International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 6- Feb 2014
Mobile Computation Dynamic Offloading using
Cloud
Pooja V. Bhokare1 Chitra J. Patil2
Assistant Professor, Computer Engineering Department
SSBT’s college of engineering and technology, Bambhori, post box no.94, Jalgaon-425001, India
Abstract— The mobile cloud computing integrates the cloud
computing into the mobile environment and overcomes obstacles
related to the performance (e.g., battery life, storage, and
bandwidth), environment (e.g., heterogeneity, scalability, and
availability), and security (e.g., reliability and privacy).The
Mobile devices are becoming global for online web services and
applications. Many services available for the mobile devices,
but the mobile devices have some limitation in the sense of
computation low memory, limited battery. First, we present
several challenges for the design of Mobile Cloud computing
service. Second, a concept model has been proposed to
analyze related research work. Third, we survey recent mobile
cloud computing architecture, application & offloading, and
generalize services for mobile.
Keywords— Offloading, Mobile computation, Cloud computing,
Intensive applications.
I. INTRODUCTION
Today, mobile devices, such as smart phones,
tablets, PDAs, and iPods, have become
indispensable in our daily lives. With their
growing popularity, users have come to rely
more and more on them as their primary
computation and communication devices, and are
even beginning to expect functionality and
performance similar to that from traditional
computing devices such as desktops and
workstations. Mobile devices (e.g., Smartphone,
tablet pcs, etc) are increasingly becoming an
essential part of human life as the most effective
and convenient communication tools not bounded
by time and place. Mobile users accumulate rich
experience of various services from mobile
applications (e.g., iPhone apps, Google apps, etc),
which run on the devices and/or on remote servers
via wireless networks. The rapid progress of mobile
computing becomes a powerful trend in the
development of IT technology as well as commerce
and industry fields. However, the mobile devices
are facing many challenges in their resources (e.g.,
battery life, storage, and bandwidth) and
communications (e.g., mobility and security). The
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limited resources significantly impede the
improvement of service qualities. However,
meeting such expectations on these devices is
challenging due to several reasons. First, the
mobility of these devices implies that they are
battery- powered, limiting their power capabilities.
As the devices become more popular and their
use becomes more frequent, the increasing
energy consumption becomes a key bottleneck in
their computational
capability.
Second the
computational power, in terms of processing
and memory, is severely limited compared to
traditional computers. Thus, these devices are
limited in their ability to execute rich user
applications involving extensive use of computer
vision and graphics, speech recognition, or
machine learning, which can be resource intensive.
Mobile Cloud Computing at its simplest refers to an
infrastructure where both the data storage and the
data processing happen outside of the mobile device.
Mobile cloud applications move the computing
power and data storage away from mobile phones
and into the cloud, bringing applications and mobile
computing to not just Smartphone users but a much
broader range of mobile subscribers. The concept
of Mobile Cloud Computing (MCC) intends to
make the advantages of Cloud Computing available
for mobile users but will provide additional
functionality to the cloud as well. Mobile
Cloud Computing (MCC) will help to overcome
limitations of mobile devices in particular of the
processing power and data storage. It might also
help to extend the battery life by moving the
execution of commutation-intensive application to
the cloud. However, a significant gain in battery
stand-by Time will require that the wireless
connectivity for the MCC operation is at least as
energy-efficient as the state of the art.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 6- Feb 2014
II.CHALLENGES OF MOBILE CLOUD COMPUTING
Mobile Cloud Computing services are implemented
in mobile wireless environment, incorporating
several challenges such as the dependency on
continuous network connections. Also Mobile
Cloud Computing concepts rely on an always on
Connectivity and will need to provide a scalable
and high quality mobile access.
A. Network latency and limited bandwidth in the mobile
network: First, Mobile Cloud Computing may face
the challenge from the transmission channel due
to the intrinsic nature and constraints of wireless
networks and devices. This is especially true
when it comes to rich- internet and immersive
mobile applications, e.g. On line gaming and
augmented reality that require high- processing
capacity and minimum network latency. These will
most probably continue to be processed locally
on powerful smart phones and mobile tablets.
