A Survey of Research on Mobile Cloud Computing

2011 10th IEEE/ACIS International Conference on Computer and Information ScienceA Survey of Research on

Mobile Cloud Computing

Le Guan

Beijing University of Posts

and Telecommunications

Beijing, China

optimism1226@

Abstract—The rapid development of mobile computing and cloud

computing trigger novel computing paradigm-----Mobile Cloud

Computing. This paper review current research effort towards

Mobile Computing. 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 partition & offloading , and context-aware service.

Xu Ke

Beijing University of Posts

and Telecommunications

Beijing, China

permit@

Meina Song

Beijing University of Posts

and Telecommunications

Beijing, China

mnsong@

Junde Song

Beijing University of Posts

and Telecommunications

Beijing, China

jdsong@

security and privacy, elastic mobile applications requirement

may obstruct the development of Mobile Cloud Computing as

well.

Nowadays, the market of mobile phone has expanded

rapidly. By the end of 2009, less than 20 years later, the

number of mobile cellular subscriptions worldwide reached

approximately 4.6 billion, 370 times the 1990 number [1]. The

widely use of mobile phone lead to the prosperity of mobile

service. Dream of “Information at your fingertips anywhere,

anytime” has become true. However, mobile devices still lack

in resources compared to a conventional information

processing device such as PCs and laptops. Also, the limitation

of battery restricts working time. How to augment capability of

mobile phone has become the important technical issue for

mobile computing.

The paradigm of cloud computing brings opportunities for

this demand. Cloud computing provide new supplement,

consumption, and delivery model for IT service. Cloud-based

services are on-demand, scalable, device-independent and

reliable. Thus, there comes Mobile Cloud Computing, which

aims at using cloud computing techniques for storage and

processing of data on mobile devices, thereby reducing their

limitations. According to ABI Research, by 2015, more than

240million business customers will be leveraging cloud

computing services through mobile devices, driving revenues

of $5.2billion[2].

To deliver cloud service in mobile environment, we might

face several problems. Device may hand off among different

wireless transmission district, and transport channels are not so

reliable to guarantee cloud service delivery. Furthermore,

mobile devices can’t handle complicated applications due to

their innate characters. Also, it is impossible that mobile device

always online, that is, we should consider the offline solution

for mobile device. What’s more, the absence of standards,

978-0-7695-4401-4/11 $26.00 © 2011 IEEEDOI 10.1109/ICIS.2011.67Researchers provide various solutions for Mobile Cloud

Computing service. Some proposal application partition &

offload schemes to leverage the working load of Cloud and

Client, which may reduce processing burden on the mobile

client. Several researchers focus on the feature of “Mobile”, to

Keywords-mobile cloud computing, application partition,

provide context-aware service for users, which may triggers

offloading, context-aware

new applications for mobile environment. Contexts include

geo-location and social activities.

I.

INTRODUCTION

This paper introduces the basic model of Mobile Cloud

Computing, and surveys state-of-art of systems. First, we

describe technical challenges of Mobile Cloud Computing.

Then, after introducing concept model and basic architecture,

we survey key technologies, e.g. partition & offloading and

context-based service. At last, we conclude the recent research

activities of Mobile Cloud Computing.

The rest of paper is organized as follows: SectionⅡshows

challenge of Mobile Cloud Computing service. Section Ⅲ

presents basic model and architecture of Mobile Cloud

Computing systems. Section Ⅳtalks about partition &

offloading schemes and SectionⅤdescribes context-aware

service. Section Ⅵ conclude the whole paper.

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. online gaming and augmented reality that

require high-processing capacity and minimum network

latency. These will most probably continue to be processed

387

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.

A. Concept model

As well known, cloud computing service can be divided

into three types according to delivery manner: Infrastructure as

a Service (IaaS), Platform as a Service (PaaS), Software as a

Service (SaaS). However, Mobile Cloud Computing would not

separate into these types. Mobile Cloud Computing focuses on

B. Various access scheme in mobile envinroment

the connection between client and cloud, which may differ

Mobile Cloud Computing would be deployed in a

from common features of cloud computing.

heterogeneous access scenario with different radio access

In architectural considerations of creating next generation

technologies such as GPRS, 3G, WLAN, WiMax. Mobile

mobile applications, Jason H Christensen [5] proposal three

Cloud Computing requires wireless connectivity with the

component archetype: the combination of smart mobile device,

following features:

REST based cloud computing, and context enablement. This

• Mobile Cloud Computing requires an “always-on”

connectivity for a low data rate cloud control signaling

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.

three component model matches with transmission model of

Mobile Cloud ---“Client-Connection-Cloud”.

