Energy-aware VM Scheduling in IaaS Clouds using Pliant Logic

Attila Benyi

, Jozsef Daniel Dombi

and Attila Kertesz

2,1

Software Engineering Department, University of Szeged, 6720 Szeged, Dugonics ter 13, Hungary

Institute for Computer Science and Control, MTA SZTAKI, H-1111 Budapest, Kende u. 13-17, Hungary

Keywords:

Cloud Computing, VM Scheduling, Pliant System, Simulation.

Abstract:

Cloud Computing is facing an increasing attention nowadays as it is present in many consumer appliances by

advertising the illusion of inﬁnite resources towards its customers. Nevertheless it raises severe issues with

energy consumption: the higher levels of quality and availability require irrational energy expenditures. This

paper proposes a Pliant system based virtual machine scheduling approach for reducing energy consumption of

IaaS datacenters. In order to evaluate our proposed solution, we have designed a CloudSim-based simulation

environment, and applied real-world traces for the experiments. We show that signiﬁcant savings can be

achieved in energy consumption with our proposed Pliant-based algorithms, and by ﬁne-tuning the parameters

of the proposed Pliant strategy, a beneﬁcial trade-off can be set between energy consumption and execution

time.

1 INTRODUCTION

Cloud computing incorporates many aspects of shar-

ing software and hardware solutions, including com-

puting and storage resources, application runtimes

or complex application functionalities. The cloud

paradigm changed the way people look at computing

infrastructures. First, one does not need to be expert

in infrastructure administration, operation and main-

tenance even if large scale systems are utilized. Sec-

ond, the elasticity of Infrastructure as a Service clouds

allows these systems to better follow the users’ actual

demands. However, there is also an adversary effect:

the virtualized nature of these systems detaches users

from several operational issues like energy efﬁcient

usage, that has been addressed previously in the con-

text of parallel and distributed systems, and largely

remains unnoticed (Buyya et al., 2009; Schubert and

Jeffery, 2012).

The Cloud computing technology made a qualita-

tive breakthrough as it is present in many consumer

appliances including various mobile devices. They

advertise the illusion of inﬁnite resources towards

the consumers, meanwhile it also raises severe issues

with energy consumption: the higher levels of qual-

ity and availability require irrational energy expendi-

tures, according to some experts the consumed energy

of resources spent for idling represent a considerable

amount (Lef

evre and Orgerie, 2009). Current trends

are claimed to be clearly unsustainable with respect

to resource utilisation, CO

footprint and overall en-

ergy efﬁciency. It is anticipated that further growth is

limited by energy consumption, furthermore compet-

itiveness of companies are and will be strongly tied to

these issues.

As cloud services become more and more popular,

small- and medium-sized cloud service providers will

soon face increasing user demands that cannot be met

with their current infrastructures. These user demands

range from occasional needs for extreme amount of

resources (compared to the provider’s current infras-

tructure) to the need for multi-site virtual machine de-

ployment options that enable enhanced services such

as disaster recovery. Thus these providers need to in-

crease the size of their infrastructure by introducing

multiple datacenters covering various locations, and

offering unprecedented amount of resources. Cur-

rent IaaS solutions provide the opportunity for ser-

vice providers to satisfy these needs by focusing their

attention to non-technical issues like the increased

operating cost of their datacenters. Despite energy

consumption is a major component of these operat-

ing costs, current IaaS solutions barely handle the in-

frastructure with energy aware solutions. Therefore

providers were restricted to reduce their consumption

on the hardware level so far, independently from the

applied IaaS solution. Energy costs are also increas-

ing, and datacenter equipment is stressing power and

519

Benyi A., Daniel Dombi J. and Kertesz A..

Energy-aware VM Scheduling in IaaS Clouds using Pliant Logic.

