is relevant to remember that the VMP problem is a
NP-Hard combinatorial optimization problem [17]. From
an Infrastructure as a Service (IaaS) provider’s perspective,
the VMP problem must be formulated as an online problem
(considering that requests from customers are unknown a
priori) and it should be solved with short time constraints.
Consequently, solution techniques with low computational
complexity are very studied for this VMP environment [13].
The most studied heuristics in the VMP literature are:
Best-Fit (BF), Best-Fit Decreasing (BFD), First-Fit (FF),
First-Fit Decreasing (FFD) and Worst-Fit (WF) [13].
As presented by L´opez-Pires and Bar´an in [13], over 60
different objectives have been proposed for VMP problems.
The number of considered objective functions may rapidly
increase once a complete understanding of the VMP problem
is accomplished for practical problems, where many different
parameters should be ideally taken into account.
This work focuses in online formulations of a provider-
oriented VMP problem in federated-clouds [13] to provide
relevant information for design and implementation of re-
source management systems, more specifically, resource al-
location algorithms for VMP problems. Many relevant ob-
jectives for IaaS providers are considered in this work. To
solve the formulated online problem, five of the most stu-
died heuristics were compared against a Memetic Algorithm
(MA) solving an offline formulation of the problem.
Background and Motivation
opez-Pires and Bar´an recently proposed in [9] and [12]
offline formulations of VMP problems considering many ob-
jectives, proposing novel memetic algorithms for solving the
formulated problems. Considering the on-demand model of
cloud computing with dynamic resource provisioning and
dynamic workloads of cloud applications [14], the resolution
of the VMP problem should be performed as fast as possible.
Consequently, applying only offline formulations of the VMP
problem with meta-heuristics as solution technique, may not
be appropriate for these dynamic environments. Therefore,
as previously mentioned, solution techniques with low com-
putational complexity (e.g. heuristics) are very studied for
solving online formulations of VMP problems [13].
To help IaaS providers in the design and implementation
of resource allocation algorithms considering many objective
functions, the following research question must be answered:
Which heuristics are most suitable for solving online VMP
problems in federated-clouds considering many objectives?
In this context, Fang et al. presented in [6] a validation of
a proposed power-aware algorithm against well-known heu-
ristics such as: BF, FF and WF. Additionally, Jin et al. stu-
died FFD and Dominant Resource First (DRF) algorithms
[10], while Anand et al. evaluated in [1] a proposed In-
teger Linear Programming (ILP) formulation and an FDD
algorithm to attend common limitations of ILP algorithms
for large instances of NP-Hard optimization problems. On
the contrary, this work does not compare novel algorithms
against well-known heuristics as presented in the above men-
tioned works. The main goal of the presented experimental
comparison is to analyze the online nature of the VMP pro-
blem optimizing many different objectives, identifying ad-
vantages and disadvantages of well-known online heuristics
against offline alternatives such as the MA proposed in [9].
Experimental results presented by Ihara et al. in [9] rec-
ommend the combination of many objective functions into
a single objective for IaaS environments, given the require-
ment of obtaining solutions in a short period of time.
To compare the considered algorithms (see Section 3),
a provider-oriented VMP problem formulation is proposed
for the optimization of four objective functions: (1) power
consumption, (2) economical revenue, (3) quality of service
and (4) resource utilization (see Section 2). To combine the
above mentioned objectives, a weighted sum method is con-
sidered. Experiments were performed to define appropriate
weights for each objective function (see Section 4).
The remainder of this paper is structured in the following
way: Section 2 summarizes the proposed provider-oriented
VMP problem formulation with many objectives. Section 3
describes the considered algorithms to solve the formulated
problem, while Section 4 presents experimental workloads,
obtained results and main findings of the comparison. Fi-
nally, conclusions and future work are left to Section 5.
2. PROBLEM FORMULATION
This section presents a formulation of a VMP problem
considering the optimization of the following objective func-
tions: (1) power consumption, (2) economical revenue, (3)
quality of service and (4) resource utilization. According
to the taxonomy presented in [13], this work focuses on
a provider-oriented VMP for federated-cloud deployments,
considering two formulation types: (1) online and (2) offline.
An online problem formulation is considered when an algo-
rithm makes decisions on-the-fly without knowing upcoming
events (e.g. online heuristics for VMP problems) [4]. On the
other hand, if an algorithm has a complete knowledge of the
future events of a problem instance, the formulation is called
offline (e.g. MAs for VMP problems used in [9] and [12]).
The formulation of the proposed provider-oriented VMP
problem is based on [9] and could be enunciated as:
Given a cloud infrastructure composed by a set of PMs (H),
a dynamic scenario composed by a set of VMs requested at
each discrete time t(V(t)) and the current placement of VMs
into PMs (P(t)), it is incrementally sought a placement of
V(t)into Hfor the next time instant (P(t+ 1)), satisfying
the constraints and optimizing the considered objectives.
In online algorithms for solving the proposed VMP pro-
blem, placement decisions are performed at each discrete
time t, without knowledge of upcoming VM requests. On
the other hand, an offline algorithm have knowledge of the
complete set of VM requests in order to decide the place-
ment of these VMs into available PMs. Consequently, the
current placement is not necessary for offline algorithms.
2.1 Input Data
The proposed formulation of the VMP problem models a
cloud computing datacenter, composed by PMs and VMs,
receiving the following information as input data:
a set of navailable PMs and their specifications;
a set of m(t) VMs at each discrete time tand their
specifications;
the current placement at each discrete time t.
The set of PMs is represented as a matrix HRn×4, as
presented in (1). Each PM Hiis represented by its physi-
cal resources. This work considers 3 different physical re-
sources (P r1-P r3): CPU [ECU], RAM memory [GB] and
network capacity [Mbps]. It is important to mention that
the proposed notation is general enough to include more
characteristics associated to physical resources. Finally, the
maximum power consumption [W] is also considered, i.e.
H=
P r1,1P r2,1P r3,1pmax1
. . . . . . . . . . . .
P r1,n P r2,n P r3,n pmaxn
(1)
where:
P r1,i: Processing resources of Hiin [ECU];
P r2,i: Memory resources of Hiin [GB];
P r3,i: Network capacity resources of Hiin [Mbps];
pmaxi: Maximum power consumption of Hiin [W];
n: Number of PMs.
The set of VMs requested by customers at each discrete
time tis represented as a matrix V(t)Rm×5, as presented
in (2). In this work, each VM Vjrequires 3 different virtual
resources (V r1-V r3): CPU [ECU], RAM memory [GB] and
network capacity [Mbps]. It is important to mention that the
notation could represent any other set of virtual resources
such as: Block Storage or even Graphics Processing Unit
(GPU). An economical revenue Rj[$] is associated to each
VM Vjas well as a priority level represented as a Service
Level Agreement (SLA). The VMs try to lease the requested
virtual resources for a fixed period of discrete time.
V(t) =
V r1,1V r2,1V r3,1SLA1R1
. . . . . . . . . . . . . . .