•

•

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 cost-efficiency [3].

C. Elastic application models

Cloud Computing services are scalable, via dynamic

provisioning of resources on a fine-grained, self-service basis

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 [4].

III. CONCEPT MODEL AND

ARCHITECTURE

.In this section, we present concept model to analyze

mobile cloud computing technology, and then provide several

architecture model to organize Mobile Cloud Computing

systems.

We can reconstruct concept model on vertical view, as

shown in Figure.1. The left and right entities are respectively

client and cloud. Between client side and cloud side there is

“Transmission Channel” component. Upon this entity are

“Resource Scheduling” and “Context Management”

components, both of which occupy client and cloud sides. The

prerequisite of this model is that: a) The client is context-aware;

b) Cloud side should deliver elastic, on-demand service for

client. Next, we explain three middle part of the model with

down-top approach.

Context MangementResource SchedulingClientTransmission ChannelCloud

Figure 1. Concept model of Mobile Cloud Computing

1) Transmission Channel

Transmission channel refers to various wireless transport

protocols. The wireless connection between client and cloud is

double-edged sword for mobile cloud computing applications:

For one thing, the weak transmission channels degrade

performance of stable cloud service; for another, dynamic

characters of connection produce various contexts, which

trigger prosperous mobile applications.

2) Resource Scheduling

Resource scheduling component address the schedule o

resource, such as computing resource and storage resource. In

this level, virtual machines will be introduced to handle of

resource dispatch. Nevertheless, we can view this problem in

another view. Resource may be stable but applications may

transmit to other places. In mobile cloud computing scenario

we often consider to decompose complex application and

handle application with parallel methods. Usually, application

partition and offloading may contribute to usage of mobile

device. Partition and offloading approach will be studied in

Section Ⅳ.

388

3)

Context Management

Context Enabled features of mobile device allow us to

ascertain additional information from the computing device

itself without the need for explicit user input. Context

Management module can track context parameters and adapt to

modification of context conditions. This capability has enabled

a number of new application spaces such as Location Based

Services (LBS), spatial augmented reality (SAR), and explicit

spatial contexts using Bluetooth or WiFi.

Typically, context can be classified into two types:

a) Spatial contexts

Spatial contexts are contexts that are based on position,

proximity. They allow context-aware applications to provide

input for Location Based Service. For example, my iPhone can

get location information and provide to Foursquare software,

then I can play online games.

b) Social contexts

Social contexts are contexts that have been explored in

social network analysis threads. In the context of mobile

computing these contexts are particular ones that out of

inherent characters of mobile computing but encourage user to

group interaction.

Context management technology will be surveyed in

Section Ⅴ.

B. Architecture

The architecture of Mobile Cloud Computing refers to the

organization of Mobile Cloud Computing systems. Generally,

most researchers want to enhance capability of mobile devices

with cloud technology. Also, some researchers explore the use

of cloud computing to execute mobile applications in behalf of

the device. Thus, architecture scheme contains two types:

agent-client scheme and collaborated scheme.

1) Agent-client scheme

In this scheme, cloud side provides overall resource

management for mobile devices, to help to overcome

limitations of mobile devices in particular of the processing

power and data storage. As is shown in Figure 2, cloud side

generate agent for each device. Mobile device communicate

with its agent to contact with other entities outside this domain.

Mahadev Satyanarayanan [6] provided cloudlet-based,

resource-rich, mobile computing. In this architecture, a mobile

user exploits virtual machine technology to customize service

software on a nearby cloudlet and then uses that service over a

wireless LAN; the mobile device typically functions as a thin

client with respect to the service. A cloudlet is decentralized

and widely-dispersed Internet infrastructure whose compute

cycles and storage resources can be leveraged by nearby

mobile computers. The natural implementation is to extend Wi-Fi access points to include substantial processing, memory and

persistent storage for use by associated mobile devices.

Xinwen Zhang [7] built elastic applications which augment

resource-constrained platforms. An elastic application can

consist of one or more weblets, which function independently,

but communicate with each other. When the application is

launched, an elasticity manager running on the device monitors

the resource requirements of the weblets of the application, and

makes decisions where they should be launched. Computation

intensive weblets usually strain the processors of mobile

devices, therefore they can be launched on one or more

platforms in the cloud; while user interface components (UI) or

those needing extensive access to local data may be launched

on the device.

2) Collaborated scheme

Collaborated schemes regard device as a part of cloud. This

approach utilize remain resource of mobile device. The

function of cloud server may be the controller and scheduler

for collaboration among devices.