DOI: 10.5220/0004842905190526

In Proceedings of the 4th International Conference on Cloud Computing and Services Science (CLOSER-2014), pages 519-526

ISBN: 978-989-758-019-2

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

cooling infrastructures, thus the main issue is not the

current amount of data center emissions but the fact

that these emissions are raising faster than any other

carbon emission (Berral et al., 2010). Although these

improvements in hardware are crucial, we believe that

the energy consumption could also be signiﬁcantly

reduced with software in over-provisioned IaaS sys-

tems. Over-provisioning is a key behaviour at smaller

sized providers, who offer services for users with oc-

casional peaks in resource demands.

Reducing the carbon footprint of European coun-

tries is also a must and expected by the European

Commission, as well as to increase the number and

size of European Cloud providers (Schubert and Jef-

fery, 2012). By federating these providers, more com-

petitive initiatives can be founded, that can be sophis-

tically managed to meet these expectations. The gen-

eral goal of the management layer in a Cloud fed-

eration is to distribute load among the participating

cloud providers, to enhance user satisfaction by ﬁl-

tering out underperforming providers, and schedule

and execute service calls with minimized energy con-

sumption within the selected IaaS system. To achieve

this, we have already proposed an architecture called

Federated Cloud Management (FCM – as introduced

in (Kecskem

eti et al., 2012)). In this holistic ap-

proach a two-level brokering solution is used: a meta-

brokering component is used to direct service calls to

providers, and then a cloud-brokering component to

map these calls onto an optimized number of virtual

machines.

In this paper we target the later, cloud-brokering

layer, and we focus on the energy-aware manage-

ment of datacenters of single cloud providers special-

ized for provisioning task-based cloud applications.

In order to enable experimentation in this ﬁeld, we

have developed a CloudSim-based simulation envi-

ronment. To cope with the high uncertainty and un-

predictable load present in these heterogeneous, virtu-

alized large-scale systems, we apply a Pliant system

based approach (Dombi, 2012) to the management

of these systems, which is similar to a fuzzy system

(Dombi, 1982).

Therefore the main contributions of this paper are:

(i) the development of a cloud simulation environ-

ment for task-based cloud applications, (ii) the design

of an energy-aware and Pliant-based VM scheduling

algorithm for VM management Clouds, and (iii) the

evaluation of the proposed algorithms in the extended

simulation environment with real-world traces.

The remainder of this paper is as follows: Sec-

tion 2 presents the related VM management ap-

proaches in datacenters; Section 3 introduces our ex-

tended simulation architecture; Section 4 introduces

the advanced scheduling algorithms using the Pliant

method for VM scheduling; and Section 5 describes

the evaluation methodology and the simulation re-

sults. Finally, Section 6 summarizes the main con-

tributions of the paper.

2 RELATED WORK

Regarding energy efﬁciency in a single cloud, Cioara

et al. (Cioara et al., 2011) introduced an energy aware

scheduling policy to consolidate power management

by using reinforcement learning techniques to restore

a service center to an energy efﬁcient state. Feller

et al. proposed a dynamic cluster manager called

Snooze (Feller et al., 2010), which is able to dynam-

ically consolidate the workload of a heterogeneous

large-scale cluster composed of resources using vir-

tualization. In a later work (Feller et al., 2012), they

use power meters to monitor energy usage of cloud

resources, and estimate the resource usage of VMs.

Their mechanisms address VM placement, relocation

and migration by keeping VMs on as few nodes as

possible.

Cardosa et al. (Cardosa et al., 2009) presented

a novel suite of techniques for placement and power

consolidation of VMs in datacentres taking advantage

of the min-max and shares features inherent in virtu-

alization technologies, like VMware and Xen. These

features allow to specify the minimum and maximum

amount of resources that can be allocated to a VM,

and provide a shares based mechanism for the hyper-

visor to distribute spare resources among contending

VMs. Lee et al. (Lee et al., 2010) discuss service re-

quest scheduling in Clouds based on achievable prof-

its. They propose a pricing model using processor

sharing for composite services in Clouds.

