Adaptive Computation Offloading in Mobile Cloud Computing
Vibha Tripathi
Keywords: Mobile Cloud Computing, Computation Offloading, Data as a Service (DaaS), Platform as a Service (PaaS),
Software as a Service (SaaS), Infrastructure as a Service (IaaS), Machine Learning, Artificial Intelligence,
Augmented Reality, Internet of Things (IoT), Nash Equilibrium.
Abstract: Mobile Computing has been in use for a while now. A mobile device is a concise tool with limited
computational resources like battery, CPU and memory. Although these resources suffice the immediate
traditional needs of its user, as the mobile devices are fast turning into personal computing devices, with the
rapid development in Cloud-Based technologies like Machine Learning in the Cloud, Data as a Service,
Software as a Service, and so on there is an emergent need to implement iteratively more effective ways to
offload mobile computation to the Cloud in an on-demand, adaptable and opportunistic way. The major
issue in implementing this requirement lies in the very fact that mobile devices are location and context
sensitive, limited in battery capacity and need to be constantly reconnecting with their provider’s Base
Transceivers while still providing efficient response time to its user. In this paper, we survey this issue and a
few proposed solutions in this area and in the end; propose a model for adaptive computation offloading.
Mobile devices have been around for some time
now. Mobile Computing has come of age. Cloud
Computing is rapidly growing in its own space. For
Mobile devices to be able to leverage on Cloud
Computing resources available today, there are some
constraints to consider. We cannot simply merge the
two worlds of Mobile and Cloud Computing due to
the difference in nature of a handheld or wearable
device versus a desktop machine virtually always
connected on the same Local Area Network (LAN).
Mobile devices often need to connect and
reconnect to their provider’s transceivers owing to
possible location changes.
The functional collaboration required between a
Cloud and a Mobile Network, to make a mobile
user’s Quality of Experience (QoE) seamless is
Computation offloading involves methods and
means to decide the optimal nature and amount of
computation to be delegated from a Mobile device to
the Cloud on an on-demand basis and on-the-go.
In this paper, we study the nature of some of the
complexities in Computation Offloading, proposed
solutions and their limitations and finally propose a
possible adaptive computation offloading model for
Mobile Cloud Computing.
Cloud Computing offers ‘as a Service’ frameworks
that are used in collaborative computations. For
Mobile Computing, the service components that are
of more significance can be shown as in Fig. 1.
Figure 1: Cloud Service Layers.
The three layers are Software as a Service (SaaS)
e.g. email service, data mining and machine learning
applications, Platform as a Service (PaaS) e.g.
Tripathi, V.
Adaptive Computation Offloading in Mobile Cloud Computing.
DOI: 10.5220/0006348505520557
In Proceedings of the 7th International Conference on Cloud Computing and Services Science (CLOSER 2017), pages 524-529
ISBN: 978-989-758-243-1
Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
operating systems, and Infrastructure as a Service
(IaaS) e.g. storage and virtual machines.
At the core of Cloud Computing is the concept of
Hardware Virtualization – many virtual servers
participate independently with their own operating
environment but with the same physical layer.
Mobile Networks are laid out architecturally to
provide an illusion of pervasive uninterrupted
network connection to the end user while making
location and reconnections appear seamless most of
the times. The architecture is described in the
Section 2.1.
2.1 Mobile Cloud Computing and
Offloading Computations
Figure 2: The figure shows how handheld devices switch
connections to their base transceivers based on their
location in a cell.
Computation and Data Intensive Apps: Some mobile
applications may need to perform computation and
data-intensive tasks like voice and speech analysis,
facial expression analysis (Google Cloud Vision
API), Augmented Reality etc.
Cloud based mobile apps can allow the end users to
leverage the vast resources of the Cloud and run in a
virtual environment, by offloading the data-intensive
computation components to the Cloud and provide a
better response time from the Cloud as opposed to
local device-based computations when
communication is light (Jesus Zambrano, 2015).
Mobile Battery Life: With the rapid increase in the
multimedia content and computation intensive
games being made available for mobile system, one
of the primary constraints while considering
computation offloading is the mobile battery life.
Battery technology has until now been unable to
catch up with the fast growing battery consumption
on mobile devices.
(Karthik Kumar et al, 2012) propose a formula to
dynamically decide on computation offloading of
certain energy-intensive computations with a view to
benefit from offloading as opposed to local mobile
based computation which can drain out the entire
Along with the computational components, however,
we also need to consider the location and context
awareness of these mobile devices.
Location and Context Awareness of Mobile Devices:
Mobile devices have sensors that can collect
information about the location of the device, some
context metrics about the users (e.g. Fitbit) for
example to recommend services, customize answers
to user queries, advertise local businesses, provide
individualized search results etc. (Gabriel Orsini et
al, 2015) discuss the requirements, design and a pre-
phasing or partitioning for computational offloading
to arrive at an optimal context aware solution.