V r1,m(t)V r2,m(t)V r3,m(t)SLAm(t)Rm(t)
(2)
where:
V r1,j : Processing requirements of Vjin [ECU];
V r2,j : Memory requirements of Vjin [GB];
V r3,j : Network requirements of Vjin [Mbps];
Rj: Economical revenue for attending Vjin [$];
SLAj: SLA of Vj, where SLAj∈ {1,2,...,s}, being
sthe highest priority level;
m(t): Number of VMs at each discrete time t, then
m(t)∈ {1,...,mmax};
mmax: Maximum number of acceptable VMs.
Once a VM Vjis powered-off by the customer, its virtual
resources are released, so the provider can reuse them. For
simplicity, in what follows the index jis not reused; there-
fore, for this work Vjis not a function of time.
The current placement of VMs into PMs (P(t)) is also
considered as an input data, taking into account that the
placement of requested VMs is performed incrementally at
each discrete time t. The placement at each discrete time t
is represented as a matrix P(t)Rm(t)×n, defined as:
P(t) =
P1,1(t)P1,2(t). . . P1,n(t)
. . . . . . . . . . . .
Pm(t),1(t)Pm(t),2(t). . . Pm(t),n (t)
(3)
where:
Pj,i(t)∈ {0,1}: Indicates if Vjis allocated (Pj,i(t) = 1) or
not (Pj,i(t) = 0) for execution on a PM Hi
(i.e., Pj,i(t) : VjHi) at a discrete time t.
2.2 Output
The result of the proposed VMP problem at each discrete
time tis a new placement for the next time instant (P(t+1)).
This is represented by a matrix P(t+ 1) Rm(t+1)×n.
P(t+ 1) =
P1,1. . . P1,n
. . . . . . . . .
Pm(t+1),1. . . Pm(t+1),n
(4)
where:
Pj,i(t+ 1) ∈ {0,1}: Indicates if Vjis allocated (Pj,i (t) = 1)
or not (Pj,i(t) = 0) for execution on a PM
Hi(i.e., Pj,i(t) : VjHi) at instant t+ 1.
2.3 Constraints
2.3.1 Constraint 1: Unique Placement of VMs
A VM Vjshould be located to run on a single PM Hi
or alternatively for each Vjsuch that SLAj< s, it could
not be located into any PM. Consequently, this placement
constraint is expressed as:
n
X
i=1
Pj,i(t)1j∈ {1,...,m(t)}(5)
2.3.2 Constraint 2: Assure SLA Provisioning
A VM Vjwith the highest level of SLA (i.e. SLAj=s)
must be mandatorily allocated to run on a PM Hi. Conse-
quently, this constraint is mathematically expressed as:
n
X
i=1
Pj,i(t) = 1 jsuch that SLAj=s(6)
2.3.3 Constraints 3-5: Physical Resources of PMs
A PM Himust have sufficient available resources to meet
the requirements of all VMs Vjthat are allocated to run on
Hi. Consequently, these constraints can be formulated as:
m(t)
X
j=1
V r1,j ×Pj,i(t)P r1,i (7)
m(t)
X
j=1
V r2,j ×Pj,i(t)P r2,i (8)
m(t)
X
j=1
V r3,j ×Pj,i(t)P r3,i (9)
i∈ {1, ..., n},i.e. for all PM Hi.
Physical resources are considered as resources available for
VMs, without considering resources for the PM’s hypervisor.
2.4 Objective Functions
Each of the considered objective functions must be for-
mulated in a single optimization context (i.e. minimization
or maximization) and each objective function cost must be
normalized to be comparable and combinable as a single ob-
jective. This work normalizes each objective function cost
by calculating ˆ
fi(x)R, where 0 ˆ
fi(x)1.
2.4.1 Power Consumption Minimization
Based on Beloglazov et al. [3], this work models the power
consumption of PMs considering a linear relationship with
the CPU utilization of PMs. This can be represented by the
sum of the power consumption of each PM Hi.
min f1(x) =
n
X
i=1
((pmaxipmini)×Ur1,i(t) +pmini)×Yi(t)
(10)
where:
f1(x): Total power consumption of PMs;
pmini: Minimum power consumption of a PM Hi. As
suggested in [3], pminipmaxi0.6;
Ur1,i(t): Utilization ratio of resource 1 (in this case CPU)
by Hiat instant t;
Yi(t)∈ {0,1}: Indicates if Hiis turned on (Yi(t) = 1) or
not (Yi(t) = 0) at instant t.
2.4.2 Economical Revenue Maximization
For IaaS customers, cloud computing resources often ap-
pear to be unlimited and can be provisioned in any quantity
at any required time t[14]. Consequently, this work con-
sidered a federated-cloud deployment architecture, where a
main provider may attend requested resources that are not
able to be provided (e.g. a workload peak) by transparently
leasing low-price resources at a federated provider [8].
In this work, the maximization of the total economical
revenue that a provider receives for attending the require-
ments of its customers is achieved by minimizing the total
costs of leasing resources from alternative datacenters of the
cloud federation. Equation (11) represents the mentioned
leasing costs, defined by the sum of the total costs of leasing
each VM Vjthat is effectively allocated for execution on any
PM of an alternative datacenter of the cloud federation.
A provider must offer its idle resources to the cloud fede-
ration at lower prices than offered to customers in the ac-
tual cloud market. The pricing scheme may depend on the
particular agreement between providers of the cloud federa-
tion [8]. Consequently, this work considers that the main
provider may lease requested resources (that are not able to
provide) from the cloud federation at 70% ( ˆ
Xj= 0.7) of its
price in markets (Rj). This objective may be formulated as:
min f2(x) =
m(t)
X
j=1
(Rj×Xj(t)׈
Xj) (11)
where:
f2(x): Total costs for not allocating VMs in the main
provider;
Xj(t)∈ {0,1}: Indicates if Vjis allocated for execution on
a PM (Xj(t) = 1) or not (Xj(t) = 0) at instant t;
ˆ
Xj: Indicates if Vjis allocated on the main provider
(ˆ
Xj= 0) or on an alternative datacenter of the
cloud federation ( ˆ
Xj= 0.7).
It is important to note that ˆ
Xjis not a function of time.
The decision of locating a VM Vjon a federated provider is
considered only in the placement process, with no possible
migrations between providers. The value of ˆ
Xjdepends on
the agreement celebrated between federated providers.
2.4.3 Quality of Service Maximization
In this work, the quality of service (QoS) maximization
proposes the location of the maximum number of VMs with
the highest level of priority associated to the SLA prior to
VMs with smaller SLA. In case the main provider allocates
a VM in a federated provider the QoS is considered as 0. In
order to evaluate this objective in a minimization context,
the total of SLA violations is minimized and formulated as:
min f3(x) =
m(t)
X
j=1
(ˆ
CSLAj×SLAj×Xj(t)׈
Yj) (12)
where:
f3(x): Total SLA violations figure for a given placement;
ˆ
C: Constant, large enough to prioritize services with
a larger SLA over the ones with a lower SLA;
ˆ
Yj∈ {0,1}: Indicates if Vjis allocated for its execution on
the main provider ( ˆ
Yj= 0) or on an alternative
datacenter of the cloud federation ( ˆ
Yj= 1).