Lucas-Simarro et al. (Lucas-Simarro et al., 2013)

proposed different scheduling strategies for optimal

deployment of services across multiple clouds based

on various optimization criteria. The examined

scheduling policies include budget, performance, load

balancing and other dynamic conditions, but they ne-

glected energy efﬁciency, which is the aim of our

work.

Regarding fuzzy approaches, Salleh et al. (Salleh

et al., 1999) have shown how to set up and use fuzzy

logic in a traditional way for dynamic task schedul-

ing in multiprocessor systems. We have already pub-

lished a paper (Dombi and Kert

esz, 2011) on applying

the Pliant approach to job scheduling in Grids. In this

current paper we would like to show that it is also pos-

sible to use Pliant system for scheduling, with only a

few rules. The novelty of this contribution lies in the

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520

way we apply the Pliant system to Clouds: the way

we select cloud-speciﬁc properties as parameters of

the Pliant system.

Concerning cloud simulations, Berge et al.

(vor dem Berge et al., 2012) have designed a simu-

lator called SVD within the CoolEmAll project for

investigating energy consumption in datacenters. It

is an extended version of the GSSIM simulator, and

they are planning to support application modeling and

proﬁling through benchmarks. Regarding federation-

wide simulations, Sotiriadis et al. (Sotiriadis et al.,

2013) investigated inter-cloud simulations by devel-

oping the SimIC simulation toolkit that is able to

mimic the inter-cloud service formation to enable the

investigation of service-oriented cloud utilization, but

they neglect energy efﬁciency.

3 SIMULATION OF CLOUDS

We have used the CloudSim simulator (Calheiros

et al., 2011) to develop a simulation environment for

our research. Beloglazov and Buyya (Beloglazov and

Buyya, 2012) have already started to examine how en-

ergy efﬁciency could be investigated within this sim-

ulator. Datacenters consume huge amounts of energy

resulting in high operating costs and increased car-

bon dioxide emissions. The dynamic consolidation

of VMs using live migration and switching off idle

nodes can be used to optimize resource usage and re-

duce energy consumption, but they argue that aggres-

sive consolidation may lead to performance degrada-

tion. They proposed adaptive heuristics for dynamic

consolidation of VMs based on an analysis of histor-

ical data from the resource usage by VMs, while en-

suring a high level of adherence to the Service Level

Agreements (SLA). They used PlanetLab trace ﬁles

(Park and Pai, 2006) workload logs to simulate load

changes of continuously running services in VMs.

These traces contain records of each VM’s periodic

utilization, thus the simulation assumes each VM is

going to process only one task (called as cloudlet in

CloudSim) at a time as a service.

In this work our goal was to investigate task-based

(HPC/HTC) cloud applications executed by a single

cloud provider possibly having more than one data-

center. Since CloudSim is tailored to the evaluation

of continuously running web-based applications (Be-

loglazov and Buyya, 2012), we decided to extend this

simulation environment to suite our needs.

Our approach is slightly different to the one used

by the original version of CloudSim, as we tried send-

ing cloudlets with varying parameters, such as start

time and length at random intervals. For that purpose

Listing 1: Pseudo code of the default OptUtil algorithm

lowestVm = f i r s t VM w i th t h e same

qu eue t y p e as t h e c l o u d l e t ;

FOREACH ( v m l i s t a s vm)

IF (vm . u t i l i z a t i o n ( ) <

lowestVm . u t i l i z a t i o n ( )

AND vm . q ueu eTy pe ==

lowestVm . q ueu eTy pe )

lowestVm=vm ;

IF ( lowestVm . u t i l i z a t i o n > 10 0 )