Mobile broadband networks generally require
longer execution times for a given application to
run in the cloud and network latency issues may
deem certain applications and
Services unfit for the mobile cloud.
B. Various access scheme in mobile environments: Mobile
Cloud Computing would be deployed in a
heterogeneous access scenario with different
radio access technologies such as GPRS, 3G,
WLAN, WiMax. Mobile Cloud Computing requires
wireless connectivity with the following features:
• Mobile Cloud Computing requires an always-on
connectivity for a low data rate cloud control
signalling channel.
• Mobile Cloud Computing requires an on-demand
available wireless connectivity with a scalable link
bandwidth.
• Mobile Cloud Computing requires a network
selection and use that takes energy-efficiency and
costs into account. The most critical challenge of
Mobile Cloud Computing is probably to guarantee
a wireless connectivity that meets the requirements
of Mobile Cloud Computing with respect to
scalability, availability, energy- and costefficiency.
C. Elastic application models: Cloud Computing
services are scalable, via dynamic provisioning
of resources on a Fine-grained self-service basis
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near real-time, without users’ consideration for
peal
loads. This requirement is particularly
important towards mobile cloud computing scenario.
Mobile applications can be launched on the device
or cloud, and can be migrated between them
according to dynamic changes of the computing
context or user preferences. Also, limited resource
of mobile device will restrict application processing.
Thus, elastic application model should be proposed
to solve fundamental processing problem.
D. Security and Privacy: Cloud computing users prove
their identities with digital credentials, typically
passwords and digital certificates. If an attacker
could fake or steal these credentials, the cloud
computing system will suffer from spoofing attacks.
In mobile cloud computing, the problem is even
severe because mobile devices often lack of
computing power to execute sophisticated security
algorithms. Moreover, it is difficult to enforce a
standardized credential protection mechanism due
to the variety of mobile devices. There are four
basic approaches to saving energy and extending
battery lifetime in mobile devices:
1) Adopt a new generation of semiconductor technology. As
transistors become smaller, each transistor
consumes less power. Unfortunately, as transistors
become smaller, more transistors are needed to
pro-vide
more functionalities
and
better
performance; as a result, power consumption
actually increases.
2) Avoid wasting energy. Whole systems or individual
components may enter standby or sleep modes to
save power.
3) Execute programs slowly. When a processor’s clock
speed doubles, the power consumption nearly
couples. If the clock speed is reduced by half, the
execution time doubles, but only one quarter of the
energy is consumed.
4) Eliminate computation all together. The mobile system
does not perform the computation; instead,
computation is performed somewhere else, thereby
extending the mobile system’s battery lifetime. We
focus on the last approach for energy management.
Transfer computation to another machine is not a
new idea.
Thus, cloud computing can save energy for mobile
users through computation off-load. Virtualization,
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International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 6- Feb 2014
a fundamental feature in cloud computing, lets
applications from different customers run on
different virtual machines thereby providing
separation and protection. In this paper we also
tray to solve the problem of the proxy time
consuming we will try to implement a web logic
that will run on cloud in this way we can eliminate
this approach to solve the timing and the response
time problems.
Mesh also allows sharing files and folders across
multiple devices simultaneously. Although the
cloud computing architecture can be divided into
four layers, it does not mean that the top layer must
be built on the layer directly below it. For example,
the SaaS application can be deployed directly on
IaaS, instead of PaaS.
IV.FRAMEWORK AND METHODOLOGY
III.CLOUD SERVICES
Generally, a cloud computing is a large-scale
distributed network system implemented based on a
number of servers in data centres. The cloud
services are generally classified based on a layer
concept. In the upper layers of this paradigm,
Infrastructure as a Service (IaaS), Platform as a
Service (PaaS), and Software as a Service (SaaS)
are stacked. Data centres layer: This layer provides
the hardware facility and infrastructure for clouds.
