A Study of Virtual Machine Placement Optimization in Data Centers

ephanie Challita, Fawaz Paraiso and Philippe Merle

Inria Lille - Nord Europe, Villeneuve d’Ascq, France

University of Lille, CRIStAL UMR CNRS 9189, Villeneuve d’Ascq, France

Keywords:

VM Placement, Data Center, Multi-objective Optimization, Energy-aware, Trafﬁc-aware, Cloud computing.

Abstract:

In recent years, cloud computing has shown a valuable way for accommodating and providing services over

the Internet such that data centers rely increasingly on this platform to host a large amount of applications

(web hosting, e-commerce, social networking, etc.). Thus, the utilization of servers in most data centers can

be improved by adding virtualization and selecting the most suitable host for each Virtual Machine (VM).

The problem of VM placement is an optimization problem aiming for multiple goals. It can be covered

through various approaches. Each approach aims to simultaneously reduce power consumption, maximize

resource utilization and avoid trafﬁc congestion. The main goal of this literature survey is to provide a better

understanding of existing approaches and algorithms that ensure better VM placement in the context of cloud

computing and to identify future directions.

1 INTRODUCTION

Cloud computing is a technology that enables com-

puting resources, software, or data to deliver on-

demand services over the Internet. These resources

have become cheaper, more powerful and more ubiq-

uitously available than ever before (Mell and Grance,

2011). The cloud computing stack consists of three

types of cloud service models: Infrastructure, Plat-

form and Software, which are built upon the Hard-

ware layer. Since provisioning Virtual Machines

(VMs) is fundamental to provide infrastructure ser-

vices, one can say that virtualization is the key con-

cept of cloud computing. According to Mike Adams,

director of product marketing at VMware

, “Virtual-

ization software makes it possible to run multiple op-

erating systems and multiple applications on the same

server at the same time. It enables businesses to re-

duce IT costs while increasing the efﬁciency, utiliza-

tion and ﬂexibility of their existing computer hard-

ware. ”(Angeles, 2014)

However, VMs need to be adequately placed

to fulﬁll performance goals, to optimize network

ﬂows, and to reduce CPU, storage and energy

costs. VM placement optimization processes may be

trafﬁc-aware, energy-aware, application-aware, net-

work topology-aware, data-aware, or a combination

VMware is an innovator in virtualization and cloud in-

frastructure, http://www.vmware.com/

of these.

In recent years, the problem of allocating VMs to

suitable Physical Machines (PMs) has been studied

for efﬁciency and quality purposes (Xu and Fortes,

2010), (Feller et al., 2011), (Fang et al., 2013), (Gao

et al., 2013), (Vu and Hwang, 2014). On the provider

side, these solutions map VMs to PMs to optimize

server efﬁciency, allowing some servers to hibernate

or shut down depending on load conditions. On the

consumer side, these solutions maximize Quality of

Service (QoS) and Quality of Experience (QoE). In

addition to this, such solutions lead to better utiliza-

tion of resources and less frequent overload situations,

leading to cost savings.

Although VMs have shown considerable opportu-

nities to the IT industry, their placement brings many

challenges that need to be carefully addressed. In

this paper, we present a survey of various approaches

studying VM placement, highlighting their key con-

cepts, as well as the state-of-the-art implementations.

The remainder of this paper is organized as follows.

In Section 2, we motivate the need of VM placement

optimization. Section 3 presents the state-of-the-art

that details the four approaches addressing energy,

cost and data ﬂow issues in data centers. Section 4

discusses potential comparison metrics between pre-

sented solutions. In Section 5, we discuss related sur-

veys that allow us to situate our work. Finally, Section

6 concludes and outlines future work.

Challita, S., Paraiso, F. and Merle, P.

A Study of Virtual Machine Placement Optimization in Data Centers.