Figure 3. Collaborated architecture for Mobile Cloud Coputing

Hyrax [8] is a platform derived from Hadoop that supports

cloud computing on Android smart phones. Hyrax allows client

applications to conveniently utilize data and execute computing

jobs on networks of smart phones and heterogeneous networks

of phones and servers. By scaling with the number of devices

and tolerating node departure, Hyrax allows applications to use

distributed resources abstractly, oblivious to the physical nature

of the cloud. In Hyrax, several traditional machines play the

role of NameNode and JobTracker.

Black and Edgar [9] demonstrated the feasibility and value

of enabling mobile devices within a grid computing framework

by implementing the BOINC client on an Apple iPhone. Work

units are downloaded from a BOINC server and executed on

the iPhone via a virtual machine emulating an x86 processor,

and results are uploaded to the server. The world of mobile

Figure 2. Agent-client architecture for Mobile Cloud Computing

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devices brings renewed challenges to the problem of grid client

design in the areas of network bandwidth, processor capability,

storage, and energy consumption.

of offloading percent and methods. Should we put all

applications to cloud?

Byung-Gon and Petros [14] first introduced offloading

execution from the smart phone to a computational

IV. APPLICATION PARTITION AND OFFLOADING

infrastructure hosting a cloud of smart phone clones. The idea

Application partition and offloading technology play an

is simple: clone the entire set of data and applications from the

important role for the implementation of elastic applications.

smart-phone onto the cloud and selectively execute some

Application partition decompose complex workload to atomic

operations on the clones, reintegrating the results back into the

ones, thus can be processed concurrently. Offloading

smart-phone. There are five types of augmentation, each of

application can free burden of mobile devices and save their

which uses special method to offloading. One can have

multiple clones for the same smart-phone, clones pretending to

energy consumption.

be more powerful smart-phones, etc.

A. Partition

Eduardo Cuervo [15] el. presented MAUI, a system that

To achieve seamless and transparent migration and

enables fine-grained energy-aware offload of mobile code to

offloading, each application should be partitioned into

the infrastructure. It maximizes the potential for energy savings

components. Application partition should consider resource

through fine-grained code offload while minimizing the

changes required to applications. First, MAUI uses code

consumption and data dependency.

portability to create two versions of a smart phone application,

Ioana Giurgiu [10] el. proposed two-step approach to

one of which runs locally on the smart phone and the other runs

optimally partition an application between a mobile phone and

remotely in the infrastructure. Second, MAUI uses

a server. First, they abstract an application’s behavior as a data

programming reflection combined with type safety to

flow graph of several inter-connected software modules. Given

automatically identify the remote methods and extract only the

this graph, in the second step, a partitioning algorithm finds the

program state needed by those methods. Third, MAUI profiles

optimal cut that maximizes (or minimizes) a given objective

each method of an application and uses serialization to

function. They propose two types of partitioning algorithms:

determine its network shipping costs

ALL and K-step. In the first case, the best partitioning is

computed offline by considering different types of mobile

V. CONTEXT-AWARE

SERVICE

phones and network conditions. In the second case, the

partitioning is computed on-the-fly, when a phone connects to

It is context that distinguishes mobile cloud computing

the server and specifies its resources and requirements. ALL

from common concepts. Context leads to advent of various

fits the first scenario, while K-step the second one.

mobile applications. As mentioned in Section Ⅲ,contexts can

Xun Luo [11] presented “Cloud-Mobile Convergence for

be classified into two types, spatial contexts and social contexts.

Virtual Reality (CMCVR)” concept. In CMCVR, to take

First we introduce common context management methods, and

advantage of the better load balancing inherent in by-region

then discuss

application partitioning, the author proposed Hybrid

Application Partitioning Strategy, also in two steps: The first

A. Common context management

stage breaks down the workload with the by-scale strategy;

Andreas Klein [16] el. presented a framework for the use of

large workloads at high scale levels are further partitioned in

context information for the Heterogeneous Access

the second stage which uses the by-region strategy. The

Management (HAM) provided by the Mobile Cloud as a

partitioning process is completed when an optimized overall

service for the mobile terminals. A formal method assessing

system performance is achieved.

link quality based on available context information has been

Byung-Gon and Petros [12] introduced the notion of

developed for triggering handover mechanisms. The proposed

Context Management Architecture (CMA) is responsible for

dynamic partitioning of applications between weak devices and

clouds and argue that it is the key to addressing heterogeneity

acquiring, processing, managing, and delivering context

problems. The author found that partitioning applications

information. Context Quality Enabler (CQE) controls the

provision of context information according to the requirements

statically does not provide optimal user experience as more and

more applications are used in diverse environments and inputs.