A typical Cloud-enabled Mobile device would have
the below minimum requirements:
An uninterrupted connection to a Cloud
service provider
An efficient computation offloading model
that takes context awareness as well as
battery usage efficiency into account
An effective computation component
distribution model
2.2 Offloading Model
Fig. 3 shows how the current offloading process
appears considering the major participants. However
the components involved in a typical mobile
application computation offloading to Cloud can be
categorized as below:
Surrogate – a computing node or a virtual
environment made available by the Cloud
service provider where the offloaded code
can run
Partitioner – divides and sorts out the
application components to be offloaded for
Cloud-based computing, this could be static
or dynamic (Porras, J. Riva, 2009)
Context Monitor – Monitors and provides
context related information about the
device, the available surrogates in the local
area, battery status, connectivity etc.
Solver – uses the information from Context
Monitor to decide which surrogate to
offload the computation the Partitioner
decides to offload
Adaptive Computation Offloading in Mobile Cloud Computing
Coordinator – authenticates and securely
sends computation over to surrogate,
receives and presents the results from the
….. Cloud
Figure 3: Offloading Mobile Computation.
2.2.1 Offloading Granularity and Surrogate
Several approaches are in use at the moment, with
each having its own pros and cons. Traditional
remote method invocation (RMI) and client-server
model for offloaded computation keep the object-
oriented-ness for mobile app designers but require
synchronization of variables which may mean error-
prone context-insensitive computation results.
Virtual Machine based offloading can be a language
based VM for the mobile app or a mirrored mobile
computing environment with the ability to run any
app in a given mobile environment.
(Xu, Chen et al, 2015)
propose a game theoretic
approach to decision making problem in
computation offloading amongst multiple mobile
device users. They formulate the problem as a multi-
user computation offloading game and prove that it
always admits Nash equilibrium. They also propose
a distributed offloading algorithm that can achieve
this equilibrium. However, the dynamicity of mobile
users frequently joining and leaving the game on-
the-fly has yet to be considered in this approach.
None of the above solutions however, make it easy
for the Partitioner to decide the granularity of
offloading computation components on-the-fly or for
the Coordinator to decide which Surrogate to offload
2.2.2 Opportunistic Partitioning
For an opportunistic Partitioner the application
should be able to compute the cost of local versus
cloud-based computation based on the context
information e.g. battery usage, available bandwidth
and quality of network connection. Derived from the
report by (S. Pachamuthu & Kumar, 2016) a simple
formula to compute the cost can be as below:
α (Tcloud - Tlocal )/Tlocal + (1-α )(Ecloud - Elocal)/Elocal
Where α denotes a weight between 0 and 1 towards
computing time T or energy consumed E being
assigned that weight.
Mobile Apps that can run optimizations to decide
what parts of their computation should be offloaded
and what should be run locally can either come with
pre-defined static partitioning of the program into
components while some others can run this
optimization itself in the cloud.
2.2.3 Dynamic Surrogate Selection
In Section 2.2.1, we mentioned the complexity with
Coordinator to decide which Surrogate to connect to
for offloading. Considering the mobility of a
handheld, we acknowledge another level of
complexity to the decision making – the mobile
device should potentially be able to connect and
offload to multiple Surrogates and able to receive
and reconstruct results from them dynamically.
2.3 Current Solutions
2.3.1 Native Mobile Cloud Computing
Some solutions in use today have been designed
specifically to solve the offloading from mobile
devices. These solutions fall in two broad categories.
The first category is, where the entire mobile
application or parts of it are run in Virtual Machines
mimicking the mobile device on servers to decide
which threads of methods should be offloaded when
running from a mobile device. CloneCloud (Chun,
B.G, 2011) and MAUI (Cuervo, E., 2010) are such
examples. The application running on the server’s
VM is profiled for its cost of computation.
In the second category, the computation
components are analysed a-priori and decisions are
made to offload more computation intensive tasks to
Surrogates. This solution doesn’t take into account
the context sensitivity of the mobile device and
hence results can be unsatisfactory.
1) Offload
2) Request computation
3) Receive results
Offloaded Computation
onent Execution
CLOSER 2017 - 7th International Conference on Cloud Computing and Services Science
2.3.2 Frameworks and Domain Specific
There are many programming languages in market
today, specifically designed to solve problems in
domains like mobile commerce etc. These languages
are often required to run in their specialized run time
environment. While this solution follows an agile
approach as the developers are focussed in the
domain, it does require developers to niche their
expertise for a specific language and run time
Various frameworks and platforms like Java
Remote Method Invocation provide proven
methodologies for distributed execution of
applications, thereby letting the developer focus on
the application functionalities. However, these
conventional standard frameworks are not flexible
enough to support mobile computation offloading in
rapidly varying contexts.