DOI: 10.5220/0006236503430350

In Proceedings of the 7th International Conference on Cloud Computing and Services Science (CLOSER 2017), pages 315-322

ISBN: 978-989-758-243-1

315

2 MOTIVATION

As stated before, the motivation behind VM place-

ment optimization can be trafﬁc-aware, energy-aware,

application-aware, network topology-aware, data-

aware, and multi-objective. In our paper, we are in-

terested in investigating solutions that reduce energy

consumption (Ajiro and Tanaka, 2007), (Verma et al.,

2008), (Chaisiri et al., 2009), resource costs (Xu and

Fortes, 2010), (Fajjari et al., 2014), and properly man-

age data ﬂow (Kanagavelu et al., 2014), (Aral and

Ovatman, 2016) in data center architectures. Each of

these objectives is detailed below.

2.1 Energy Management

Enhancing energy efﬁciency in data centers can be

resolved by applying a suitable VM placement algo-

rithm that minimizes the cost of powering at the hard-

ware level. Moreover, turning off unused machines,

on the basis of server consolidation

and energy-

aware job scheduling, can also constitute a solution

for the energy problem. In this context, “Green Data

Centers” (Basmadjian et al., 2015) are nowadays a

must to ﬁght against huge power consumption and

bills caused by inappropriate virtualization.

2.2 Resource Usage Optimization

In order to maintain the application performance,

isolation and security, each VM requires a certain

amount of resources, such as CPU, memory and links

bandwidth, etc. In order to minimize their cost, these

resources should be made available to applications

only as needed and not allocated statically based on

the peak workload demand (Kusic et al., 2009). This

is known as the “elasticity of the cloud” (Herbst et al.,

2013).

2.3 Trafﬁc Engineering

Measuring and optimizing data center trafﬁc is impor-

tant to maintain the efﬁciency of applications. A data

center, which hosts thousands of devices like servers,

switches and routers, needs an accurate planning of

the network architecture. One can distinguish several

architectures (Wang et al., 2014) such as Fat-tree (Al-

Fares et al., 2008), VL2 (Greenberg et al., 2009) and

BCube (Guo et al., 2009).

Server consolidation is an approach to the efﬁcient us-

age of physical resources in order to reduce the total number

of servers that an organization requires. It involves gather-

ing several VMs into a single physical server.

3 APPROACHES

In this section, we review existing methods related

to the optimization of VM placement, embedded in

the cloud computing domain. Placement algorithms,

that collect and study information from deployed ap-

plications, can be either static or dynamic. Static al-

gorithms, which mainly do ofﬂine calculations, take

as an input the information that is formerly collected.

After a primary placement, a relocation may not be

considered for several months. Static allocation nor-

mally indicates initial VM placements that will be

actively running in subsequent phases of the system

administration. As for dynamic VM placement, it is

implemented on shorter timescales, preferably shorter

than periods of signiﬁcant variability of the resource

demand (Shankar and Bellur, 2010), (Abdelsamea

et al., 2014). It does online VM placement, including

VM migrations. One very important difference be-

tween static and dynamic VM placement algorithms

is the fact that dynamic solutions consider poten-

tial VM live migrations and therefore require larger

amount of resources than the static solutions, which

can badly affect the performance of the hosted appli-

cations. Since the static solutions are primitive and

outdated (Usmani and Singh, 2016), we are interested

to study the dynamic VM placement optimization so-

lutions. As shown in Figure 1, our classiﬁcation is

based on four main approaches: 1) Constraint Pro-

gramming, 2) Bin Packing, 3) Stochastic Integer Pro-

gramming, and 4) Genetic Algorithm. We detail in

the following the algorthims that were implemented

through these approaches.

Figure 1: VM placement approaches.

