Towards Balancing Energy Savings and Performance for Volunteer

Computing through Virtualized Approach

abio Rossi

1 a

, Tiago Ferreto

2 b

, Marcelo Conterato

2 c

, Paulo Souza

2 d

Wagner Marques

2 e

, Rodrigo Calheiros

3 f

and Guilherme Rodrigues

4 g

Federal Institute of Education, Science and Technology Farroupilha, Alegrete, Brazil

Polytechnic School, Pontiﬁcal Catholic University of Rio Grande do Sul, Porto Alegre, Brazil

Western Sydney University, Parramatta, Australia

Federal Institute of Education, Science and Technology Sul Rio-grandense, Charqueadas, Brazil

Keywords:

Energy-efﬁcient, Grid Computing, Performance-aware, Virtualization, Volunteer Computing.

Abstract:

Computational grids consist of distributed environments where partner institutions offer hosts along with com-

putational resources that can be used by all members of the grid. When an application needs to run in such

environment, it allocates a portion of hosts necessary for its executions. Traditionally, the workload imposed

on computational grids has a characteristic of being bag-of-tasks (BoT). It means that multiple replicas are

submitted to different hosts, and when a response is processed, such replicas are either ignored or released.

On resource allocation, as the grid is distributed among different participants, only idle resources can be leased

by a new application. However, due to the behavior of BoTs, many allocated resources do not use their re-

sources in their entirety. Another important fact is that only fully idle hosts can be added to the grid pool, and

used only at these times. From the above, this paper proposes an approach that uses underutilized resource

slice of grid hosts through virtualization, adjusting the use of grid applications to the leftover resources from

daily hosts usage. It allows grid applications to run, even when hosts are in use, as long as there is an idle

slice of resources, and their use does not interfere with the host’s current running. To evaluate this approach,

we performed an empirical evaluation of a virtualized server running applications concurrently with a virtu-

alized grid application. The results have showed that our scheme could accelerate the performance of grid

applications without impacting on higher energy consumption.

1 INTRODUCTION

Usually, the volunteer computing (Nov et al., 2010) is

a distributed computing system often associated with

signiﬁcant scientiﬁc projects, which uses the power

of idle machines around the world to process substan-

tial amounts of information. The workload is divided

and sent to different hosts, and them can handle such

workload simultaneously, reducing costs and the time

https://orcid.org/0000-0002-2450-1024

https://orcid.org/0000-0001-8485-529X

https://orcid.org/0000-0001-7782-812X

https://orcid.org/0000-0003-4945-3329

https://orcid.org/0000-0003-3304-5611

https://orcid.org/0000-0001-7435-2445

https://orcid.org/0000-0002-0389-2900

spent on studies and research as well as providing a

way to enhance the use of resources. Such hosts use

client software that connects it to a distributed and

heterogeneous computational grid, creating a highly-

scalable environment. With the increased processing

power of servers, computational resources are get-

ting underutilized. The most of the user applications

are not parallel enough to take advantage of these re-

sources, such as sequential applications on multicore

processors.

Due to this fact, although there are applications

that make intensive usage of resources (Hwang et al.,

2003), this is not the reality of the most of today’s grid

computing environment. It means that although the

hosts are running an application, there is a signiﬁcant

slice of idle resources available for use. It seems to

be a waste of both resources and power, and it would

422

Rossi, F., Ferreto, T., Conterato, M., Souza, P., Marques, W., Calheiros, R. and Rodrigues, G.

Towards Balancing Energy Savings and Performance for Volunteer Computing through Virtualized Approach.

DOI: 10.5220/0007733304220429

In Proceedings of the 9th International Conference on Cloud Computing and Services Science (CLOSER 2019), pages 422-429

ISBN: 978-989-758-365-0

be interesting to use for volunteer computing that part

unused of the idle user, concomitantly. An important

issue is that such a technology that uses idle resources

does not interfere with all other user applications.