In data centre layer, a number of servers are linked
with high-speed networks to provide services for
customers. Typically, data centres are built in less
populated places, with high power supply stability
and a low risk of disaster.
1) Infrastructure as a Service (IaaS): IaaS is built on top
of the data centre layer. IaaS enables the provision
of storage, hardware, servers and networking
components. The client typically pays on a per-use
basis. Thus, clients can save cost as the payment is
only based on how much resource they really use.
Infrastructure can be expanded or shrunk
dynamically as needed. The examples of IaaS are
Amazon EC2 (Elastic Cloud Computing) and S3
(Simple Storage Service).
2) Platform as a Service (PaaS): PaaS offers an
advanced integrated environment for building,
testing Accepted in Wireless Communications and
Mobile Computing – Wiley and deploying custom
applications. The examples of PaaS are Google App
Engine, Microsoft Azure, and Amazon Map
Reduce/Simple Storage Service.
3) Software as a Service (SaaS): SaaS supports a
software distribution with specific requirements. In
this layer, the users can access an application and
information remotely via the Internet and pay only
for that they use. Salesforce is one of the pioneers
in providing this service model. Microsoft’s Live
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The mobile computation framework will provide
the global accessing for any web enabled mobile
platform and can be deployed easily on any
backend platform. There are two components at
the cloud end: the web agent and server. The web
logic provides a gateway sandwiched between the
mobile device and the cloud backend. In this case
the web logic will perform the duty of proxy
and stabiles the connection and respond for the
services of the mobile agent. Mobile devices are
communicating with the web logic using mobile
application, and get connection to the cloud. So
the direct communication wills stabiles between
the mobile devices and cloud for the fast data
access. The code is previously running in the
cloud, the code is available in the storage area but
not deploy, or the code is available only on the
mobile and should be uploaded by the mobile
device. So the web logic is not on the data
communication path, which makes the web logic
not a bottleneck.
V.PROFILING METHODOLOGY
The choice to contract out will be dependent
both on the application characteristics (e.g., its
computational requirements) and the current
environment (e.g., the amount of remaining
battery, network connectivity, etc.). Thus, in sort
to make the right outsourcing choice, the
application’s feat and resource habit needs to be
profiled under dissimilar environments. In our
current implementation, five parameters are
monitored and analysed in different execution
configurations, i.e., input data size, CPU usage,
memory usage, execution time and power
consumption. Three execution modes
are
considered: i.e., Local (executed on the mobile),
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International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 6- Feb 2014
Remote- WIFI (outsource to the remote server
through WIFI),and Remote-3G (Outsource to the
remote server through Sprint 3G network). The
system file ’/proc/stat’ and the Debug class can
provide CPU and memory procedure for each
process in the web enabled system.
If the
execution mode is Local, the total execution time is
recorded. The result of the open source framework
for mobile, it will increase the speed of the
mobile computation that will help the mobile
users to use mobile devices longer. It is a new
concept for the mobile user to save the battery
power and the speed up the computation power.
VI.STRATEGY
Our results highlight three different application
patterns that impact the outsourcing strategy.
Simple
Blur
and Simple
Sharpen
are
computation intensive, which gains significant
speedup and energy saving through computation
outsourcing. Tile Filter and Gray Filer are
computation non intensive, which gains no
performance or
energy improvement.
Face
Recognition
demonstrates
a case
where
offloading benefits depends on the execution
environment (input data size and network
connectivity). Thus, the decision to outsource
depends heavily on the application characteristics
and the execution environment. The offloading
decision can be made based on the user’s
preference, i.e., low response time, long battery
life, or both. A group of pre-defined preferencepolicies can be stored at the mobile device which
can be selected by the user. Preference-policies
include the desired operating range threshold for
each metric (CPU usage, response time, power
consumption, etc.)