IF ( t r y t o c r e a t e a new vm )

lowestVm = new vm ;

c l o u d l e t . setVm = lowestVm ;

we used the log ﬁles provided by Prezi Inc. (Prezi,

2013) (discussed in detail in Section 5). These log

ﬁles contain detailed data on each cloudlet received,

such as its start time, length and queue type. To

adapt CloudSim to the new features, several changes

had to be made. One of the crucial changes was in

the CloudletScheduler component, so each VM could

handle multiple cloudlets at the same time. As long

as the VM’s utilization is below 100%, it can process

new cloudlets, and once a VM reaches its full utiliza-

tion, further cloudlets get queued. Once a VM has no

cloudlets left to process, it is shut down, and if a host

has no remaining VMs, it is shut down as well. Each

host’s power consumption is based on a power model,

which is based on a benchmark result provided by

SPEC (SPEC, 2013). We used 5 different power mod-

els to make the difference between varying algorithms

more obvious. Each datacenter sums up the power

consumed by its hosts for every timeframe a cloudlet

is being processed, giving us a close approximation

of the amount of power and time needed to complete

all the requested cloudlets. For each cloudlet a VM

is chosen by our default VM scheduling algorithm

called ’OptUtil’ shown in Listing 1. The hosts (physi-

cal machines) created during the simulations differ in

their characteristics, altogether 5 types of hosts were

used. However, while there are different hosts, only

one type of VM was used in all simulations.

In case every VMs utilization is over 100%, the

algorithm will try to create a new one, thus ensuring

the lowest process time. For each new VM the host

is chosen based on its power model, and we are as-

suming that every host will be fully utilized, so the

host with the lowest power consumption on 100% uti-

lization will be submitted, ensuring the lowest power

consumption. In the following section we discuss the

Pliant-based VM scheduling solution.

Energy-awareVMSchedulinginIaaSCloudsusingPliantLogic

521

4 PLIANT SCHEDULING

APPROACH

Fuzzy sets were introduced by Lofti Zadeh in 1965

with the aim of reconciling mathematical modeling

and human knowledge in the engineering sciences.

Most of the building blocks of the theory of fuzzy

sets were proposed by him, especially fuzzy exten-

sions of classical basic mathematical notions like log-

ical connectives, rules, relations and quantiﬁers. The

Pliant system (Dombi, 2012) is a kind of fuzzy theory

that is similar to a traditional fuzzy system (Dombi,

1982). The difference between the two systems lies

in the choice of operators. The Pliant system has a

strict, monotonously increasing t-norm and t-conorm,

and the following expression is valid for the generator

function:

(x) f

(x) = 1, (1)

where f

(x) and f

(x) are the generator functions for

the conjunctive and disjunctive logical operators, re-

spectively. This system is deﬁned in the [0,1] interval.

In our previous paper (Dombi and Kert

esz, 2011), we

developed a scheduling component that uses the Pli-

ant system to select a good performing Grid broker

for a user’s job even under conditions of high uncer-

tainty. The algorithm we developed calculates a score

for each cloudlet using the cloud’s properties. The

calculation step includes a normalization step, where

we apply a special Sigmoid function. In the normal-

ization step it should be mentioned that if the normal-

ized value is close to one, it means it is a more valu-

able property, and if the normalized value is close to

zero, it means it is a less prioritized property. For ex-

ample, if the counter of power consumption is high,

the normalization algorithm should give a value close

to zero. In our previous work (Dombi and Kert

esz,

2011) we found that if we use the aggregation opera-

tor to calculate the score number, we can achieve bet-

ter results.

Here, we created two scheduling algorithms in or-

der to handle the energy aware management case with

a similar approach. One considers time and the other

considers energy for optimization. There are hosts in

the simulated datacenters, and each host can run sev-

eral VMs. This environment can be described with the

same three properties, namely a power usage counter

(PUC), the power consumption counter (PCC) and the

number of processors (PROC):

• The power usage counter gives performance of the

CPU usages of the given simulation time. The

value can be larger than 100, which means that

there are more cloudlets in the VM’s queue.

Table 1: Parameters of the Sigmoid function.