One of the current technologies that enable to use

that slice of idle processing, or even allocate slices of

available resources, increasing or decreasing depend-

ing on the demand in each slice, is the virtualization

(Uhlig et al., 2005). Virtualization enables to run in-

dependent operating systems or applications, concur-

rently, on the same resources. The advantage of this

approach compared with the traditional architecture

of volunteer computing is that there is no interrup-

tion in grid service. Although the slices of available

resources used by the virtual machine vary over the

time, this new proposal could increase the execution

time of grid applications.

However, a signiﬁcant resource usage also in-

creases the power consumption. Currently, there are

several papers studying trade-offs between perfor-

mance and power consumption such as (Siam et al.,

2010). It is a growing concern, given that the physi-

cal limits of silicon have been achieved (Bohr, 1998).

This paper combines some technologies that can re-

duce such trade-off. The big question answered by

this paper, in addition to measuring the performance

of a voluntary virtualized computing environment,

is to check how much energy savings this proposal

promotes. Moreover, this paper shows that the bal-

ance between most magniﬁcent performance (execu-

tion time of the grid applications) and energy-saving,

exposing this trade-off.

This paper provides two classical problems solu-

tions of volunteer computing:

• The ﬁrst refers to the increase in energy consump-

tion. The idle processor usually has lower power

consumption than when it is active. A participant

may leave the grid host connected for 24 hours,

and disable the power saving features, such as sus-

pension. Also, if adequate cooling is not in place,

this constant load at host volunteer can cause over-

heating.

• The second problem is the decreased performance

of the host. If the application tries to execute vol-

unteer computing while the computer is in use, it

can affect both host and grip application perfor-

mance. It is due to the increased processor, cache,

disk I/O, and network I/O contentions.

Even high-performance applications do not use all

available features at all times. In these environments,

there are some moments of idleness or some idle slice

of resources. With this in mind, our proposal ana-

lyzes whether a virtualized grid application could be

used for these slices, whether in an HPC, cloud or fog

computing environment, in order to always keep grid

application running, but without interfering with the

performance of other applications competing with the

environment. In this way, the primary goals of this

paper are:

• Verify if the virtualization layer isolates the virtu-

alized application from the user applications;

• Test different resources usage rates for virtualized

grid applications;

• Propose a new approach allowing ﬂexibility of

grid applications;

• Find the limits of the trade-off between perfor-

mance and power consumption.

This paper is organized as follow: in Section 2, it

describes the concepts of volunteer computation and

virtualization. Section 3 presents some related works.

Section 4 presents the environment used as a testbed

along with its features. Section 5 shows evaluations

and discussion about the outcomes and; ﬁnally, Sec-

tion 6 shows the conclusions and future work.

2 BACKGROUND

This section presents general concepts of volunteer

computing and virtualization, a suite of technologies

studied and assessed in this paper.

2.1 Volunteer Computing

Volunteer computing (Nov et al., 2010) has gained

increasing importance because of the computing ca-

pacity currently available on personal computers. For

many scientiﬁc works, it is estimated that this strat-

egy can complement (or even replace) the need for

signiﬁcant investments in high-performance comput-

ing infrastructures.

This approach is based on voluntary work, where

computational and storage resources are ceded to re-

search projects at no cost. This technique allows the

participation of citizens in scientiﬁc research in di-

rect and real-time, offering the time of processing of

their computers for computational computation of sci-

entiﬁc interest by distributed computing techniques.

In this way, volunteer computing aims to offer

for large projects, the ability to idle servers and per-

sonal computers around the world to process signif-

icant amounts of information, thereby reducing the

processing time of a given experiment. Anyone can

be a volunteer. It is necessary to install client soft-

ware on the computer and sign up for some project.

Towards Balancing Energy Savings and Performance for Volunteer Computing through Virtualized Approach

423

This software is responsible for communication with

the personal computer on the Internet server of the

project and the scheduling of tasks in idle cycles of

personal computing.

Projects that would take thousands of years to pro-

cess the information has its execution time reduced

considerably using the volunteer computing through

the problem division in millions of small units that

can be processed in parallel and distributed architec-

ture. The central server sends small data packets to

idle computers registered in the system. Although

some of these projects for common causes, requiring

rugged computers of large research centers, most of

them are satisﬁed with personal computers, either at

home or work, it has to offer.