2.4.4 Resources Utilization Maximization
An efficient utilization of resources is a relevant manage-
ment challenge to be addressed by IaaS providers. This work
proposes the resource utilization maximization strategy by
minimizing the average ratio of wasted resources on each
PM Hi(i.e. resources that are not allocated to any VM Vj).
This objective function is formulated in Equation (13).
min f4(x) = Pn
i=1 1Ur1,i(t) + · · · +U rr,i(t)
r×Yi(t)
Pn
i=1 Yi(t)
(13)
where:
f4(x): Average ratio of wasted resources;
Ur1,i(t): Utilization ratio of resource 1 (in this case CPU)
by Hiat instant t;
Urr,i(t): Utilization ratio of resource r(any resource) by
Hiat instant t;
r: Number of considered resources. In this paper 3:
CPU, RAM memory and network capacity;
The following sub-section summarizes the main conside-
rations taken into account to combine the four presented
objective functions into a single objective.
2.5 Scalarization Method
Experimental results presented in [9] recommend the com-
bination of many normalized objective functions into a single
objective (e.g. minimum distance to origin) for IaaS envi-
ronments. This work considers a weighted sum method to
combine many objectives into a single objective.
As previously mentioned, each objective function cost must
is normalized to be comparable and combinable as a single
objective. This work normalizes each objective function cost
by calculating ˆ
fi(x)R, where 0 ˆ
fi(x)1, as defined in
[16]:
ˆ
fi(x) = fi(x)fi(x)min
fi(x)max fi(x)min
(14)
where:
ˆ
fi(x): Normalized cost of objective function fi(x);
fi(x): Cost of objective function fi(x);
fi(x)min: Minimum possible cost for fi(x);
fi(x)max: Maximum possible cost for fi(x).
Finally, the four previously presented normalized objec-
tive functions are combined into a single objective conside-
ring a weighted sum method, expressed as follows:
min f(x) =
q
X
i=1
ˆ
fi(x)×wi(15)
where:
f(x): Single objective function combining each fi(x);
ˆ
fi(x): Normalized cost of objective function fi(x);
wi: Weight of importance associated to fi(x);
q: Number of objective functions. In this case 4.
In this work, values of each weight wiassociated to an ob-
jective function fi(x) were obtained empirically by analyzing
a large number of experiments to be presented in Section 4.
3. EVALUATED ALGORITHMS
To analyze alternatives to solve the formulated VMP pro-
blem (see Section 2), an experimental comparison of five
different online deterministic heuristics against an offline
memetic algorithm with migration of VMs was performed.
This section summarizes the six different algorithms conside-
red in the experimental comparison presented in this work.
3.1 A1: First-Fit (FF)
In the First-Fit (FF) algorithm, requested VMs Vjare
allocated on the first PM Hiwith available resources (see
Section 2.3). Interested readers can refer to [6] for details
on FF, BF and WF algorithms applied to VMP problems.
3.2 A2: Best-Fit (BF)
The Best-Fit (BF) algorithm, allocates requested VMs Vj
on the first PM Hiwith available capacity from a sorted list
of PMs in increasing order by a score associated to each PM.
The score of a PM Hiis determined as a sum of its ratios
of unutilization of resources, as detailed in [6].
3.3 A3: Worst-Fit (WF)
In the Worst-Fit (WF) algorithm, VMs Vjare allocated
on the first available PM Hiof a decreasingly ordered list of
PMs based on the score of the PM, inversely to the operation
of the BF algorithm. For more details, refer to [6].
3.4 A4: First-Fit Decreasing (FFD)
The First-Fit Decreasing (FFD) algorithm operates simi-
larly to the previously presented FF algorithm. The main
difference with the FF algorithm is that FFD algorithm sorts
the list of requested VMs Vjin decreasing order by requested
CPU resources, as described in [7].
3.5 A5: Best-Fit Decreasing (BFD)
The Best-Fit Decreasing (BFD) algorithm has a similar
behavior to the BF algorithm. The main difference between
the BF algorithm and the BFD one is that the BFD sorts
the list of requested VMs Vjin decreasing order by requested
CPU resources. Interested readers can refer to [5] for details
on the BFD algorithm applied to VMP problems.
3.6 A6: Memetic Algorithm (MA)
A Memetic Algorithm (MA) is considered for an offline
alternative to solve the formulated VMP problem taking into
account many objectives (see Section 2). This algorithm is
based on the one proposed in [9] by Ihara et al.
The considered algorithm may be classified as a meta-
heuristic, following an evolutionary process to find appro-
priate solutions. Basically, the considered MA follows this
evolutionary behavior: solutions are selected from an evo-
lutionary set of solutions (or population). Crossover and
mutation operators are applied as usual, and eventually so-
lutions are repaired, as there may be unfeasible solutions.
Improvements of solutions of the evolutionary population
may be generated using local optimization operators. Next,
a new evolutionary population is selected from the union of
the (until that generation) best population and the set of
improved solutions. The evolutionary process is repeated
until the algorithm meets a stopping criterion (such as a
maximum number of generations), returning from the evolu-
tionary population the solution with minimum cost of f(x).
4. EXPERIMENTAL COMPARISON
In this work, an experimental comparison of five of the
most studied online deterministic heuristics was performed
considering several experimental workloads. The quality of
solutions obtained by the evaluated online heuristics was
compared to the average performance (in ten runs) of an
offline MA that has complete knowledge of future VM re-
quests and the ability of migrating VMs between PMs.
4.1 Experimental Environment
The six evaluated algorithms were implemented using ANSI
C programming language. The source code is available on-
line1, as well as all the considered experimental data. Expe-
riments were performed on a Linux Operating System with
an Intel Core i7 2.3 GHz CPU and 12 GB of RAM memory.
Physical resources (matrix H) represent an homogeneous
IaaS cloud composed by 10 PMs with the following speci-
fications: 8 [ECU] of CPU, 10 [GB] of RAM memory, 780
[Mbps] of network capacity and 960 [W] of maximum power
consumption (see Section 2.1 for notation details).
In this work, requested virtual resources (matrix V(t))
were considered using four different workload traces genera-
ted using a Workload Trace Generator for provider-oriented
VMP problems [15] available for research purposes2.
1http://github.com/DynamicVMP/vmpCompetitiveAnalysis
2http://wtg.cba.com.py
Table 1: Considered Data for Generated Experi-
mental Workload Traces (W1to W4). Details in [15].
Parameter Description Input Data
1. Environment No overbooking,
No elasticity
2. Workload Trace Duration [t] 100
3. Number of Cloud Datacenters 1
4. Number of Cloud Services 100
5. Number of VMs per Service 1
6. VMs creation time [t]
W1:Poisson(λ= 10)
W2:Poisson(λ= 50)
W3:Poisson(λ= 70)
W4:Uniform(0,100)
7. CPU Resources [ECU] Uniform(1,8)
8. RAM Resources [GB] Uniform(1,8)
9. Network Capacity [Mbps] Uniform(10,1000)
10. Revenue of VMs (Rj) [$] Uniform(0.1,1.5)
11. SLA Level of VMs (SLAj)Uniform(1,5)
Each considered workload trace simulates customers re-
quests to allocate a set of 100 VMs following different Pro-
bability Distribution Functions (PDFs) for the VM request
arrivals. Three workload traces follow a Poisson PDF with
different expected values (λ): (W1): λ= 10, (W2): λ= 50
and (W3): λ= 70. In this case, different values of λmay
represent workload peaks at different time instants t(see
Figure 1). The fourth workload trace (W4) follows an Uni-
form PDF, representing a stable workload of VM requests.