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316

3.1 Constraint Programming

Foremost Constraint Programming is used where we

have already completely collected the input informa-

tion, so we start the cost function calculations after

we are aware of the VM requirements. Since this ap-

proach can always consider additional constraints, it

can always be expandable. However, in cases where

we have several constraints to take into consideration,

this approach may take too much time to ﬁnd the most

suitable VM placement. Therefore, the main chal-

lenge consists in ﬁnding the optimal solution before

any modiﬁcation in terms of the constraint parame-

ters. Although this approach formally expresses the

demands of VMs in terms of constraints, it has been

modestly adopted. In 2009, Hermenier et al. (Herme-

nier et al., 2009) introduce the Entropy resource man-

ager, which is a constraint programming-based solu-

tion allowing for a dynamic server consolidation. It

tackles both problems of ﬁnding the available servers,

and migrating the VMs to these servers. This solution

is only applied in homogeneous data centers. In 2010,

Van et al. (Van et al., 2010) propose an interactive re-

source management framework that ensures both dy-

namic VM provisioning and placement, by treating

them as two constraint satisfaction problems.

3.2 Bin Packing

Bin Packing is used for dynamic VM placement when

all servers have the same amount of resources (CPU,

memory, storage, etc.), but the requirements of VMs

are variable in time. In order to minimize the number

of PMs, this approach may host two dependent VMs

on one PM. The Bin Packing problem is an NP-hard

problem that can be solved using i) Greedy heuris-

tics like First Fit Decreasing (FFD) algorithm (Ajiro

and Tanaka, 2007), (Tang et al., 2014), (Verma et al.,

2008), Best Fit (BF) algorithm (Jiankang et al., 2015),

Least Loaded (LL) algorithm (Ajiro and Tanaka,

2007), etc., ii) Ant Colony Optimization (ACO)

heuristics (Dorigo and Gambardella, 1997), (Dorigo

et al., 1996), (St

utzle and Hoos, 2000), iii) Subgraph

isomorphism algorithms (Ullmann, 1976).

3.2.1 Greedy Heuristics

FFD algorithm is a greedy heuristic. It places each

VM into “the ﬁrst bin in which it will ﬁt”. This ap-

proach is more efﬁcient when ﬁrst sorting the list of

A problem is NP-hard if the algorithm for solving it can

be translated into one for solving any NP-problem (nonde-

terministic polynomial time) problem. NP-hard therefore

means “at least as hard as any NP-problem,” but it can even-

tually be harder!

elements into a decreasing order. For example, Ajiro

et al. (Ajiro and Tanaka, 2007) present an Improved

FFD + LL heuristic. Both FFD and LL heuristics pack

VMs with certain resources into destination servers.

However, other resources may interfere and increase

the number of destination servers. The proposed algo-

rithm aims to address this issue and reduce the num-

ber of destination servers.

The pMapper system (Verma et al., 2008) packs

the VMs in a small number of physical servers in or-

der to minimize the migration costs, but always under

a ﬁxed constraint of performance. The pMapper sys-

tem is a subset of the FFD heuristic and it aims for a

compromise between performance and cost.

In 2013, VMPlanner (Fang et al., 2013) is pro-

posed as another greedy Bin Packing algorithm for

reducing power costs of network elements in data

centers. VMPlanner takes advantage of the ﬂexi-

bility provided by dynamic VM migration and pro-

grammable ﬂow-based routing, available in modern

data centers, to optimize network power consumption

while satisfying network trafﬁc demands. In order

to evaluate VMPlanner, the authors implement it in a

simulated environment running real data center trafﬁc

workloads, with a VL2 architecture (Greenberg et al.,

2009).

Kanagavelu et al. (Kanagavelu et al., 2014) de-

velop a fast heuristic algorithm called Greedy Vir-

tual Machine Placement with Two Path Routing

(GVMTPR) that is based on some constraints. Its goal

is to reduce the maximum load on any link, by re-

ducing the network trafﬁc load, followed by the num-

ber of used servers and the server resource utilization,

while guaranteeing the speciﬁed protection grade re-

quirements. It uses a greedy approach to assign the

hypervisor for each VM, in order to satisfy the com-

puting and memory resource requirements. This will

be done due to the VM management and routing de-

cisions. In addition, instead of a single-path routing,

it chooses a two-path routing to ensure load balanc-

ing on the two least-congested paths. Trafﬁc split-

ting helps to reduce congestion, which is largely inﬂu-

enced by the VM placement, and ensures trafﬁc pro-

tection (minimum bandwidth) in the event of failures.