Thus, these data are processing by the personal

computers, obtaining results that are returned through

the Internet to a central server. Such data are received

by the server, logged, analyzed, and the process starts

again, until the entire workload completion. From

the above, a very popular model in this type of envi-

ronment consists of bag-of-tasks, i.e., a chunk of the

problem to be solved is sent to several workers in the

grid, who process such chunk in idle cycles and return

the processing result to the grid Servers.

2.2 Virtualization

In the past few years, processing capacity in com-

puters has considerably increased, even though there

was a signiﬁcant lack of its usage. There are situa-

tions where applications could be executed more efﬁ-

ciently, improving the processor. One of the solutions

for that is the use of virtualization (Uhlig et al., 2005),

which has been applied to satisfactory outcomes more

often.

Considering virtualization, it can be done to disso-

ciate the hardware to the operating system, bringing

new and useful tools. Virtualization allows the user to

control processor, memory, storage and other guest’s

operating system resources (virtual machines); thus,

each guest system receives the necessary amount of

resources. This control eliminates the risk of a pro-

cess that uses all available memory or all processor

load.

This type of ﬂexibility changes the traditional con-

cept of server usage and capacity planning. In virtu-

alized environments, it is possible to deal with com-

putational resources such as the processor, memory,

and storage as a cache of resources and applications

that might be quickly reallocated to receive new re-

sources when necessary. But virtualization is not a

new concept, in the 60’s it was already applied to

mainframes. Currently, with the processing power ad-

vance of desktop computers, different models of vir-

tual machines have been developed, and its use has

become widespread with excellent results.

Therefore, virtualization allows executing multi-

ple operating systems on the same computer. It is

possible with the use of speciﬁc programs that creates

virtual machines, emulating the physical components

of a host.

The isolation capacity of virtualization is one of

the leading factors to support this work. This isola-

tion is done through a technique called compression

ring (Figure 1. Most of processors have four levels

of priority for code execution, numbered 0-3. Code

running on level 0 can execute any instruction on the

CPU, while at level 3 (the more restricted) some in-

structions cannot be performed. These priority levels

earned the name rings because of the way they were

illustrated in the chip programming 80386 manual. It

also relates to the isolation performance, meaning that

a virtual machine can be isolated from the slice of re-

sources allocated to the host operating system or other

virtual machines hosted on the same physical host.

The virtual memory manager (VMM or virtual

machine manager) run inside the virtual host or non-

system kernel, and when a virtual machine is created,

it moves the virtual kernel to run at level 1 instead

of level 0. The virtual kernel thinks it is operating at

level 0 but is running at level 1, and this allows the

VMM to monitor the execution of the virtual machine

and manages access to memory and peripherals, and

eventually emulate software instructions that can only

be called the real level 0.

Ring 0

Kernel

Ring 1

Drivers

Ring 2

Drivers

Ring 3

Applications

Most

Privileged

Least

Privileged

Least

Privileged

Figure 1: Virtualization Rings.

3 RELATED WORK

Virtualization provides the computing environment

capabilities such as elasticity, resource isolation, bet-

ter use of available resources, security and software

support legacy. Such capabilities attract the interest

of several distributed systems models that seek het-

erogeneity and scalability. According, several studies

using volunteer computing along with virtualization

technology, such as (Ben Belgacem et al., 2012).

CLOSER 2019 - 9th International Conference on Cloud Computing and Services Science

424

Ferreira et al. (Ferreira et al., 2011) show the vir-

tualization technology as a sandbox for security ap-

plications to BOINC. Besides, they created a mid-

dleware called libboincexec, allowing BOINC to run

optimally on several virtual machine monitors. The

authors claim that use BOINC in a virtualized envi-

ronment can provide security against forged answers

arising from grid customers. Based on portability of-

fered at work, Theodoropoulos et al. (Theodoropou-

los et al., 2016) propose to use the same platform on

a cluster, with the objective of improving the perfor-

mance of grid applications. Due to the high scala-

bility of virtualized cluster environments, the results

showed gains of up to 13%. The ﬁrst solution fo-

cuses only on security issues due to the virtual isola-

tion capabilities, but not taking into account energy

efﬁciency or application performance. The second

work was developed for high-performance applica-

tions, which led the focus of the work to improve the

performance of applications. However, the proposal

probably increased energy consumption, since it has

seen the more excellent use of resources and the be-

havior of the applications used in such work.