Property Time Energy

Property Alpha Lambda Alpha Lambda

PUC 0.5 -4.0 0.5 -4.0

PCC 85.0 -0.08 75.0 -0.08

PROC 1.0 0.8 1.0 0.8

• The power consumption counter gives the energy

usage of the given host at a given time. The value

is generally between 40 and 120 MIPS, but it de-

pends on the actual physical processor.

• The number of processors gives the available

number of processors of a host.

We have developed two Pliant decision making al-

gorithms that take into account the above-mentioned

properties and decide to which VM a cloudlet should

be submitted: one optimizes cloudlet executions for

time, and the other one for energy. We use differ-

ent normalization for the two strategies. First we start

with a normalization step and we apply different kinds

of Sigmoid functions to normalize the environment’s

property value. We examine the environment’s vari-

able and deﬁne the value of the Sigmoid’s parameter.

Table 1 shows the predeﬁned values of the parameters

of the normalization functions.

In this environment every host has 4 processors, so

after the normalization the normalized property value

is the same for all instances. We would like to em-

phasize that it is better if we use less power, therefore

we created two different parameter sets: one for time-

aware and one for energy-aware scheduling. As we

can see in Figure 1, the minimum energy in this en-

vironment is around 40 and the maximum is around

120. Here we can see that if the number of power

consumption is increasing then the value of the nor-

malized function is decreasing. The opposite is true

for the number of processors.

After the normalization step we modify the nor-

malized value to emphasize the importance of the re-

sult. To achieve this we will modify the normalized

value by using the Kappa function with ν = 0.4 and

λ = 3.0 parameters:

(x) =

1 +



1−ν

1−x



(2)

Finally to calculate a VM’s score number for the

given cloudlet, we use the aggregation operator:

ν,ν

, · · · , x

) =

1 +

1−ν

∏

i=1

1−x

, (3)

where ν is the neutral value and ν

is the threshold

value of the corresponding negation. Here we don’t

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Figure 1: Utilized normalized function for the power con-

sumption (PCC).

want to threshold the result so both parameters have

the same value 0.5. The result of the calculation is

always a real number that lies in the [0,1] interval. So

we calculate the score for all VM to ﬁnd which VM

is the most suitable for our strategy. If the best score

value is very low (the value depends on the strategy),

then we try to create a new VM.

5 EVALUATION

In order to investigate the energy consumption of

cloud providers in our extended simulation environ-

ment, we have used real-world trace ﬁles of an inter-

national company called Prezi Inc, who offers a pre-

sentation editing service, which is available on multi-

ple platforms, therefore they have to convert some of

the uploaded media ﬁles to other formats before they

can display them on all devices. In April 2013, they

launched a competition titled ”Scale Contest” (Prezi,

2013) for university students to test their knowledge

of control and queueing theories on real-life prob-

lems. Their conversion processes are carried out on

virtual machines: at peak times, they need to launch

more instances of these VMs, but over the weekend

they can stop most of them. This campaign was initi-

ated in order to ﬁnd a suitable algorithm that launches

the exact number of VMs for a given workload. They

published log ﬁles on their website containing work-

load traces for two weeks of utilization, which serves

as a basis for algorithmic experimentations.

They operate three queues in their system for the

jobs participating in the conversion processes:

• export: contains jobs which result in download-

able zipped prezis.

• url: these jobs download an image from a URL

and insert them into a prezi.

• general: all other conversion jobs (audio, video,

pdf, ppt, etc).

The lines of the published workload traces have

the following format: ”2012-12-14 21:35:12 237 gen-

eral 9.134963”. This means that at the given time, a

job enters the general queue with the id 237, and the

job will take 9.134963 seconds to run. These logs had

to be used as input by the competitors. They con-

tain three weeks of actual data accumulated by Prezis

conversion system, and the ﬁrst two weeks of logs are

publicly available. They planned to use unpublished

logs from the third and fourth week to evaluate your

submissions to the competition.

Table 2: Evaluation results for RoundRobin.