Generated based on the
profiling data. For instance, if the users prefer to
run more applications, then applications should
offload as many operations as possible to the
Cloud. If the user prefers long battery life or
low response time, the mobile can make decision
based on the preference-policy and the available
resources. After the operation is offloaded to the
remote server, the outsourcing controller will
monitor the execution and adjust the offloading
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accordingly. We are building such a system. In
some cases, outsourcing may be necessary simply
to make an application execution feasible. For
instance, the Android system has an upper limit
of heap memory for each application, e.g., 24M
for the Nexus One, so that it cannot execute
applications consuming memory over this limit. In
our experiments, an image over 300K cannot be
processed by Simple Blur locally. Thus, offloading
such memory-intensive applications to the Cloud
can extend the computability of mobile devices.
Besides the performance and energy benefits to
the application being offloaded, outsourcing can
also benefit other applications running on the
mobile. Thus, it can share the same image
manipulation server as other users.
VII.ADVANTAGES OF MOBILE CLOUD COMPUTING
A. Extending battery lifetime: Battery is one of the main
concerns for mobile devices. Several solutions have
been proposed to enhance the CPU performance,
and to manage the disk and screen in an intelligent
manner, to reduce power consumption.
B. Improving data storage capacity and processing power:
Storage capacity is also a constraint for mobile
devices. MCC is developed to enable mobile users
to store/access the large data on the cloud through
wireless networks.
C. Improving reliability: Storing data or running
applications on clouds is an effective way to
improve the reliability since the data and
application are stored and backed up on a number
of computers. This reduces the chance of data and
application lost on the mobile devices
D. Dynamic provisioning: Dynamic on-demand
provisioning of resources on a fine-grained, selfservice basis is a flexible way for service providers
and mobile users to run their applications without
advanced reservation of resources.
E. Scalability: The deployment of mobile applications
can be performed and scaled to meet the
unpredictable user demands due to flexible resource
provisioning. Service providers can easily add and
expand an application and service without or with
little constraint on the resource usage.
F. Multi-tenancy: Service providers (e.g., network
operator and data centre owner) can share the
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International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 6- Feb 2014
resources and costs to support a variety of
applications and large number of users.
G. Ease of Integration: Multiple services from different
service providers can be integrated easily through
the cloud and the Internet to meet the users’
demands.
VIII.APPLICATIONS OF MOBILE CLOUD COMPUTING
I. Mobile Commerce: Mobile commerce (m-commerce)
cloud very interactive and easy to use the services
of the WWW.
We are unaware of any other live experimental
analysis to date. The results show that offloading
compute intensive applications to the cloud can
greatly reduce the response time and battery
consumption. With WIFI network, it can achieve
more than 28_ speedup and 9.5_ power efficiency.
The system can make decisions dynamically and
deploy the services. The user can define their own
preferences as well, e.g., save power, improve
performance or both. Since the open source
framework is much more computationally
powerful, the mobile client and the cloud server
can work on different data sets for the fidelity
concern. For instance, the image processing
application can perform on a coarse grain scale
while the cloud server can work on a finer grain
scale. Thus when the user needs quick, less accurate
results, then the user can execute it locally on the
mobile. In this way, we have discussed about
mobile offloading using cloud.
is a business model for commerce using mobile
devices. The m-commerce applications generally
fulfil some tasks that require mobility (e.g., mobile
transactions and payments, mobile messaging, and
mobile ticketing).
II. Mobile Learning: Mobile learning (m-learning) is
designed based on electronic learning (e-learning)
and mobility. However, traditional m-learning
applications have limitations in terms of high cost
of devices and network, low network transmission
rate, and limited educational resources.
III. Mobile Healthcare: The purpose of applying MCC
in medical applications is to minimize the
ACKNOWLEDGMENT
limitations of traditional medical treatment (e.g.,
small physical storage, security and privacy, and Our thanks to the experts who have contributed
medical errors.
towards development of the paper.
IV. Mobile Gaming: Mobile game (m-game) is a
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IX.CONCLUSION
As we seen, Mobile cloud computing is one of
mobile technology trends in the future since it
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and cloud computing, thereby providing optimal
services for mobile user. In this paper, we have
shown the mobile Computation using cloud, we
come with the outcome that we can implement
the new offloading concept in the generalized
form that would help the user to use mobile
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