Cavalcanti et al. (Cavalcanti et al., 2006) use vir-

tual machines to meet the need for security in ﬁle

sharing. For this, the study used Xen to offer the

safety of a safely distributed ﬁle system to the grid

environment. Brasileiro et al. (Brasileiro et al., 2007)

used the previously proposed model to strengthen a

scalable peer-to-peer grid environment called Our-

Grid. Such an environment has become one of the

most widely used distributed grid platforms, includ-

ing serving as a model for well-known grid simula-

tors such as SimGrid (Brennand et al., 2016). Unlike

Ferreira et al. (Ferreira et al., 2011), Cavalcanti et al.

(Cavalcanti et al., 2006) used the resource allocation

capability provided by the virtualization layer to tune

virtual machines in a distributed, heterogeneous envi-

ronment in order to increase the performance of grid

applications. On top of such proposal, Brasileiro et al.

(Brasileiro et al., 2007) proposed Ourgrid, one of the

most used grid environments, which allows the pro-

cessing of a large volume of data in a distributed way,

focusing on the performance of applications.

Cunsolo et al. (Cunsolo et al., 2009) present the

concept of a grid-as-a-service. This new paradigm

is compared with state of the art and discussed as a

viable proposal being implemented. The work ad-

dresses the heterogeneity and independence of cloud

hosts as being an environment that can also be ex-

ploited by the grid paradigm, although it was not de-

signed for this purpose. In such proposal, grid ap-

plications are placed on an already established cloud

environment, and it uses the characteristics of that en-

vironment to achieve performance and energy saving

metrics. In contrast, our proposal has a focus from

conception to development of addressing such met-

rics.

Jonathan et al. (Jonathan et al., 2017) bring this

concept to edge devices. The main idea is to take

the application to a location closer to the consumer,

but reliably and dynamically replicate data to achieve

timeliness for computation and high data availabil-

ity for data storage respectively. Again, the work

presents a scenario that has been previously estab-

lished and performs a grid environment on top of it

with the intent of leveraging all of its infrastructure

capabilities.

Table 1 summarizes the related work. As we can

seen and to the best of our knowledge, no previ-

ous work has used virtualization technology to ﬁt the

slices of idle resources on hosts in the same way we

are (enabling voluntary computing to keep running

even when a machine is in use). Besides, no other

work has attempted to improve the trade-off between

performance of grid applications and energy savings.

4 ENVIRONMENTAL TEST

This section shows the technologies used to this work:

grid software, virtualization platform, and infrastruc-

ture.

4.1 BOINC

The grid software used in this work was the BOINC

(Anderson, 2004). The motivation for creating this

program was the SETI project (Search for Extrater-

restrial Intelligence) abandonment by NASA in the

mid-70, due to its controversial proposals is quite crit-

ical. By 1990, they were taken by an institute called

SETI League, which together with the University

of Berkeley launched the scientiﬁc program called

SERENDIP, which applied the distributed computing

via home computers with the SETI@Home.

In 1992, researchers at UC Berkeley developed the

BOINC software, originally designed to ease connect

the home computers to this program. The success of

this project made it clear that distributed computing

could be utilized for many other scientiﬁc projects,

which demand high processing capabilities, and then,

several new projects were created. The BOINC client

software is highly customizable, and it allows to

choose which projects will help and the number of

resources that will be available. BOINC application

does automatic updates of the applications of projects

and downloads new jobs to be processed. Therefore,

Towards Balancing Energy Savings and Performance for Volunteer Computing through Virtualized Approach

425

Table 1: Related work comparison.