Likewise, in 2014, Tang et al. (Tang et al., 2014)

present VM-DFS, an algorithm based on a dynamic

forecast scheduling method and that is a sort of Bin

Packing problem which can also be solved by FFD.

VM-DFS analyzes the historical memory consump-

tion of each VM, to select the most appropriate PM

to place the running VM according to the future con-

sumption prediction. The purpose of this method is to

reduce the number of active servers, such that every

VM can conform to the required memory consump-

A Study of Virtual Machine Placement Optimization in Data Centers

317

tion SLA

In (Jiankang et al., 2015), the authors propose

BF-HC and VM-Mig, two algorithms based on the

BF heuristic. The difference from FFD heuristic

is that the VMs are placed in the bin that can hold

them with minimum empty space. The objective

of this two-stage heuristic algorithm is to minimize

the energy consumption and to optimize the trafﬁc

management, by respectively reducing the number of

activated PMs and network elements and maximizing

the link utilization. The evalutaion is performed by

simulation experiments in Amazon EC2 data center

using Fat-tree topology (Al-Fares et al., 2008).

3.2.2 ACO Heuristics

Broadly speaking, ACO is a probabilistic technique

for solving computational problems, which can be re-

duced to ﬁnding good paths through graphs. The col-

lective behaviour of social insects is an inspiration

source for researchers. When searching for food, ants

tend to choose paths marked by strong pheromone

concentrations. As soon as an ant ﬁnds a food source,

it studies the quantity and the quality of the food and

takes some of it back to the nest. During the return

trip, the quantity of pheromones that an ant leaves on

the ground may depend on the quantity and quality of

the food. The pheromone trails will guide other ants

to the food source and enables them to ﬁnd the short-

est paths between their nest and food sources (Blum,

2005). Brieﬂy, ants mark the best solutions and take

into account the previous markings to optimize their

search (Yu et al., 2008). This behaviour, aiming for

the shortest paths, can be used for the VM migra-

tion optimization between PMs. Some extensions of

ACO algorithms are presented in the literature such

as Ant System (AS) (Dorigo et al., 1996), Ant Colony

System (ACS) (Dorigo and Gambardella, 1997) and

MAX-MIN Ant System (MMAS) (St

utzle and Hoos,

2000). Let us note that AS is the progenitor of all

the research efforts with Ant algorithms. It is widely

used to solve the Traveling Salesman Problem (TSP

)

(Hoffman et al., 2013).

MMAS (St

utzle and Hoos, 2000) is a variant of

ACO that aims to exploit the best solutions found

during the search in order to reach an optimal solu-

tion. MMAS updates the pheromone according to the

A Service-Level Agreement (SLA) is a contract be-

tween a network service provider and a customer that speci-

ﬁes, in measurable terms, what services the network service

provider will furnish.

Given a list of cities and the distances between each

pair of cities, TSP looks for the shortest possible route that

visits each city exactly once and returns to the origin city.

best solution found from the beginning of the pro-

cess until the current iteration. Feller et al. (Feller

et al., 2011) propose Single-objective ACO (SACO),

a MMAS metaheuristic-based algorithm that is fo-

cused on reducing the number of physical servers,

which are needed to handle the workload. Moreover,

Fajjari et al. (Fajjari et al., 2014) propose a new, ef-

ﬁcient and scalable, strategy named VNE-AC. It is

based on a MMAS metaheuristic too. Its main aim is

to reduce the amount of allocated resources for each

virtual network request. This will help to minimize

the reject rate and maximize the cloud provider

's rev-

enue. Based on extensive simulations, this proposal

makes the VM placement more effective and achieves

better resource utilization even when assuming a high

arrival rate of VM requests. Its fundamental stages

are: i) formation of solution components, ii) local-

ization of potential candidates, iii) stochastic selec-

tion of the candidate, iv) selection of the best solu-

tion, and v) updating the pheromone trail. Moreover,

VNE-AC goes beyond related strategies investigated

in existing literature, such as VNE-Greedy (Yu et al.,

2008), VNE-Cluster (Zhu and Ammar, 2006), VNE-

Subdividing (Zhu and Ammar, 2006), and VNE-Least

(Zhu and Ammar, 2006).