All parameters considered for generating the experimental
workload traces are presented in Table 1.
In order to effectively analyze the described federated-
cloud deployment architecture and provider-oriented VMP
formulation considering many objectives, experimental work-
load traces requested more virtual resources than the availa-
ble ones in the considered main cloud computing datacenter.
Experiments could be summarized as: For each conside-
red workload trace (W1to W4), one run of the following
deterministic heuristics was performed in an online context:
(1) FF, (2) BF, (3) WF, (4) FFD and (5) BFD. Considering
the randomness of the MA compared against the previously
mentioned heuristics, ten runs of the mentioned algorithm
were performed. The average values of the ten runs were
considered for the experimental comparison, as summarized
in Table 2, where the offline MA clearly outperforms all 5
online heuristics as expected, given that it uses complete
knownledge of VM requests and migration of VMs to opti-
mize the objective function presented in Equation 15.
4.2 Objective Functions Weights
To determine appropriate values for the weights wiasso-
ciated to each objective function fi(x), an exploration of the
VMP problem domain was performed. In this context, 1000
feasible solutions (x1to x1000) were randomly generated by
the MA (A6) considering: Has described in Section 4.1,
V(t) as presented in entorno00-1 (a benchmark available
online2), highest priority of VMs s= 4 and ˆ
C= 1000.
Obtained values of each objective function fi(x) were nor-
malized in ˆ
fi(x), as described in Section 2.5. Consequently,
each weight wiwas defined as:
0 10 20 30 40 50 60 70 80 90 100
0
3
6
9
12
15
Time (t)
Number of created VMs
W1: Poisson λ= 10
W2: Poisson λ= 50
W3: Poisson λ= 70
W4: Uniform
Figure 1: Experimental Workload Traces: Number of created VMs as a function of time t.
wi=1000
P1000
k=1 ˆ
fi(xk)(16)
resulting:
w1: 1.3903; w2: 2.1379; w3: 2.7393; w4: 1.4586.
4.3 Comparison of Online Heuristics
This section summarizes the main findings obtained in the
experimental comparison of algorithms for the VMP formu-
lation with many objectives presented in Section 2.
The main goal of the experimental comparison presented
in this section is to define which heuristics are most suitable
for solving online formulations of provider-oriented VMP
problems in federated-clouds considering many objectives.
Table 2 summarizes the costs of objective function f(x),
that combines the four considered objectives (see Section
2.5), summarizing performed experiments. It worth noting
that according to the experimental results presented in Ta-
ble 2, there is no evaluated heuristic that outperforms all
other alternatives in all experimental workload traces. Con-
sequently, Table 2 also presents the average costs of ob jec-
tive function f(x) in the 4 experimental workloads as well
as the corresponding ranking. It seems that FFD algorithm
is the best heuristic amount the five considered ones closely
followed by BFD (logically, MA is first in this ranking).
To better understand the presented comparison, Table 3
details the average normalized costs of each ob jective func-
tion f1(x) to f4(x), to analyze particular preferences of IaaS
providers for the optimization of the considered objectives
(e.g. power consumption could be more important in hours
where the electricity price is higher).
Based on the information presented in Tables 2 and 3,
the Main Findings (MF) of the experimental comparison
performed in this paper are summarized as follows:
MF1: There is no evaluated heuristic that can clearly be
considered as the best alternative for all objective functions,
considered simultaneously.
Additionally, none of the evaluated heuristics performed equ-
ally well in all 4 experimental workloads (see Table 2). Con-
sequently, an heuristic performing good enough in average
when considering different types of workloads could be suf-
ficiently convenient.
A more detailed evaluation could be performed to obtain in-
formation for conjuntural preferences of IaaS providers (see
Table 3, where the best heuristic for each objective function
and each workload trace is highlighted in the last column).
MF2: FFD heuristic (A4) was the algorithm that outper-
forms all other evaluated heuristics taking into account ave-
rage results in performed experiments.
According to the average performance of each evaluated
heuristic (see Table 2), the following ranking was built: (1st)
FDD (A4), (2nd) BFD (A5), (3th) BF (A2), (4th) FF (A1),
and (5th) WF (A3), where BFD follows very close the ave-
rage performance of FDD (with a difference of 1.75%).
MF3: WF algorithm (A3) is suggested for workloads that
can be considered stable (i.e. no workload peaks).
For experimental workload trace W4that considers an Uni-
form PDF for VM request arrivals, A3 clearly presented the
best performance obtaining minimum average results in Ta-
ble 2. It also performed as the best algorithm for 3 out of
the 4 objectives, considering only W4(see Table 3).
MF4: The WF (A3), BFD (A5), FFD (A4) and BFD
(A5) algorithms are recommended for f1(x),f2(x),f3(x)
and f4(x)objective functions respectively, when there is a
preferred objective function (lexicographic order).
The BFD algorithm (A5) performed as the best algorithm on
average for f2(x) and f4(x). For f1(x) the best alternative
seems to be A3 while A4 could be considered as the best for
f3(x) (see Table 3).
MF5: As expected, the offline MA (A6) outperformed all
evaluated online heuristics in all experimental workloads.
An offline MA was compared to the five evaluated heuristics
to experimentally demonstrate that online decisions made
along a simulation affects the quality of solutions. Clearly,
offline algorithms such as the considered MA present a sub-
stantial advantage over online heuristics when considering
the quality of solutions for the objective function f(x) (see
Table 2). This advantage is presented for the following two
main reasons: (1) offline algorithms have a complete knowl-
edge of future events of a problem instance and (2) only A6
considered migrations of VMs between PMs in the compa-
rison, reconfiguring the placement when convenient.
Table 2: Objective Function Costs of Evaluated Algorithms.
Workload
Algorithm A1: FF A2: BF A3: WF A4: FFD A5: BFD A6: MA
W1: Poisson λ= 10 3.2927 3.3098 3.5250 3.0205 3.1392 2.6096
W2: Poisson λ= 50 2.4602 2.5112 2.4811 2.4602 2.4555 2.0001
W3: Poisson λ= 70 1.7054 1.6458 1.7054 1.6458 1.6458 1.3588
W4: Uniform 3.1875 3.1556 3.0489 3.0907 3.1556 2.3420
Average 2.6615 2.6556 2.6901 2.5543 2.5990 2.0776
Ranking 5th 4th 6th 2nd 3th 1st
Table 3: Objective Functions Costs of Evaluated Heuristics.