Work

Energy Efﬁcient Performance Aware

Ferreira et al. (Ferreira et al., 2011) - -

Theodoropoulos et al. (Theodoropoulos et al., 2016) - x

Cavalcanti et al. (Cavalcanti et al., 2006) - -

Brasileiro et al. (Brasileiro et al., 2007) - x

Cunsolo et al. (Cunsolo et al., 2009) - -

Jonathan et al. (Jonathan et al., 2017) - -

This proposal x x

volunteer computing uses idle processor cycles, but

when the user is using such resources, these resources

are not used in its entirety. BOINC allows to be con-

ﬁgured for low usage, that is, it can remain active dur-

ing the period of user usage. However, the resource

utilization rate is always ﬁxed (for example, BOINC

will still use 10% of available resources). The tech-

nology that ﬂuctuates in resource utilization rate de-

pending on user usage is called virtualization.

4.2 VirtualBox

VirtualBox (Watson, 2008) is a virtual machine mon-

itor developed by Oracle that aims to create envi-

ronments for installation of different systems. It al-

lows the installation and use of one operating sys-

tem within another, as well as their respective soft-

ware, such as two or more independent computers,

but physically sharing the same hardware.

It was created by the company called Innotek,

which initially offered a proprietary license, but there

was also a version of the product for personal use or

evaluation at no cost. In January 2007, the release of

the VirtualBox OSE (Open Source Edition) with the

GNU General Public License (GPL) - Version 2 was

released. In February 2008, the company Innotek was

acquired by Sun Microsystems. In April 2009, Oracle

purchased Sun Microsystems and all of its products,

including VirtualBox.

VirtualBox has an extremely modular design with

well-deﬁned internal programming interfaces and a

client/server design. It makes it easy to control mul-

tiple interfaces at once. For example, the user can

start a virtual machine on a typical virtual GUI ma-

chine and then control that machine from a command

line, or possibly remotely. A host operating system

maintains the communication between the virtual ma-

chines monitor and hardware.

Such virtual machine monitor also offers a com-

plete software development kit. Although it is open

source, the user does not need to rewrite new access

interfaces. The conﬁguration settings for virtual ma-

chines are stored in XML and are entirely indepen-

dent of local hosts. Therefore, the settings can easily

be transferred to other computers.

Besides, VirtualBox has special software that can

be installed on Windows and Linux virtual machines

to improve performance and make integration much

more seamless. Like many other virtualization solu-

tions, to facilitate the exchange of data between hosts

and guests, VirtualBox allows the declaration of the

directories of speciﬁc hosts as shared folders which

can be accessed from within virtual machines.

The characteristics of modularity and ﬂexibility,

as well as the management of the slices of resources

provided by VirtualBox, boosted its choice for the de-

velopment of this work.

4.3 Testbed

A traditional volunteer computing architecture is pre-

sented in Figure 2. In a few words, there are four

personal computers connected to a server, forming a

computational grid. Furthermore, the Machine 1 is

with a stream running in user space (the ray represents

an execution ﬂow) which means that this machine is

not available at this time for grid applications. Ma-

chines 2, 3, 4 does not support any ﬂow in user space,

allowing the grid to keep streams running on their re-

sources (the execution ﬂow within the BOINC space).

Figure 2: Traditional Grid Environment.

The primary issue discussed in this paper is that

even if the user is using hosts, their resources are be-

ing underutilized because of the massive processing

power of current hosts.

Therefore, it proposes a new environment that can

be seen in Figure 3, which uses virtualization tech-

nology always to keep the grid execution, even if the

user is using the host. Virtualization can adapt to the

share of resources that other applications are not us-

ing, which means that will not overheat the user per-

formance and its use.