Most of the solutions that attempt to optimize

the placement of VMs are shifted towards process-

ing only one criterion. However, similar to any other

problem, it is usually useful to simultaneously con-

sider several criteria. For this, a new research direc-

tion aims to realize multi-objective optimized alloca-

tion of VMs. Therefore, Gao et al. (Gao et al., 2013)

propose a solution that tackles at a time the prob-

lem of resource waste and energy utilization. Know-

ing that ACS is built upon the previous AS, a mod-

iﬁed version of ACS algorithm is proposed and de-

signed to properly deal with the potential large solu-

tion space for large-scale data centers. Its name is

VMPACS. A performance benchmark is carried out

between the proposed Ant algorithm and other algo-

rithms, such as Multi-objective Grouping Genetic Al-

gorithm (MGGA

) (Xu and Fortes, 2010), SACO

al-

gorithm (Feller et al., 2011) and the Improved FFD

+ LL algorithm (Ajiro and Tanaka, 2007). To sum-

marize the results, we provide in Table 1 the energy

utilization and resource consumption comparison be-

tween the algorithms under consideration.

As depicted in Table 1, it can be noticed that VM-

PACS goes beyond MGGA. Indeed, the choice of

Such as Google, Microsoft and Amazon.

Further information about MGGA are provided in Sec-

tion 3.4

SACO is a modiﬁed MMAS algorithm for VM place-

ment.

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318

Table 1: Comparison of VMPACS and other algorithms.

Energy Utilization Resource Consumption

MGGA (Xu and Fortes, 2010) X X

Improved FFD

+ LL (Ajiro and Tanaka, 2007)

XXXX XX

VMPACS (Gao et al., 2013) XXX XXX

SACO (Feller et al., 2011) XX XXXX

placement given by VMPACS consists of: i) the data

being collected at the moment and ii) the traces kept

by a non-optimal solution. VMPACS also updates,

in a continuous way, the pheromones, which allows

a much more relevant VM placement. Evenly, VM-

PACS goes beyond Improved FFD + LL heuristic and

SACO. These results are mainly related to the number

of servers employed and to the resource utilization of

each algorithm.

3.2.3 Subgraph Isomorphism Algorithms

The subgraph isomorphism problem is a NP-hard

computational task in which two graphs are given as

input, and one must determine whether the ﬁrst graph

contains a subgraph that is isomorphic to the second

graph. Recently, several algorithms have used sub-

graph isomorphism to formulate the problem of VM

placement, i.e., to model data center topologies and

VM clusters. (Zong et al., 2014) proposes Gradin,

a new graph index framework that accelerates sub-

graph matching on dynamic graphs of numerical la-

bels. Gradin efﬁciently calculates frequent index up-

dates and eliminates unpromising matches to mini-

mize the cost. Gradin's performance is evaluated over

BCube topology (Guo et al., 2009), on query process-

ing, index update and scalability. Results show that

Gradin outperforms competitive approaches such as

VF2 (Cordella et al., 2004) up to 10 times.

(Aral and Ovatman, 2016) provides Topology

Based Mapping (TBM) algorithm that uses a sub-

graph search to locate federated clouds with a topol-

ogy isomorphic to the VM cluster. Addition-

ally, it presents RalloCloud framework for model-

ing and simulating distributed VM allocation. Us-

ing RalloCloud for TBM evaluation, results indicated

that TBM outperforms greedy heuristics in latency,

throughput, cost and acceptance rate.