Hosts Cloud- VMs Energy Time

lets (kWh) (sec)

100 10000 1< 63.20 25200

100 50000 1< 104.66 39000

500 50000 1< 143.62 48600

500 100000 1< 381.37 70200

Table 3: Evaluation results for OptUtil.

Hosts Cloud- VMs Energy Time

lets (kWh) (sec)

100 10000 1< 18.90 7500

100 50000 1< 87.12 32400

500 50000 1< 90.41 7200

500 100000 1< 197.26 15000

For a preliminary evaluation phase we used the

trace ﬁle of the ﬁrst week. We have performed ex-

periments with datacenters having 100 to 500 hosts,

and submitted 10000 to 100000 jobs from the log. By

default we used a round robin strategy to schedule the

logs to the available VMs (1 at the beginning), and

if no more available VM was present in the system

(that could execute the job without any delay) at a

given time, we have deployed another one continu-

ously. The results of this evaluation can be seen in

Table 2. We have also executed similar simulations

by applying our proposed optimized utilization strat-

egy called ’OptUtil’, that deploys another VM, if the

available ones are at least 80% loaded. The results of

this second evaluation can be seen in Table 3.

From these preliminary evaluation we can see that

our proposed algorithm performed better than the

round robin, both in energy consumption and execu-

tion time.

Energy-awareVMSchedulinginIaaSCloudsusingPliantLogic

523

To develop Pliant-based algorithms, we created

three initial strategies: the ﬁrst one uses only one VM

to execute all submitted jobs (MINIMUM), the sec-

ond deploys a new VM for all jobs (MAXIMUM),

and the third uses randomized VM selection from the

available VMs (smartly prioritizing the less loaded

ones), and deploys a new one, if no free VM is found

(SMARTRANDOM). Tables 5, 4 and 6 summarize

the results of evaluating these algorithms. From these

results we can see that utilizing the lowest number of

VMs results in the lowest energy consumption, but of

course on the expense of the execution time, which is

the highest in this case.

Table 4: Evaluation results for MAXIMUM.

Hosts Cloud- VMs Energy Time

lets (kWh) (sec)

100 1000 241 7.64 759

100 10000 241 76.35 4088

100 50000 241 365.35 14220

100 100000 241 934.22 39224

Table 5: Evaluation results for MINIMUM.

Hosts Cloud- VMs Energy Time

lets (kWh) (sec)

100 1000 3 0.19 8179

100 10000 3 1.91 81008

100 50000 3 6.54 240940

100 100000 3 13.87 461724

Table 6: Evaluation results for SMARTRANDOM.

Hosts Cloud- VMs Energy Time

lets (kWh) (sec)

100 1000 3 0.20 8619

100 10000 3 1.53 60298

100 50000 3 5.77 198060

100 100000 3 12.50 386074

Based on these preliminary evaluations we have

created a Pliant-based strategy (PLIANTDEFAULT),

ﬁrst focusing on execution time reduction with some

energy savings. For its default algorithm Table 7

shows the results of the simulation. This table shows

that it could achieved signiﬁcant performance gains

in terms of execution time as expected, but it also

had much higher energy consumption than the MINI-

MUM and SMARTRANDOM initial strategy.

Therefore we have modiﬁed the parameters of the

applied Pliant system, and created more focused al-

gorithms. In Table 8 we used a Pliant version that

is more focused on execution time savings (PLIANT-

TIME), while in Table 9 we modiﬁed a Pliant param-

eter to focus on energy savings (PLIANTENERGY).

Table 7: Evaluation results for PLIANTDEFAULT.

Hosts Cloud- VMs Energy Time

lets (kWh) (sec)

100 1000 14 0.26 749

100 10000 16 2.87 3768

100 50000 24 17.26 14240

100 100000 25 53.21 39304

Table 8: Evaluation results for PLIANTTIME.

Hosts Cloud- VMs Energy Time

lets (kWh) (sec)

100 1000 13 0.21 629

100 10000 16 2.77 4128

100 50000 21 15.20 14380

100 100000 21 43.55 39274

Figure 2 shows comparison diagrams concerning the

last rows of the tables.