Workload fi(x)
Heuristic A1: FF A2: BF A3: WF A4: FFD A5: BFD Best Heuristic
W1
f1(x) 0.9240 0.9107 0.8929 0.9255 0.9208 A3
f2(x) 0.0372 0.0373 0.0391 0.0359 0.0359 A4,A5
f3(x) 0.6104 0.6139 0.6670 0.5101 0.5611 A4
f4(x) 0.1756 0.1933 0.2556 0.1778 0.1679 A5
W2
f1(x) 0.5900 0.5756 0.5612 0.5900 0.5903 A3
f2(x)0.0404 0.0406 0.0420 0.0404 0.0404 A4,A5
f3(x) 0.4790 0.4975 0.4715 0.4790 0.4790 A3
f4(x) 0.1652 0.1789 0.2189 0.1652 0.1618 A5
W3
f1(x)0.4146 0.4198 0.4203 0.4198 0.4198 A1
f2(x) 0.0243 0.0235 0.0245 0.0235 0.0235 A2,A4,A5
f3(x) 0.3155 0.3003 0.3103 0.3003 0.3003 A2,A4,A5
f4(x) 0.1457 0.1296 0.1497 0.1296 0.1296 A2,A4,A5
W4
f1(x) 0.8549 0.8544 0.8694 0.8598 0.8544 A2,A5
f2(x) 0.0375 0.0364 0.0356 0.0371 0.0364 A3
f3(x) 0.5311 0.5283 0.4884 0.4931 0.5283 A3
f4(x) 0.3177 0.3034 0.2919 0.3188 0.3034 A3
Average
f1(x) 0.6959 0.6901 0.6859 0.6988 0.6963 A3
f2(x) 0.0349 0.0345 0.0353 0.0343 0.0341 A5
f3(x) 0.4841 0.4851 0.4844 0.4457 0.4672 A4
f4(x) 0.2011 0.2014 0.2291 0.1979 0.1907 A5
Table 4: VMs Migration Overhead for MA (A6).
Workload
Migration # of VMs Memory in [GB]
Poisson λ= 10 1493.7 4865.9
Poisson λ= 50 740.0 2775.1
Poisson λ= 70 643.6 2415.5
Uniform 1416.7 5329.8
Having a complete knowledge of future VMs requests is con-
sidered unrealistic for IaaS environments [4]. Consequently,
online algorithms, such as the heuristics evaluated in this
work, are a good alternative for VMP problems in IaaS en-
vironments. The evaluated offline MA (A6) improves the
quality of solutions with migrations of VMs between PMs,
at expense of costs associated to placement reconfigurations,
as seen in Table 4 where the average VMs migration over-
head of each workload is presented as: (1) total number of
VM migrations and (2) total RAM memory migrated.
MF6: As expected, the execution time of all online heuris-
tics are considerably shorter than the offline alternative.
Considering the low complexity of the evaluated heuristics,
these algorithms are able to find promising solutions in short
periods of time (a main concern for IaaS providers solving
VMP problems) at the expense of not exploring solutions
that can potentially result in better quality solutions.
Clearly, any offline alternative that intelligently explore a
large space of feasible solutions will result in higher exe-
cution times, mainly considering the exponential computa-
tional complexity of the VMP problem.
5. CONCLUSIONS AND FUTURE WORKS
This work presented a first experimental comparison of
five different online heuristics for solving a proposed provider-
oriented VMP problem for the optimization of the following
objectives: (1) power consumption, (2) economical revenue,
(3) quality of service, and (4) resource utilization. The qual-
ity of solutions obtained by online heuristics was compared
to the best performance of an offline MA that have complete
knowledge of future VM requests and the capacity of VM
migration. A previously studied MA [9] was implemented ta-
king into account that obtaining optimal solutions for large
instances of the problem could result impracticable [11].
Considering the evaluation of the mentioned algorithms
solving several experimental workload traces, six main fin-
dings (MF1 to MF6) were identified (see Section 4.3).
The main advantage of the offline MA (A6) over experi-
mentally evaluated online heuristics (A1 to A5) is a better
quality of solutions, as summarized in Table 2.
Unfortunately, offline alternatives such as A6 are not ap-
propriate for dynamic environments of VMP problems for
IaaS providers, where future VM requests are unknown and
placement should be solved in a very short time.
In order to improve the quality of solutions obtained by
the evaluated heuristics, the VMP problem could be formu-
lated as a two-phase optimization problem. In this context,
VMP problems with many objectives for an IaaS could be
decomposed into two different sub-problems: (1) incremen-
tal VMP (iVMP) and (2) VMP reconfiguration (VMPr) [18].
A two-phase optimization strategy can be considered, com-
bining both online (iVMP) and offline (VMPr) algorithms
for solving each considered VMP sub-problem. The iVMP
problem is considered for dynamic arriving requests when
VMs should be created and removed at runtime. Conse-
quently, this sub-problem should be formulated as an online
problem and solved in a short period of time, where the stu-
died heuristics could be appropriate. On the contrary, the
VMPr problem is considered for improving the quality of
solutions obtained by the iVMP, considering placement re-
configurations through migration of VMs. This sub-problem
could be formulated offline, where alternative solution tech-
niques (e.g. meta-heuristics) could be appropriate.
Authors of this work are already working on the men-
tioned two-phase optimization (iVMP + VMPr), conside-
ring more realistic representation of cloud infrastructures
and more complex VMP environments with both overboo-
king and elasticity considerations [15], as well as extending
experiments to more workload traces including real-world
experimental workloads [4]. Finally, future directions in-
clude exploring research questions as: (1) when the VMPr
problem should be triggered? and (2) what should the VMPr
problem do with requests arriving during reconfiguration?
6. REFERENCES
[1] A. Anand, J. Lakshmi, and S. Nandy. Virtual machine
placement optimization supporting performance SLAs.
In Cloud Computing Technology and Science
(CloudCom), 2013 IEEE 5th International Conference
on, volume 1, pages 298–305. IEEE, 2013.
[2] L. A. Barroso and U. H¨
olzle. The case for
energy-proportional computing. IEEE computer,
40(12):33–37, 2007.
[3] A. Beloglazov, J. Abawajy, and R. Buyya.
Energy-aware resource allocation heuristics for
efficient management of data centers for cloud
computing. Future Generation Computer Systems,
28(5):755–768, 2012.
[4] A. Beloglazov and R. Buyya. Optimal online
deterministic algorithms and adaptive heuristics for
energy and performance efficient dynamic
consolidation of virtual machines in cloud data
centers. Concurrency and Computation: Practice and
Experience, 24(13):1397–1420, 2012.
[5] D. Dong and J. Herbert. Energy efficient VM
placement supported by data analytic service. In
Cluster, Cloud and Grid Computing (CCGrid), 2013
13th IEEE/ACM International Symposium on, pages
648–655. IEEE, 2013.
[6] S. Fang, R. Kanagavelu, B.-S. Lee, C. H. Foh, and
K. M. M. Aung. Power-efficient virtual machine
placement and migration in data centers. In Green
Computing and Communications (GreenCom), 2013
IEEE and Internet of Things (iThings/CPSCom),
IEEE International Conference on and IEEE Cyber,
Physical and Social Computing, pages 1408–1413.
IEEE, 2013.
