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
Energy-efficient, Grid Computing, Performance-aware, Virtualization, Volunteer Computing.
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.
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
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
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
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
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
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-
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.
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
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
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
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
Ring 1
Ring 2
Ring 3
Figure 1: Virtualization Rings.
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
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-
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
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.
This section shows the technologies used to this work:
grid software, virtualization platform, and infrastruc-
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
Table 1: Related work comparison.
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
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).
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
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
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.
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.
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%.
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-
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
Grid execution is kept even if the user using the
Better utilization of the hosts.
Upper limiting the proportion of resources used
by grid execution ensuring the avoidance of over-
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.
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