of the Mobile Cloud Controller. Finally, based on the outlined

So it is decision to demand particular purpose. The decision

HAM concept, the author presented a context-aware radio

network simulator (CORAS) that is able to model context

may be impacted not only by the application itself, but also by

the expected workload and the execution conditions, such as

availability, accuracy, and delay, thus enabling an evaluation of

network connectivity and CPU speeds of both weak and cloud

the impact of different levels of context relevance, confidence,

devices. After formalizing the dynamic partitioning problem,

and quality on simulation results.

and sketch how to construct a system that supports dynamic

partitioning.

Hyun Jung La [17] presented a framework for enabling

context-aware mobile services. The framework enables tasks of

capturing context, determining what context-specific

adaptation is needed, tailoring candidate services for the

context, and running the adapted service. The net result of

context-aware services is for consumers to receive better

services which fit to the current context of the consumers.

B. Offloading

Offloading task from client to cloud can reduce energy

consumption of mobile device [13]. The problem is the choice

390

Aaron Beach [18] presented a vision of mobile-cloud

computing in which context-aware services are organized and

integrated by a Context-Aware Intention Compiler (CAIC).

Run-time creation of these programs allows contextual

information from a mobile phone and the environment to be

integrated in real-time. Furthermore, the mobile device can

look up context-aware services using a Contextual Lookup

Service, which maps context and intention to the appropriate

Context-Aware Intention Compiler. Use of the CAwbWeb

framework allows mobile-cloud challenges to be divided into

four major concerns: specifying intention, describing context,

identifying appropriate actions, and efficient actuation of those

actions.

B. Spatial contexts service

Patrick Stuedi [19] el. presented WhereStore, a location-based data store for Smartphones interacting with the cloud.

The key property of WhereStore is that it uses the phone's

location history to determine what data to replicate locally. The

main goal of caching cloud data on the phone is to decrease the

overall data access latency and also reduce the probability of

data becoming unavailable in periods of no connectivity.

Furthermore, WhereStore is a shared resource for different

applications and exchanges data with the cloud in batches, thus

potentially reducing the overall energy consumption on the

phone.

Pelin Angin [20] el. proposed a mobile-cloud collaborative

approach for context-aware navigation by exploiting the

computational power of resources as well as location-specific

resources available on the Internet. The author proposes an

extensible system architecture that minimizes reliance on

infrastructure, thus allowing for wide usability.

C. Social contexts service

Dejan Kovachev [21] el. proposed Mobile Community

Cloud Platform (MCCP) as a cloud computing system that can

leverage the full potential of mobile community growth. Also,

the author analyses the requirements of mobile communities,

proposes a cloud computing model for mobile communities,

and discusses the technical settings of this cloud infrastructure.

Lan Zhang [22] el. designed and constructed a multi-hop

networking system named MoNet based on WiFi, and a

privacy-aware geo-social networking service. Also the author

designs a distributed content sharing protocol which can

significantly shorten the relay path, reduce conflicts and

improve data persistence and availability. A role strategy is

designed to encourage users to collaborate in the network.

Furthermore, a key management and an authorization

mechanism are developed to prevent some attacks and protect

privacy.

Eric Jung [23] el. proposed to exploit the potential of smart

phones in proximity cooperatively, using their resources to

reduce the demand on the cellular infrastructure. The author

introduces RACE (Resource Aware Collaborative Execution),

a Markov Decision Process (MDP) optimization framework

that takes user profiles and user preferences to determine the

degree of collaboration. Then RACE can enable the use of

other mobile devices in the proximity as mobile data relays.

VI. CONCLUSION

This paper surveys recent research activities on Mobile

Cloud Computing. Mobile Cloud Computing aims to utilize

cloud computing techniques for storage and processing of data

on mobile devices, thereby reducing their limitations. Several

problems would challenge the development, including intrinsic

nature of mobile device and wireless connection. Then we

proposal concept model for Mobile Cloud Computing systems

and analyze typical architecture. After that, we discuss the

detail of technology, application partition & offloading and

context-aware services.

ACKNOWLEDGMENT

This work is supported by the National Key project of

Scientific and Technical Supporting Programs of China (Grant

Nos.2008BAH24B04, 2008BAH21B03); the National Natural

Science Foundation of China (Grant No.61072060); the

Program of the Co-Construction with Beijing Municipal

Commission of Education of China.

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