3.3 Stochastic Integer Programming

Stochastic Integer Programming is introduced for sit-

uations where future demands and prices of resources

are unknown, but their expected distributions are ei-

ther known or can be computed. This is the best tech-

nique to be used in the case where we have two or

more uncertain parameters on which the cost depends.

It is helpful in estimating the variation in demands

and costs. Thereby, frequent recomputations are not

needed, but if there is an error in the estimation, users

might end up paying more. For example, the authors

in (Chaisiri et al., 2009) deﬁne Optimal Virtual Ma-

chine Placement (OVMP), an algorithm for optimally

placing the VMs on the suitable PMs. The goal is to

minimize the cost of deploying and running VMs in

a cloud provider environment, where future demands

and prices are not necessarly stable. This objective is

reached by reducing the number of used nodes. The

algorithm they deﬁne is particularly based on linear

and quadratic programming. In (Speitkamp and Bich-

ler, 2010), the authors translate the server consolida-

tion problem into a linear programming formulation.

Their approach, Branch & Bound (B & B), aims at

limiting the number of VMs in each PM. As a result,

it is certain that VMs are not hosted in a single phys-

ical server. Some design constraints were added in

order to achieve this approach, very useful to ensure

fault-tolerancy in case of outages.

3.4 Genetic Algorithm

Finally, Genetic Algorithm is particularly convenient

in cases where we need to operate on groups and

for problems where objective functions

dynamically

change. It considers additional constraints while op-

timizing the cost function, so it solves the VM in-

terference problem encountered in the Bin Packing

approach, but it requires more computing time and

higher computing resources as compared to Bin Pack-

ing. Mi et al. (Mi et al., 2010) propose a genetic

algorithm-based approach, namely GABA, to adap-

tively self-reconﬁgure the VMs in cloud data cen-

ters consisting of heterogeneous nodes. GABA can

efﬁciently decide the optimal physical placement of

VMs according to application requirements and dy-

namic environmental conditions, that may vary over

time. Moreover in the same year, the VM placement

problem is formulated in (Xu and Fortes, 2010) as

An objective function is a function to maximize or min-

imize.

A Study of Virtual Machine Placement Optimization in Data Centers

319

a multi-objective optimization problem of simultane-

ously minimizing total resource wastage, power con-

sumption and thermal dissipation costs. MGGA with

fuzzy multi-objective evaluation is investigated for ef-

ﬁciently searching the large solution space and conve-

niently combining possibly conﬂicting objectives.

4 DISCUSSION

A proper VM placement is essential for easier fail-

ure recovery, better geographical coverage and ven-

dor lock-in avoidance in the cloud computing. The

existing VM placement techniques consider various

approaches, system assumptions, features of data cen-

ters like the network topology, as well as different

evaluation approaches. Moreover, VM placement and

migration is a broad research area with various op-

timization and objectives. Some of the techniques

are only focused on a single objective, such as (Ajiro

and Tanaka, 2007), (Chaisiri et al., 2009) and (Feller

et al., 2011). Other techniques try to incorporate mul-

tiple objectives while making VM placement deci-

sions, such as (Xu and Fortes, 2010) and (Gao et al.,

2013). We can state that Bin Packing is the most em-

ployed approach. It always generates a good solution

in a correct amount of time.

It is crucial to choose a VM placement technique

that suits the needs of both the cloud user and cloud

provider. However, due to the presence of several

parameters, comparative analysis in a uniform fash-

ion of such techniques becomes quite tricky. In fact,

each of the VM placement algorithm works well un-

der certain speciﬁc conditions/objectives. Thereby, in

order to compare the efﬁciency of the previous algo-

rithms, we propose that future empirical studies will

be based on the three following metrics to measure

and evaluate the algorithms performance. Firstly, one

should take into account the energy amount consumed

by data center resources, due to the application work-

loads. The second metric to be considered is the SLA

violation percentage, which expresses the level by

which performance requirements deﬁned between the

resource provider and consumers are violated. The

SLA violation can happen when VMs sharing the

same PM need a CPU performance that cannot be pro-

vided because of energy-aware resource management

and consolidation. The provider pays a penalty to the

client in case of SLA violation. The third metric is the

number of VM migrations during the adaptation of the

VM placement (Beloglazov et al., 2012). VM migra-

tions consume time, energy and network bandwidth.