[7] T. Ferreto, C. A. De Rose, and H.-U. Heiss. Maximum
migration time guarantees in dynamic server
consolidation for virtualized data centers. In Euro-Par
Parallel Processing, pages 443–454. Springer, 2011.
[8] M. Gahlawat and P. Sharma. Survey of virtual
machine placement in federated clouds. In Advance
Computing Conference (IACC), 2014 IEEE
International, pages 735–738, Feb 2014.
[9] D. Ihara, F. L´opez-Pires, and B. Bar´an.
Many-objective virtual machine placement for
dynamic environments. In Proceedings of the 2015
IEEE/ACM 8th International Conference on Utility
and Cloud Computing. IEEE Computer Society, 2015.
[10] H. Jin, D. Pan, J. Xu, and N. Pissinou. Efficient VM
placement with multiple deterministic and stochastic
resources in data centers. In Global Communications
Conference (GLOBECOM), 2012 IEEE, pages
2505–2510. IEEE, 2012.
[11] F. L´opez-Pires and B. Bar´an. Multi-objective virtual
machine placement with service level agreement: A
memetic algorithm approach. In Proceedings of the
2013 IEEE/ACM 6th International Conference on
Utility and Cloud Computing, pages 203–210. IEEE
Computer Society, 2013.
[12] F. L´opez-Pires and B. Bar´an. A many-objective
optimization framework for virtualized datacenters. In
Proceedings of the 2015 5th International Conference
on Cloud Computing and Service Science, 2015.
[13] F. L´opez-Pires and B. Bar´an. A virtual machine
placement taxonomy. In Proceedings of the 2015
IEEE/ACM 15th International Symposium on
Cluster, Cloud and Grid Computing. IEEE Computer
Society, 2015.
[14] P. Mell and T. Grance. The NIST definition of cloud
computing. National Institute of Standards and
Technology, 53(6):50, 2009.
[15] J. Ortigoza, F. L´opez Pires, and B. Bar´an. A
taxonomy on dynamic environments for
provider-oriented virtual machine placement. In
Proceedings of the 2016 IEEE 4th International
Conference on Cloud Engineering. IEEE Computer
Society, 2016.
[16] S. B. Salem, M. Fakhfakh, D. S. Masmoudi,
M. Loulou, P. Loumeau, and N. Masmoudi. A high
performances cmos ccii and high frequency
applications. Analog Integrated Circuits and Signal
Processing, 49(1):71–78, 2006.
[17] B. Speitkamp and M. Bichler. A mathematical
programming approach for server consolidation
problems in virtualized data centers. Services
Computing, IEEE Transactions on, 3(4):266–278,
2010.
[18] Q. Zheng, R. Li, X. Li, N. Shah, J. Zhang, F. Tian,
K.-M. Chao, and J. Li. Virtual machine consolidated
placement based on multi-objective
biogeography-based optimization. Future Generation
Computer Systems, 2015.
... Consequently, the main scope of the research summarized in this paper is studying first Many-Objective Virtual Machine Placement (MaVMP) problems from the perspective of cloud computing providers for several variants of the VMP problems. Different MaVMP problem formulations were proposed for: (1) initial placement of VMs (static) [15,16], (2) reconfiguration of VMs (semi-dynamic) [17,18], and (3) cloud computing under uncertainty (dynamic) [19,20]. Considering the novelty of the proposed formulations, several methods and algorithms were also proposed to address identified issues on solving each studied MaVMP problem. ...
... From an IaaS provider perspective, it is mostly formulated as an online problem and must be solved with short time constraints [8]. Online decisions made along the operation of a dynamic cloud computing infrastructure negatively affects the quality of solutions in VMP problems when comparing to offline decisions, studied as part of this dissertation in [19]. In this context, offline algorithms present a substantial advantage over online alternatives. ...
... In online algorithms for solving the proposed problem, placement decisions are performed at each discrete time t. The formulation of the proposed iVMP (online) problem is based on [19], formally enunciated as: Given a complex IaaS environment composed by a set of PMs (H), a set of active VMs already requested before time t (V (t)), and the current placement of VMs into PMs (i.e. x(t)), it is sought an incremental placement of V (t) into H for the discrete time t + 1 (x(t + 1)) without migrations, satisfying the problem constraints and optimizing the considered objective functions. ...
... From an IaaS provider perspective, the VMP is mostly formulated as an online problem and must be solved with short time constraints [19]. Online decisions made along the operation of a cloud computing infrastructure may negatively affect the quality of obtained solutions when compared to offline decisions [21]. Unfortunately, offline formulations are not appropriate for highly dynamic environments for real-world IaaS providers, where cloud services are requested dynamically. ...
... Unfortunately, offline formulations are not appropriate for highly dynamic environments for real-world IaaS providers, where cloud services are requested dynamically. To improve the quality of solutions obtained by online algorithms, the VMP problem could be formulated as a two-phase optimization problem, combining advantages of online and offline formulations [21]. In this context, VMP problems could be decomposed in two different sub-problems: (i) incremental VMP (iVMP) and (ii) VMP reconfiguration (VMPr) (see Figure 8.1). ...
... This first part of this work presented a formulation to solve a provider-oriented VMP problem optimizing the following objectives: (1) power consumption, (2) economical revenue, (3) quality of service, and (4) resource utilization (Objective 1, published in [21]). ...
Full-text available
Thesis
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.
... Online decisions made along the operation of a dynamic cloud computing infrastructure negatively affects the quality of obtained solutions in VMP problems when comparing to offline decisions [98]. In this context, offline algorithms present a substantial advantage over online alternatives. ...
... It is important to consider that online decisions made along the operation of a cloud computing infrastructure negatively affects the quality of obtained solutions of VMP problems when comparing to offline decisions [98]. Clearly, offline algorithms present a substantial advantage over online alternatives, when considering the quality of obtained solutions. ...
... To improve the quality of solutions obtained by online algorithms, the VMP problem could be formulated as a two-phase optimization problem, combining advantages of online and offline formulations for IaaS environments [98]. ...
... Memetic Algorithms (MAs) proposed in [6] and [7]). Online decisions made along the operation of a dynamic cloud computing infrastructure negatively affects the quality of obtained solutions in VMP problems when comparing to offline decisions [8]. Offline algorithms present a substantial advantage over online alternatives. ...
... In online algorithms for iVMP, decisions are performed at each discrete time t. The considered iVMP problem is based on [8] and could be formally enunciated as: ...
... QoS Maximization: The quality of service is improved by ensuring that the maximum number of services with high priorities are pushed. This objective is proposed in [28] as follows: ...
Article
With the increasing number of IoT devices, fog computing has emerged, providing processing resources at the edge for the tremendous amount of sensed data and IoT computation. The advantage of the fog gets eliminated if it is not present near IoT devices. Fogs nowadays are pre-configured in specific locations with pre-defined services, which limit their diverse availabilities and dynamic service update. In this paper, we address the aforementioned problem by benefiting from the containerization and micro-service technologies to build our on-demand fog framework with the help of the volunteering devices. Our approach overcomes the current limitations by providing available fog devices with the ability to have services deployed on the fly. Volunteering devices form a resource capacity for building the fog computing infrastructure. Moreover, our framework leverages intelligent container placement scheme that produces efficient volunteers’ selection and distribution of services. An Evolutionary Memetic Algorithm (MA) is elaborated to solve our multi-objective container placement optimization problem. Real life and simulated experiments demonstrate various improvements over existing approaches interpreted by the relevance and efficiency of (1) forming volunteering fog devices near users with maximum time availability and shortest distance, and (2) deploying services on the fly on selected fogs with improved QoS.