As a result of the evaluations we can state that

for minimal energy consumption the least amount of

VMs should be used with smartly randomized VM

selection. Nevertheless, when there is a need for ex-

ecution time optimizations, we have to ﬁnd a trade-

off between energy consumption and execution time.

With our proposed Pliant-based VM scheduling algo-

rithms we have shown that signiﬁcant savings can be

achieved in energy consumption with moderate exe-

cution time reductions.

6 CONCLUSION

Cloud Computing is facing an increasing attention

nowadays and it raises severe issues with energy con-

sumption: the higher levels of quality and availability

require irrational energy expenditures. Reducing the

carbon footprint of European countries is also a must

and expected by the European Commission, as well

as to increase the number and size of European Cloud

providers.

In this paper we have proposed a Pliant system

based virtual machine scheduling approach for reduc-

ing energy consumption of IaaS datacenters. We have

designed a CloudSim-based simulation environment

for task-based cloud applications, and applied real-

world traces for the performed experiments. We have

shown that signiﬁcant savings can be achieved in en-

ergy consumption with our proposed Pliant-based al-

gorithms, and by ﬁne-tuning the parameters of the

proposed Pliant strategy, a beneﬁcial trade-off can be

set between energy consumption and execution time.

Our future work aims at automating the parame-

ter selection in different IaaS systems, and adapting

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Figure 2: Comparison diagrams for 100000 cloudlets.

Table 9: Evaluation results for PLIANTENERGY.

Hosts Cloud- VMs Energy Time

lets (kWh) (sec)

100 1000 12 0.18 669

100 10000 16 2.34 3788

100 50000 18 12.99 14380

100 100000 18 34.55 39274

the proposed approach in production-level academic

Clouds.

ACKNOWLEDGEMENTS

The research leading to these results has received

funding from the EU FP7 IDGF-SP project under

grant agreement 312297, and it was supported by

the European Union and the State of Hungary, co-

ﬁnanced by the European Social Fund in the frame-

work of TAMOP 4.2.4. A/2-11-1-2012-0001 ’Na-

tional Excellence Program’.

REFERENCES

Beloglazov, A. and Buyya, R. (2012). Optimal online de-

terministic algorithms and adaptive heuristics for en-

ergy and performance efﬁcient dynamic consolidation

of virtual machines in cloud data centers. Concur-

rency and Computation: Practice and Experience,

24(13):1397–1420.

Berral, J. L., Goiri, I., Nou, R., Juli

a, F., Guitart, J., Gavald

R., and Torres, J. (2010). Towards energy-aware

scheduling in data centers using machine learning.

In de Meer, H., Singh, S., and Braun, T., editors, e-

Engery, pages 215–224. ACM.

Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., and

Brandic, I. (2009). Cloud computing and emerging

IT platforms: Vision, hype, and reality for delivering

computing as the 5th utility. Future Generation Comp.

Syst, 25(6):599–616.

Calheiros, R. N., Ranjan, R., Beloglazov, A., Rose, C. A.

F. D., and Buyya, R. (2011). Cloudsim: a toolkit for

modeling and simulation of cloud computing environ-

ments and evaluation of resource provisioning algo-

rithms. Softw, Pract. Exper, 41(1):23–50.

Cardosa, M., Korupolu, M. R., and Singh, A. (2009). Shares

and utilities based power consolidation in virtualized

server environments. In Integrated Network Manage-

ment, pages 327–334. IEEE.

Cioara, T., Anghel, I., Salomie, I., Copil, G., Moldovan,

D., and Kipp, A. (2011). Energy aware dynamic re-

source consolidation algorithm for virtualized service

centers based on reinforcement learning. In ISPDC,

pages 163–169. IEEE.

