=Paper=
{{Paper
|id=Vol-1580/38
|storemode=property
|title=Optimization Energy Consumption in Mobile Cloud Computing by Using an Elastic Framework
|pdfUrl=https://ceur-ws.org/Vol-1580/id38.pdf
|volume=Vol-1580
|authors=Yassine Elmahoti,Noura Aknin
|dblpUrl=https://dblp.org/rec/conf/bdca/ElmahotiA15
}}
==Optimization Energy Consumption in Mobile Cloud Computing by Using an Elastic Framework==
https://ceur-ws.org/Vol-1580/id38.pdf
Proceedings of the International Conference on Big Data, Cloud and Applications
Tetuan, Morocco, May 25 - 26, 2015
Optimization energy consumption in mobile cloud
computing by using an elastic framework
Yassine ELMAHOTI Noura Aknin
Information Technology and Modeling Systems Research
Information Technology and Modeling Systems Research
Unit, LIROSA Laboratory
Unit, LIROSA Laboratory
Faculty of Science, Abdelmalek Essaadi University
Faculty of Science, Abdelmalek Essaadi University
Tetuan, Morocco
Tetuan, Morocco
aknin@ieee.org
yassinemahoti@yahoo.fr
Abstract— The mobile cloud computing (MCC) has become constant mobility of mobile which contribute significantly to a
more and more present in our life and that is due to the wide quickly landfill of batteries
availability of mobile devices in the world market (smartphones,
tablets, etc.), however unlike the cloud computing has proven a This paper propose a an implementation of a framework for
big success and performance in communication technologies, the dividing a customer request between a number of devices (best
mobile devices are not able to a fully benefit from this elected), this electing is based on the following criteria:(
development due to their resource’s limitations such as the CPU, distance to the mobile concerned, the load of the battery, the
Ram, Battery life, etc.
utilization of CPU, RAM, storage, power signal), the obtained
Today, due to heavy applications that are hosted in the cloud, results showed that the installation of this framework
the mobile devices consumes more and more energy, and it contributes greatly to save energy to consumption.
contributes greatly to the battery discharge in a very short
The paper is organized as follows: Section (1) gives an
delays.
overview to the mobile cloud computing, Section (2) describes
To resolve this problem, we propose in this paper an the framework architecture, its algorithms and its results.
implementation of an elastic framework for splitting a customer
request between the others devices (best elected devices) for II. MOBILE CLOUD COMPUTING
minimizing the processing time, the results have shown that the Facebook, Whatsapp, YouTube, are the most application used
implementation of this framework contributes significantly to a by million people in the word, for example Facebook users
considerable minimization of the request processing time and have arrived at 1,393 milliard connected in January 2015.
therefore minimizing the energy consumed.
This explosion in number of users is surely due to the large
Keywords— mobile cloud computing, energy, framework, distribution of mobile devices in the word, a study conducted
agent by Gartner (June 2014) showed that the number of Worldwide
Device is reach 256 Million Units as shows the table I:
I. INTRODUCTION
TABLE I. WORD DEVICE UNIT
The use of mobile devices has seen a great explosion of the last
Worldwide Device Shipments by
ten years and have become an indispensable way in our daily (Segment Thousands of Units) 2013 2014 2015
life for checking information in the internet; a user can now Traditional PCs (Desk-Based and
view his inbox anywhere and anytime while before it needs to Notebook) 296,131 276,221 261,657
be sitting at his desk for getting this information.
Ultra mobiles, Premium 21,517 32,251 55,032
The mobile cloud computing has proven a big success by Tablets 206,807 256,308 320,964
permitting a huge facility and flexibility to give an information Mobile Phones 1,806,964 1,862,766 1,946,456
faster and a powerful additional resources for devices that can
run a very heavy applications (CPU, RAM, storage as a
service).
Despite that, the mobile cloud computing (MCC) knows some
problems and especially the high energy consumption problem
and this is due to several factors: complexity of application, the
strength of the wireless signal, distance to the base station, the
51
heavy processing (image processing, software localization,
Worldwide Device Shipments by Segment etc.), it permit also a remote storage resources and virtual
2 000
Traditonal PCs networks to connect to remote application.
1 800
(Desk-Based and
1 600 Notebook) PaaS (platform as a service): it is another model for the
1 400 delivery of cloud computing services, allowing application
Ultramobiles,
1 200 developers to prepare libraries and prerequisites needed to
Premium
program, test their applications in secured and reliable
1 000
environment.
800 Tablets
600 SaaS (software as a service): the user does not need to install
400 any tool, the software and data are stored in cloud providers,
200
Mobile Phones and the user through a web browser can connect to the service
to do the desired operations.
0
2013 2014 2015 The connection of users to the cloud computing service is
provided by wireless networks (3G, 4G, WiFi) as shown in the
figure below, and this type of connection sometimes generates
Fig. 1: Worldwide device units a fast discharge battery because of the weakness the signal, far
distance between the base station and the mobile receiver,
The Fig. 1 shows this recent years an important number of
users have emerged from the traditional computer to mobile
devices because their performance become more efficient and
sufficient for doing their majority tasks in internet, for example
the SAMSUNG galaxy S5 have 2gb in memory, 2,5 GHz quad
core of CPU and it capable to execute a several process in the
same time.