Evaluations were performed on a client-server ar-

chitecture when several clients are accessing the grid

CLOSER 2019 - 9th International Conference on Cloud Computing and Services Science

426

Figure 3: Virtualized Grid Environment.

virtualized application in a server host connected to

a Gigabit Ethernet network. The servers used in the

experiments are composed of four processors with 2

Intel Xeon E5520 (16 logical cores in total), 2.27GHz,

16 Gb RAM.

The energy consumption is acquired by using a

multimeter which is connected to the power source

and the machine. This device (EZ-735 digital multi-

meter) has a USB connection that allows external pe-

riodically reading and gives the values of power con-

sumption in watts-per-hour.

The tests were conducted with one of the standard

set of tests by BOINC leading a mean of 24 hours to

perform all the processing in a real environment, in

which the host was running with jobs over a period of

8 hours and idle for 16 hours. The test chosen was the

Collatz Conjecture (also known as 3n+1), a test based

on mathematical Internet connections. The conjec-

ture gives a rule stating that any natural number, as

applied to this theory, in the end, will always be 1.

The method applies to any natural number and tells if

this number is even, divide it by 2 and if it is odd, to

multiply by 3 and add 1. The test used took 40 hours

to be completed.

This scenario is quite realistic, if it is compared

with a production environment where the host is used

during the period of a common laborer, and the ap-

plication of grid uses idle time outside these 8 hours.

With this test scenario using the proposed virtualized

environment, it was possible to develop several exam-

ples with different settings, with the rate limitation of

the virtual machine usage between 10% and 90% (re-

garding about the proposed slices discussed earlier).

Thus, the evaluations are from an environment

with 90% of usage by user and the other 10% utilized

by the virtual environment, and BOINC up to 10% of

usage by user and the additional 90% used by the vir-

tual environment, and BOINC. The virtual machine

monitor used was the VirtualBox (Romero, 2010).

5 EVALUATION AND

DISCUSSION

This section presents the environmental assessments

of proposed metrics relating execution time and

power consumption. The ﬁrst evaluation shows the

overhead of the virtualized environment on the grid

application.

Figure 4 illustrates the difference in the through-

put (the amount of data transferred from one place to

another, or the amount of data processed in a given

time) between the Native Linux environment and the

virtualized environment when we use the BOINC

within virtual machines. As the grid application re-

ceives a bag-of-tasks, processes, and returns the re-

sponse to the server, it is possible to measure the

throughput of both ways when packets are sent, as

when the server receives packets. This difference is

due to the processing network which keeps the virtu-

alization layer. As the hypervisor interprets all com-

munication, there is a delay of up to 25%. This over-

head is not signiﬁcant when compared to the other

beneﬁts that virtualization brings this new approach.

Figure 4: Throughput.

Another essential factor to be evaluated is the ap-

plication’s execution time. Figure 5 shows the results

of the use of the virtual environment that keeps run-

ning BOINC and the execution time of each test case.

In all cases tested there is an improvement in execu-

tion time. The running time in the worst case is quite

close to the base test that took 40 hours, but still bet-

ter results. This execution time has the more signiﬁ-

cant impact, the closer the values on which there is a

higher use of the virtual machine, and consequently,

the higher bandwidth available to the virtual machine.

The ability that makes this possible is the isolation

of the virtual machines, which do not exceed the lim-

its established of use rates, not interfere with the user

applications performance.

This isolation is achieved by a technique called

compression ring. The standard x86 processors have

four levels of priority for code execution, numbered

from 0 to 3. Code running at level 0 can execute any

instruction on the CPU, while at level 3, there are in-

Towards Balancing Energy Savings and Performance for Volunteer Computing through Virtualized Approach

427

Figure 5: Time Execution.

structions that cannot be performed.

The virtual memory manager runs inside the ker-

nel of the host system or non-virtual, and when it cre-

ates a virtual machine, it moves the virtual kernel sys-

tem to run on level 1 instead of level 0. The kernel

of the virtual machine believes it is operating at level

0 but is running at level 1, and this allows the virtual

memory manager to monitor the execution of the vir-

tual machine and manage access to memory and pe-

ripherals, eventually emulate in software instructions

that only can be called the real level 0.