Thus, it is important to minimize the number of VM

migrations.

5 RELATED SURVEYS

Three recent surveys also discuss the state-of-the-art

of VM placement techniques in data centers. (Pires

and Bar

an, 2015) covers several axes such as ob-

jective functions (i.e., energy consumption minimiza-

tion, network trafﬁc minimization, economical costs

optimization, and performance maximization), solu-

tion techniques (i.e., algorithms and meta-heuristics),

cloud architectures (i.e., single cloud, multi-clouds,

and federated clouds), and experiment types (i.e. sim-

ulation and applications). However, (Pires and Bar

an,

2015) remains mainly statistics-oriented. Our work

provides though better details especially regarding the

solution techniques. It is also focused on energy-

aware and trafﬁc-aware solutions.

Also, we consider (Jennings and Stadler, 2015)

as a decent and comprehensive survey. It details the

different kinds of cloud resources and discusses ap-

proaches based on eight subdomains: global schedul-

ing, local scheduling, demand proﬁling, utilization es-

timation, pricing, application scaling, workload man-

agement, cloud management, and measurement stud-

ies. Being focused on resource management for cloud

environments in a general way, (Jennings and Stadler,

2015) misses quite a number of approaches in VM

placement ﬁeld. The authors refer to the latter as

“global scheduling” subdomain. In contrast, our tax-

onomy focuses on VM placement problem and covers

all the approaches that have been investigated in this

ﬁeld.

Finally, while (Usmani and Singh, 2016) offers a

similar classiﬁcation to ours, its motivation is mainly

focused on server consolidation. Our survey ad-

dresses the topic of VM placement with a broader mo-

tivation and takes into account the different types of

system architecture. Moreover, we explain the opti-

mal case for the use of each of the four discussed ap-

proaches. We certainly ﬁll a void in the literature by

providing this up-to-date survey, which is useful for

the cloud computing domain.

6 CONCLUSION AND FUTURE

DIRECTIONS

In this paper, we survey the state-of-the-art of VM

placement optimization, covering its essential ap-

proaches and key existing solutions that reduce net-

work power cost and prevent congestion of data ﬂow.

As the cost and the performance of cloud data cen-

ters become a practical concern and attract attention

of both service provider and consumer, our survey

provides a better comprehension of the existing VM

CLOSER 2017 - 7th International Conference on Cloud Computing and Services Science

320

placement algorithms that deal with power cost and

handle trafﬁc in data centers.

However, many future directions and perspectives

have not been explored yet and can be contemplated

for the future. For example, data security remains

unresolved in cloud infrastructures. Since the migra-

tion of VMs require a secure connection between both

source and target servers, we pave the way for further

work to address this issue and we mark the urge of

deﬁning reliable protocols for establishing and man-

aging a protected communication. Moreover, in or-

der to optimally beneﬁt from the features of the four

approaches, designing an hybrid solution combining

several approaches represents a future challenge. Fi-

nally, nowadays there are several resource managers

that are mostly doing the placement of VMs, like

vMotion (Murphy, 2011), the commercial product of

VMware and OpenStack (Sefraoui et al., 2012), the

open-source cloud manager. Other resource man-

agers like Kubernetes (Brewer, 2015), Swarm (Tha-

patsuwan et al., 2009), and Mesos (Hindman et al.,

2011), to cite a few, are responsible for the placement

of containers. Therefore, it will be interesting to con-

duct an exhaustive study of the existing static and dy-

namic resource managers, and to map between them

and the placement algorithm(s) they use.

ACKNOWLEDGEMENTS

This work is supported by the OCCIware

research

and development project funded by French Pro-

gramme d’Investissements d’Avenir (PIA). Likewise,

this work is partially funded by Nord-Pas de Calais

Regional Council.

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