Dombi, J. (1982). A general class of fuzzy operators, the De

Morgan class of fuzzy operators and fuzziness mea-

sures induced by fuzzy operators. Fuzzy Sets and Sys-

tems, 8:149–163.

Dombi, J. (2012). Pliant operator system. In Fodor, J. C.,

Klempous, R., and Araujo, C. P. S., editors, Recent

Advances in Intelligent Engineering Systems, volume

378 of Studies in Computational Intelligence, pages

31–58. Springer.

Dombi, J. D. and Kert

esz, A. (2011). Advanced schedul-

ing techniques with the pliant system for high-level

grid brokering. In Filipe, J., Fred, A., and Sharp, B.,

editors, Agents and Artiﬁcial Intelligence, volume 129

of Communications in Computer and Information Sci-

ence, pages 173–185. Springer Berlin Heidelberg.

Feller, E., Rilling, L., Morin, C., Lottiaux, R., and Lep-

rince, D. (2010). Snooze: A scalable, fault-tolerant

and distributed consolidation manager for large-scale

clusters.

Feller, E., Rohr, C., Margery, D., and Morin, C. (2012). En-

ergy management in iaaS clouds: A holistic approach.

In Proc. 2012 IEEE Fifth International Conference on

Cloud Computing (5th IEEE CLOUD’12), pages 204–

212, Honolulu, HI, USA. IEEE Computer Society.

Kecskem

eti, G., Kert

esz, A., Marosi, A., and Kacsuk, P.

(2012). Interoperable resource management for estab-

lishing federated clouds. In Villari, M., Brandic, I.,

and Tusa, F., editors, Achieving Federated and Self-

Manageable Cloud Infrastructures: Theory and Prac-

tice, pages 18–35. IGI Global, Hershey.

Energy-awareVMSchedulinginIaaSCloudsusingPliantLogic

525

Lee, Y. C., Wang, C., Zomaya, A. Y., and Zhou, B. B.

(2010). Proﬁt-driven service request scheduling in

clouds. In CCGRID, pages 15–24. IEEE.

Lef

evre, L. and Orgerie, A.-C. (2009). Towards energy

aware reservation infrastructure for large-scale exper-

imental distributed systems. Parallel Processing Let-

ters, 19(3):419–433.

Lucas-Simarro, J. L., Moreno-Vozmediano, R., Montero,

R. S., and Llorente, I. M. (2013). Scheduling strate-

gies for optimal service deployment across multiple

clouds. Future Generation Comp. Syst, 29(6):1431–

1441.

Park, K. and Pai, V. S. (2006). Comon: a mostly-scalable

monitoring system for planetlab. Operating Systems

Review, 40(1):65–74.

Prezi (2013). Prezi Inc. ”scale contest” website,

http://prezi.com/scale/, april 2013.

Salleh, S., Sanugi, B., and Jamaluddin, H. (1999). Fuzzy

logic model for dynamic multiprocessor scheduling.

Schubert, L. and Jeffery, K. (2012). Advances in clouds -

research in future cloud computing.

Sotiriadis, S., Bessis, N., and Antonopoulos, N. (2013).

Towards inter-cloud simulation performance analy-

sis: Exploring service-oriented benchmarks of clouds

in simIC. In Barolli, L., Xhafa, F., Takizawa, M.,

Enokido, T., and Hsu, H.-H., editors, AINA Work-

shops, pages 765–771. IEEE Computer Society.

SPEC (2013). Spec website, http://www.spec.org, august

2013.

vor dem Berge, M., Costa, G. D., Kopecki, A., Oleksiak,

A., Pierson, J.-M., Piontek, T., Volk, E., and Wes-

ner, S. (2012). Modeling and simulation of data cen-

ter energy-efﬁciency in coolemall. In Huusko, J.,

de Meer, H., Klingert, S., and Somov, A., editors,

E2DC, volume 7396 of Lecture Notes in Computer

Science, pages 25–36. Springer.

CLOSER2014-4thInternationalConferenceonCloudComputingandServicesScience

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