2.3.3 Distributed Virtual Machines
In this approach multiple VMs run on multiple
nodes in a network and run computation components
with a common global state of the application
(Coulouris, G., Dollimore et al, 2012). However this
solution requires the mobile device to support the
distribution of the tasks. There are proposals for
using embedded devices in routinely used objects
like shoes, watches and so on for computation
offloading on personal distributed systems
(Niroshinie Fernando. et al, 2013)
(Verbelen, T., 2012) propose a new architectural
paradigm by converging mobile computing, Internet
of Things (IoT) and Cloud Computing. A Cloudlet
sits between the Cloud and the Mobile or IoT device
and can serve as a small data centre bringing the
cloud closer to the device by providing a PaaS layer
which can support wearable cognitive assistance,
making possible new flurry of applications based on
Artificial Intelligence and Augmented Reality for
the Mobile or IoT user.
2.3.4 Other Solutions
Some solutions take the application partitioning and
component execution to the system level. Mobile
applications in this approach are designed to assure
that computationally intensive parts of the app can
be run in distributed environment. These solutions
do not however do part by part offloading from the
mobile device and instead consider the application in
entirety as per the design.
We propose that an effective cloud-enabled Mobile
App should be elastic in its decision-making of what
and where to offload its computation.
Additionally, the Mobile Cloud Computing
infrastructure should be able to provide a dynamic
selection of Surrogates that can pass on partial or
completed results from offloaded computation based
on the changing context and location of the device.
The benefit in our model arises from the Surrogates
providing a virtual PaaS by partially or completely
participating in the computation of offloaded
requests. The Mobile device may not be aware of
this virtual network of Surrogates and may receive
the results of its offloaded computation from any
one of the participating Surrogates.
3.1 Virtual Network of Surrogates
Figure 4: Virtual Networks of Surrogates.
We propose a model for surrogates that connect
amongst themselves via a cloud-based hub that
provides Data as a Service. When a mobile device
selects for example Surrogate1 in the Fig.4, based on
the application’s requirements, it either precomputes
partitioning or offloads the execution components
entirely to that Surrogate, any computation results
and context information is sent to the DaaS Hub,
which upon request provides either partially
computed or completed results to another Surrogate
which the mobile device may have selected upon
location change.
Context information and the cost of offloading may
also have changed with the location, and hence that
should be taken into consideration when selecting
the next Surrogate.
Adaptive Computation Offloading in Mobile Cloud Computing
Having a Hub providing DaaS, resolves several
issues like availability, since some computation
results may always be available for the mobile
device via the Hub once at least the first Surrogate
was selected for offloading. It also provides data
security as most of the data intensive transactions
would be limited within the Surrogate network.
The above proposed model also eases
maintainability of mobile applications as developers
can focus on the application itself while most of the
intensive computation, including the optimization
for Partitioner to decide what components to offload,
can be run in selected Surrogate once and all that is
exchanged with subsequently selected Surrogates
could be Context Sensitive information.
3.2 Multi-Surrogate Distributed
The model proposed in Section 3.1 would also allow
for more scalability as multiple Surrogates can be
employed in an elastic manner to compute a
distributed task intensive application. The selected
Surrogates can, not only compete for their
candidacies based on the Virtual Environment they
can provide to run parts of computation from a
mobile app, they can also utilize results from
participating Surrogates.
Figure 5: Distributed Computation Offloading.
This way we can apply heterogeneously specialized
VMs, for example to provide Cloud Based Machine
Learning routines, Video Games and so on.
3.3 Centralized Offload and
Another approach with the abovementioned virtual
network of surrogates can be as shown in Fig. 6 for
the mobile application to offload centrally to the first
selected Surrogate which acts as a master and
decides to distribute tasks to other Surrogates based
on the resource availability. Big Data solutions like
Hadoop internally employ such distribution of tasks.
Figure 6: Centralized Computation Offloading.
The main challenges in the field of Mobile Cloud
Computing include Context Sensitive Computation
Offloading. In order for the Mobile users to leverage
from the vast resources and functionalities offered
by rapidly advancing Cloud Computing
technologies, much further research is required in
the area of computation offloading.
We studied state of the art Mobile Cloud
Computing, current challenges and a few proposed
solutions. Based on the study we proposed a model
for opportunistic and adaptive offloading with a
focus on the context awareness of computation
intensive mobile applications. Further work is
required to run evaluations with computation
intensive mobile applications using the model and
fine tune the model.
In conclusion, Mobile Cloud Computing is still in its
nascent state and while much of the traditional
distributed application approaches can be utilized to
some extent in the context of MCC, certain areas
like adaptive context sensitive computation
offloading require further research.
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