... In the context of MOP, preferences are used as weights for conflicting objectives to obtain lexicographic order of objectives [31] or to favor one of the objectives [32]. This is a typical approach in many MOPs as the number of potential solutions increases dramatically with the size of the problem and the number of objectives [14]. ...
Full-text available
Article
Proper handling of preferences in multiobjective evolutionary algorithms is essential for the algorithms’ success in real-life applications. While there has been a tremendous work addressing preferences in evolutionary algorithms, the issue of the exact interpretation of Decision Maker’s (DM) preferences and how it affects the performance of evolutionary algorithms has received little attention. One interpretation of preferences that has received significant attention lately by the AI community and is believed to be exercised naturally by decision makers is the Ceteris Paribus (all else being equal) interpretation. In this work, we adopt the notion of Ceteris Paribus (CP) as an interpretation for the DM preferences and incorporate it in a constrained multiobjective problem known as virtual machine placement (VMP). VMP is an essential multiobjective problem in the design and operation of cloud data centers concerned about placing each virtual machine to a physical machine (a server) in the data center.We propose a variant of the NSGA-II that promotes Ceteris Paribus preferred solutions and evaluate its applicability. Our experiment results show that this variant was able to return preferred solutions at almost no extra cost when compared to NSGA-II.
... The algorithm is able to minimize execution or communication costs between the application software components. The paper [30] deals with the optimization of the placement of VMs on physical compute nodes. This is a problem different from the one discussed in this paper, where the objective is to optimize the deployment of software on VMs, whose allocation of physical nodes is neglected. ...
Article
Currently, an increasing number of customers require cloud services with guaranteed security levels. At this aim, the adoption of multi-cloud strategies is spreading in a large number of interesting application domains, since they may potentially improve security and reduce development costs. However, the problem of identifying the optimal distribution of the components of a cloud application on resources belonging to multiple and heterogeneous providers is very challenging, especially in the presence of different security and performance constraints. This paper presents a novel security-driven approach for the design, development and deployment of multi-cloud applications. It is based on a fully-automatable process that supports the developer from the elicitation of the application requirements up to the identification of the optimal deployment configuration, allowing to find the best compromise between overall cost and achieved level of security. The proposed optimization process takes explicitly into account two critical aspects that are often overlooked in similar approaches, namely the cloud on-demand leasing model for the allocation of resources and the impact that the deployment has on the security policies actually implemented by a complex application.
... Lopez-Pires et al. (2016)). The main policies of VM scheduling according to their concerns can be summarized as follows:• Efficient Resource Utilization: Improving resource utilization and decreasing communication overheads are the most promising topics in managing resource at the IaaS layer. ...
Thesis
Allocating resources in data centers is a complex task due to their increase in size, complexity, and consumption of power. At the same time, consumers' requirements regarding execution time and cost have become more sophisticated and demanding. These requirements often conflict with the objectives of cloud providers. Set against this background, this thesis presents a model of resource allocation in cloud computing environments that focuses on developing the allocation process in three phases: (i) negotiation between consumers and providers to select the data center, (ii) scheduling tasks inside data centers, and (iii) scheduling virtual machines (VMs) to physical machines. The proposed model attempts to optimize each phase by applying multi-objective optimization (MOO) and many-objective optimization (MaOO) using a particle swarm optimization (PSO) algorithm. In more detail, a parallel PSO (PPSO) algorithm based on multi-objective was therefore developed to improve the SLA negotiation process between consumers and providers. The main insight of this algorithm is that SLA negotiation can be automated and the PSO can be parallelized to minimize negotiation time and to maximize system throughput, thus increasing the profits of providers. A many-objective PSO (MaOPSO) algorithm based on a modified ranking strategy was developed to improve the task scheduling problem in each data center. The novelty of this algorithm lies in using a modified ranking strategy to minimize evaluation time and improve the quality of the results. The algorithm was executed within the constraints of the tight deadline to improve performance in terms of both waiting time and completion time. Finally, VM allocation was improved by applying a many-objective PSO to allocate VMs in physical machines after clustering the hosts. Here the novelty lies in applying PSO and K-means when clustering hosts to improve VM allocation and migration, thus maximizing resource utilization and performance whilst reducing power consumption. Most notably, SLA Negotiation reduced waiting time and completed time by up to 20%. Additionally, it increased the throughput by about 20%. The proposed SLA negotiation reduced the rates of SLA violations by about 25%. On the other hand, the proposed MaOPSO task algorithm reduced the waiting time and completed time by 15% and 20% respectively. It increased the throughput up to 15% and the profits up to 15%. With respect to MaOPSO VM allocation, it improved resource utilization by up to 20%. Additionally, it reduced the power consumption by 25% compared to other algorithms. Profits are indirectly increased by improving utilization up to 20%. Finally, the MaOPSO VM algorithm led to an increased throughput of 20%, a reduced waiting time of 15%, and reduced the completed time up to 15%.
... Memetic Algorithms (MAs) for static [45] or semi-dynamic [38] VMP problems). Combining online and offline formulations is a relevant research topic [49]. ...
Full-text available
Chapter
Cloud computing datacenters dynamically provide millions of virtual machines in real-world cloud computing environments. A large number of research challenges have to be addressed toward an efficient resource management of these cloud computing infrastructures. In the resource allocation field, Virtual Machine Placement (VMP) is one of the most studied problems with several possible formulations and a large number of existing optimization criteria, considering solutions with high economical and ecological impact. Based on systematic reviews of the VMP literature, a taxonomy of VMP problem environments is presented to understand different possible environments where a VMP problem could be considered, from both provider and broker perspectives in different deployment architectures. Additionally, another taxonomy for VMP problems is presented to identify existing approaches for the formulation and resolution of the VMP as an optimization problem. Finally a detailed view of the VMP problem is presented, identifying research opportunities to further advance in cloud computing resource allocation areas.
... It is important to consider that online decisions made along the operation of a cloud computing infrastructure negatively affects the quality of obtained solutions of VMP problems when comparing to offline decisions [23]. Clearly, offline algorithms present a substantial advantage over online alternatives, when considering the quality of obtained solutions. ...
Full-text available
Chapter
Resource allocation in cloud computing datacenters presents several research challenges, where the Virtual Machine Placement (VMP) is one of the most studied problems with several possible formulations considering a large number of existing optimization criteria. This chapter presents the main contributions that studied for the first time Many-Objective VMP (MaVMP) problems for cloud computing environments. In this context, two variants ofMaVMP problems were formulated and different algorithms were designed to effectively address existing research challenges associated to the resolution of Many-Objective Optimization Problems (MaOPs). Experimental results proved the correctness of the presented algorithms, its effectiveness in solving particular associated challenges and its capabilities to solve problem instances with large numbers of physical and virtual machines for: (1) MaVMP for initial placement of VMs (static) and (2) MaVMP with reconfiguration of VMs (semi-dynamic). Finally, open research problems for the formulation and resolution of MaVMP problems for cloud computing (dynamic) are discussed.