Thus, the isolation between the different levels of

protection, guaranteed by the architecture of the chip,

prevents virtual machine instructions compromise the

host system kernel. As for the power consumption of

this proposal, it is possible to see that there is energy-

saving up to a certain limit. To compare the energy

consumption of this proposal, we used as the base

value, a test with the same load but in a native environ-

ment without virtualization, as can be seen in Table 2.

Table 2: Base value of power consumption on a real envi-

ronment without virtualization. R-V: balancing the use of

resources between real and virtual; ET: application execu-

tion time; PC: power consumption during the test.

R-V ET PC

100-0 40 7040

In the Table 3, the values are related to rates of re-

source use between user applications and the virtual

machine with the grid application, the execution time

in hours for each use threshold, and the average en-

ergy consumption per host in watts.

Table 3: Power consumption on a real-virtual environment.

R-V: balancing the use of resources between real and vir-

tual; ET: application execution time; PC: power consump-

tion during the test.

R-V ET PC

10-90 16 4640

20-80 17 4930

30-70 19 5510

40-60 22 6380

50-50 24 6960

60-40 27 7830

70-30 30 8700

80-20 33 9570

90-10 36 10440

Up to a limit about 50% of use between user appli-

cations and virtualized environment, there are energy-

savings. It is associated with shorter execution time

of the application grid, which although having the

highest power consumption during the execution, the

execution time is reduced as to save energy. Above

this value, although the execution time still worth-

while when compared to the tests in the native en-

vironment, the energy consumption increases signif-

icantly, which not allowed its use when there is con-

cern about energy-saving. Perhaps it is not come to

be a signiﬁcant limitation, given the computers that

are currently available for computational grids, such

as personal computers, the use of resources of most

users usually do not reach 50%.

6 CONCLUSION AND FUTURE

WORK

Virtualization is the technique of running one or more

virtual servers on one physical server. It allows a

higher density of use of resources (hardware, storage,

etc.) while allowing isolation and security are main-

tained. Based on these features, virtualization has be-

come the basis for the use of other technologies that

wish to use their abilities. One of the technologies

that have turned their attention to this new infrastruc-

ture is the volunteer computing.

In this sense, virtualized computing enables a

highly heterogeneous environment to present homo-

geneity to the top software layers, making grid clients

less coupled and more dynamic. Focusing primarily

on the virtualization characteristic regarding resource

isolation, this paper proposes the use of virtualization

technology along the volunteer computing, allowing

this type of distributed processing executes concomi-

tantly with other applications in the same environ-

ment.

The results showed that there are advantages of

this approach, in which no inﬂuence on user applica-

tions, while there is an improvement of the grid ap-

plications performance, considering the best use of

available resources, managed by virtualization. Fur-

thermore, the results show that there is energy-saving

up to 50% use of the virtualized resources, which val-

idates this proposal, taking into account the use of

resources by ordinary users do not exceed this limit.

There are virtual machines with more performance,

but with less isolation. Just as there are virtual ma-

chines with less performance, but with more isolation.

Based on discussed above, the following ﬁndings

can be cited:

CLOSER 2019 - 9th International Conference on Cloud Computing and Services Science

428

• Grid execution is kept even if the user using the

host.

• Better utilization of the hosts.

• Upper limiting the proportion of resources used

by grid execution ensuring the avoidance of over-

heating.

• Due to virtualization the volunteer client app

(BOINC) is isolated from the user app.

On the results presented in this paper, several fu-

ture works can be addressed. The elasticity provided

by virtualization has enhanced cloud environments,

so grid applications with the intent to better utilize

available resources can take advantage of this new

paradigm. Besides, the service-oriented architecture

may be usable as it provides integration and interop-

erability to client applications.

REFERENCES

Anderson, D. P. (2004). Boinc: A system for public-

resource computing and storage. In Proceedings of

the 5th IEEE/ACM International Workshop on Grid

Computing, GRID ’04, pages 4–10, Washington, DC,

USA. IEEE Computer Society.

Ben Belgacem, M., Abdennadher, N., and Niinimaki, M.

(2012). Virtual ez grid: A volunteer computing in-

frastructure for scientiﬁc medical applications. Int. J.