This significant emerging from the fixed equipment to the
mobile is due to the possibility of mobile equipment has
exceeded the old and simplest communication tools (call /
message) and can now guaranteed other interesting things
service browsing in internet, install applications, share data,
etc.
The cloud computing recently appeared in the IT word allows
users to execute applications or store data without having the
necessary resources in their terminals, the entire treatment of
task is guaranteed by the cloud computing servers which are
robust and powerful. Fig. 3: mobile cloud computing architecture
The multi task operation ensured by smartphones was The remarkable development of cloud computing in recent
introduce the cloud computing in the mobile environment, a years, attracting more and more interest from various internet
simple user with his terminal can benefit from a several IT users and IT looking to enjoy the best services and applications
services as a model as shows the figure below: available online through the web. This is a new business model
that cloud computing promises to ICT. Indeed, the model
promises a change in the mode of investment and operation of
IT resources.
III. PROPOSED WORK
To resolve to the energy consumption problem for the mobile
devices, we propose an implementation of a framework which
is composed by several subsystems interconnected between
them for a smart processing of the customer queries. This
framework permits a splitting request of the cloud client
between the idlest mobiles available in the network for
Fig. 2: Cloud computing models minimizing the processing time and therefore saving the
energy consumption for executing a concerned process.
- IaaS(infrastructure as service): it is the lower level of cloud
computing services, it permit to the client to benefit of
hardware resources like as a remote RAM and CPU to execute
52
a) Description
This framework is as an intermediate software layer between and came back the result to the customer in a very short time
the mobile terminal and cloud providers: it receives the than the normal case by splitting the request by many others
request from the client, communicates with the cloud servers mobiles devices . This framework is composed by
Fig. 4 : The Elastic framework
- Mobile agent: is an agent installed in the mobile for receive c) Algorithm
and send information to the other component of this
framework. The processing time of request client depends to the transfer
data flow from the cloud providers to the terminal mobile and
- Job stat agent: It returns for the Job splitter agent the stat of its resources CPU, RAM, so we have:
the request processing and inform it when the operation if fails
for choosing another device. proc _ time computation _ time transfer _ time (1)
The main elements of this framework: With proc _ time is the processing time for executing a
- Localizer agent: localize the mobile devices which are near tasks, computation _ time is the total time required for
to the client concerned. compute the tasks by the mobile, transfer _ time is the total
- Resource monitor agent: It returns the resources allocation of time for the transfers the data from the providers cloud to the
mobiles devices that have be mentioned by the localizer agent mobile.
(CPU, RAM, buttery life, storage, signal power).
Data
- Job splitter agent: it splits the client request between the best computation _ time (2)
elected mobile (less overloaded) returned by the resource CPU
monitor and store in his cache memory the state of the process With CPU is the processor of the mobile
executed in the mobiles devices.
Data
transfer _ time (3)
Bandwidth1
53
With bandwidth is data flow from the cloud providers to the calculating the power usage of the mobile devices, we
mobiles of between the mobiles theme self. installed a software called Joulemeter a tool developed by
Microsoft researchers and we found the following result as
From (2) & (3) we have show the table I and the figure:
Data Data
proc _ time (4)
CPU Bandwidth1
Power Usage by number
1 1 25
proc _ time Data( ) (5) of devices
CPU Bandwidth1 20
When we introduce the elastic framework the task client is
15 1
divided by the best elected others mobiles.
2
1 1 1 10 3
proc _ time Data ( n )
1
CPU
n Bandwidth1 4
5
n i 1 (6)
Data 0
Power Usage (Watt)
n.Bandwidth 2
Where Bandwidth1is the bandwidth between the BTS and
Fig. 6: Power usage by number of device
the mobile, Bandwidth2 between the mobile elected and
the mobile concerned and n is the number of selected devices
by the framework. To ensure that the energy consumed by a mobile for
d) Result processing the request’s customer in the case without using the
framework exceed the energy summation by all selected
To experiment our algorithm proposed in this paper, we set up devices by the framework we calculated the energy by the
a cloud platform composed of 3 servers and 10 mobiles following formula:
devices with each one have 1.6 Ghz in its CPU and we found
this following this results: n
Energy proc _ time * power _ usagei
i 1
3000
Processing time by number of In case of introducing the elastic framework the power usage
2500 devices come the sum of power of the different devices elected:
n
power _ usage power _ usagei
2000 1
1500 2 i 1
3 Where n is the number of selected devices by the framework
1000
4
So the energy consumption becomes:
500
n
0 Energy proc _ time * power _ usagei
Processing time (s) i 1
Fig. 5: The processing time
We have the following result :
As we can see, when we introduce the elastic framework, the
processing time decreases in a very meaningful way and this is
because the response of the request client from the cloud is 80000
divided by the selected mobile those respond to the following
60000 1
criteria (CPU utilization is under 50% RAM usage is under
50%, the distance between this mobile and the client 40000 2
concerned is closer than the client with the BTS, the signal
3
strength and the rate of the battery that exceeds 50%). 20000
4
As shown the figure 6, this framework not only decrease the
0
processing time, but also decreases the power usage and this is Energy cosumption(Joule)
due to the CPU came more and more offload and therefore the
mobiles use a little power for processing the task. For
54
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