Full-text available
Conference Paper
Cloud computing datacenters provide millions of virtual machines in actual cloud markets. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering the large number of possible optimization criteria and different formulations that could be studied. Considering the on-demand model of cloud computing, the VMP problem should be solved dynamically to efficiently attend typical workload of modern applications. This work proposes a taxonomy in order to understand possible challenges for Cloud Service Providers (CSPs) in dynamic environments, based on the most relevant dynamic parameters studied so far in the VMP literature. Based on the proposed taxonomy, several unexplored environments have been identified. To further study those research opportunities, sample workload traces for each particular environment are required; therefore, basic examples illustrate a preliminary work on dynamic workload trace generation.
Full-text available
Conference Paper
This paper presents for the first time a formulation of the Virtual Machine Placement as a Many-Objective problem (MaVMP), considering the simultaneous optimization of the following five objective functions for dynamic environments: (1) power consumption, (2) inter-VM network traffic, (3) economical revenue, (4) number of VM migrations and (5) network traffic overhead for VM migrations. To solve the formulated MaVMP problem, a novel Memetic Algorithm is proposed. As a potentially large number of feasible solutions at any time is one of the challenges of MaVMP, five selection strategies are evaluated in order to automatically select one solution at each time. The proposed algorithm with the considered selection strategies were evaluated in two different scenarios.
Full-text available
Conference Paper
The process of selecting which virtual machines should be located (i.e. executed) at each physical machine of a datacenter is commonly known as Virtual Machine Placement (VMP). This work presents a general manyobjective optimization framework that is able to consider as many objective functions as needed when solving the VMP problem in a pure multi-objective context. As an example of utilization of the proposed framework, for the first time a formulation of the many-objective VMP problem (MaVMP) is proposed, considering the simultaneous optimization of the following five objective functions: (1) power consumption, (2) network traffic, (3) economical revenue, (4) quality of service and (5) network load balancing. To solve the formulated many-objective VMP problem, an interactive memetic algorithm is proposed. Simulations prove the correctness of the proposed algorithm and its effectiveness converging to a treatable number of solutions in different experimental scenarios.
Full-text available
Conference Paper
Cloud computing datacenters dynamically provide millions of virtual machines (VMs) in actual cloud markets. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering the large number of possible optimization criteria and different formulations that could be studied. VMP literature include relevant research topics such as energy efficiency, Service Level Agreement (SLA), Quality of Service (QoS), cloud service pricing schemes and carbon dioxide emissions; all of them with high economical and ecological impact. This work classifies an extensive up-to-date survey of the most relevant VMP literature proposing a novel taxonomy in order to identify research opportunities and define a general vision on this research area.
Full-text available
Article
Virtual machine placement (VMP) is an important issue in selecting most suitable set of physical machines (PMs) for a set of virtual machines (VMs) in cloud computing environment. VMP problem consists of two sub problems: incremental placement (VMiP) problem and consolidated placement (VMcP) problem. The goal of VMcP is to consolidate the VMs to more suitable PMs. The challenge in VMcP problem is how to find optimal solution effectively and efficiently especially when VMcP is a kind of NP-hard problem. In this paper, we present a novel solution to the VMcP problem called VMPMBBO. The proposed VMPMBBO treats VMcP problem as a complex system and utilizes the biogeography-based optimization (BBO) technique to optimize the virtual machine placement that minimizes both the resource wastage and the power consumption at the same time. Extensive experiments have been conducted using synthetic data from related literature and data from two real datasets. First of all, the necessity of VMcP has been proved by experimental results obtained by applying VMPMBBO. Then, the proposed method is compared with two existing multi-objective VMcP optimization algorithms and it is shown that VMPMBBO has better convergence characteristics and is more computationally efficient as well as robust. And then, the issue of parameter setting of the proposed method has been discussed. Finally, adaptability and extensibility of VMPMBBO have also been proved through experimental results. To the best of our knowledge, this work is the first approach that applies biogeography-based optimization (BBO) to virtual machine placement.
Full-text available
Conference Paper
The process of selecting which virtual machines should be located (i.e. executed) at each physical machine of a Data center is known as Virtual Machine Placement - VMP. This work proposes for the first time a multi-objective formulation of the VMP considering Service Level Agreement. A novel multiobjective memetic algorithm is also proposed to solve the formulated multi-objective problem. This proposal is validated comparing experimental results of the proposed algorithm with a brute force exhaustive search algorithm. Simulations prove the correctness of the proposed memetic algorithm and its scalability considering different experimental scenarios.
Conference Paper
Cloud computing provides facility to its customers to dynamically scale up the applications, platform and the hardware infrastructure. But the resources provided from one cloud provider are finite and at some point of time can violate the SLA (service level agreements). One approach can be used to better facilitate the customers is to scale the applications, software platforms and the infrastructure to multiple independent clouds i.e. federated clouds. The federated clouds can share the resources with other cloud providers as the scale and load increases and can pay for the service on usage based. Virtual machine allocation is also an important parameter of federated clouds, because multiple clouds are exchanging the VM (Virtual Machine) with one another and the trading policies of all the clouds is not same. VM Allocation can be optimized for cost-effective Virtual machine allocation. This paper is a survey of all VM allocation policies available in federated clouds.
Conference Paper
Cloud computing model separates usage from ownership in terms of control on resource provisioning. Resources in the cloud are projected as a service and are realized using various service models like IaaS, PaaS and SaaS. In IaaS model, end users get to use a VM whose capacity they can specify but not the placement on a specific host or with which other VMs it can be co-hosted. Typically, the placement decisions happen based on the goals like minimizing the number of physical hosts to support a given set of VMs by satisfying each VMs capacity requirement. However, the role of the VMM usage to support I/O specific workloads inside a VM can make this capacity requirement incomplete. I/O workloads inside VMs require substantial VMM CPU cycles to support their performance. As a result, placement algorithms need to include the VMM's usage on a per VM basis. Secondly, cloud centers encounter situations wherein change in existing VM's capacity or launching of new VMs need to be considered during different placement intervals. Usually, this change is handled by migrating existing VMs to meet the goal of optimal placement. We argue that VM migration is not a trivial task and does include loss of performance during migration. We quantify this migration overhead based on the VM's workload type and include the same in placement problem. One of the goals of the placement algorithm is to reduce the VM's migration prospects, thereby reducing chances of performance loss during migration. This paper evaluates the existing ILP and First Fit Decreasing (FFD) algorithms to consider these constraints to arrive at placement decisions. We observe that ILP algorithm yields optimal results but needs long computing time even with parallel version. However, FFD heuristics are much faster and scalable algorithms that generate a sub-optimal solution, as compared to ILP, but in time-scales that are useful in real-time decision making. We also observe that including VM migration overheads in the placement algorithm results in a marginal increase in the number of physical hosts but a significant, of about 84 percent reduction in VM migration.