Handheld Comput. Res., 3(1):74–85.

Bohr, M. (1998). Silicon trends and limits for advanced

microprocessors. Commun. ACM, 41(3):80–87.

Brasileiro, F., Araujo, E., Voorsluys, W., Oliveira, M., and

Figueiredo, F. (2007). Bridging the high performance

computing gap: the ourgrid experience. In Seventh

IEEE International Symposium on Cluster Computing

and the Grid (CCGrid ’07), pages 817–822.

Brennand, C. A. R. L., Duarte, J. M., and Silva, A. P. (2016).

Simgrid: A simulator of network monitoring topolo-

gies for peer-to-peer based computational grids. In

2016 8th IEEE Latin-American Conference on Com-

munications (LATINCOM), pages 1–6.

Cavalcanti, E., Assis, L., Gaudencio, M., Cirne, W., and

Brasileiro, F. (2006). Sandboxing for a free-to-join

grid with support for secure site-wide storage area.

In Proceedings of the 2nd International Workshop on

Virtualization Technology in Distributed Computing,

VTDC ’06, pages 11–, Washington, DC, USA. IEEE

Computer Society.

Cunsolo, V. D., Distefano, S., Puliaﬁto, A., and Scarpa, M.

(2009). Cloud@home: bridging the gap between vol-

unteer and cloud computing. In Proceedings of the 5th

international conference on Emerging intelligent com-

puting technology and applications, ICIC’09, pages

423–432, Berlin, Heidelberg. Springer-Verlag.

Ferreira, D., Araujo, F., and Domingues, P. (2011). lib-

boincexec: A generic virtualization approach for the

boinc middleware. In Proceedings of the 2011 IEEE

International Symposium on Parallel and Distributed

Processing Workshops and PhD Forum, IPDPSW

’11, pages 1903–1908, Washington, DC, USA. IEEE

Computer Society.

Hwang, S., Jeong, K., Im, E., Woo, C., Hahn, K.-S., Kim,

M., and Lee, S. (2003). An analysis of idle cpu cy-

cles at university computer labs. In Proceedings of

the 2003 international conference on Computational

science and its applications: PartI, ICCSA’03, pages

733–741, Berlin, Heidelberg. Springer-Verlag.

Jonathan, A., Uluyol, M., Chandra, A., and Weissman, J.

(2017). Ensuring reliability in geo-distributed edge

cloud. In 2017 Resilience Week (RWS), pages 127–

132.

Nov, O., Anderson, D., and Arazy, O. (2010). Volunteer

computing: a model of the factors determining con-

tribution to community-based scientiﬁc research. In

Proceedings of the 19th international conference on

World wide web, WWW ’10, pages 741–750, New

York, NY, USA. ACM.

Romero, A. V. (2010). VirtualBox 3.1: Beginner’s Guide.

Packt Publishing.

Siam, M. Z., Krunz, M., Cui, S., and Muqattash, A. (2010).

Energy-efﬁcient protocols for wireless networks with

adaptive mimo capabilities. Wirel. Netw., 16(1):199–

212.

Theodoropoulos, D., Chrysos, G., Koidis, I., Charitopou-

los, G., Pissadakis, E., Varikos, A., Pnevmatikatos,

D., Smaragdos, G., Strydis, C., and Zervos, N. (2016).

mcluster: A software framework for portable device-

based volunteer computing. In 2016 16th IEEE/ACM

International Symposium on Cluster, Cloud and Grid

Computing (CCGrid), pages 336–341.

Uhlig, R., Neiger, G., Rodgers, D., Santoni, A. L., Martins,

F. C. M., Anderson, A. V., Bennett, S. M., Kagi, A.,

Leung, F. H., and Smith, L. (2005). Intel virtualization

technology. Computer, 38(5):48–56.

Watson, J. (2008). Virtualbox: Bits and bytes masquerading

as machines. Linux J., 2008(166).

Towards Balancing Energy Savings and Performance for Volunteer Computing through Virtualized Approach

429