resource optimization in mobile cloud environments

8/2/2019 Resource Optimization in Mobile Cloud Environments

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School of Engineering & Design

Electronics & Computer Engineering

M.Sc. Data Communication Systems

Brunel University

Resource Optimization inMobile Cloud Environments

Qamar Idrees (1040511)

DR. Maozhen LiSeptember/2011

A Dissertation submitted in partial fulfilment of the requirements for

the degree of Master of Science


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ABSTRACT

Cloud computing means a technology, which involves virtualization, distributed computing,

networking, software and web services. The elements that are included in a cloud may consist

of mobiles clients, datacenters and distributed servers. Major issues and challenges that

should be considered in developing a cloud environment includes fault tolerance, high

availability, scalability, flexibility, reduced overhead for users, reduced cost of ownership, on

demand services etc. To overcome these issues different algorithms and techniques have been

developed that includes load balancing, resource optimization and scheduling to enhance theperformance of cloud environment. In a cloud environment resources can be shared among

many servers and nodes to overcome many issues like scalability, reliability and utilization.

In this project a mobile cloud environment is created and different techniques are developed

to ensure that all the jobs performed in the mobile cloud should be shared among virtual

servers. Furthermore job scheduling techniques like Particle Swarm Optimization techniques

have been introduced to schedule the jobs performed by each virtual machine running in the

mobile cloud.


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ACKNOWLEDGMENT

First of all I would like to thank to Almighty ALLAH who gave me the strength to finish this

project. Then I would express my profound sense of gratitude towards my supervisor Dr.

Maozhen Li , Professor, Department of Engineering and Design, for his able guidance,

support and encouragement throughout my entire one year of study. His readiness for

consultation at all times, his educative comments, his concern and assistance even with

practical things have been invaluable.

I would also like to convey my sincerest gratitude and indebtedness to our entire faculty

members and staff of the Department of Engineering and Design, Brunel University West

London, who bestowed their efforts and guidance at appropriate times without which it would

have been very difficult on my part to finish the project work. Finally, I would like to thank

my parents, my family members and friends for their unconditional love and selfless support.


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Table of Contents1. Introduction..................................................................................................................................... 1

1.1. Introduction ................................................................................................................................. 2

1.1.1. Cloud Computing ................................................................................................................. 2

1.1.2. Mobile Cloud Computing..................................................................................................... 3

1.1.3. Challenges ........................................................................................................................... 3

1.2. Motivation ................................................................................................................................... 4

1.3. Aims and Objectives .................................................................................................................... 4

1.4. Project Management ................................................................................................................... 5

1.5. Organization of Report ................................................................................................................ 6

2. Literature Review ............................................................................................................................ 7

2.1. Introduction ................................................................................................................................. 8

2.2. Types of Cloud Computing .......................................................................................................... 8

2.2.1. Public Cloud ......................................................................................................................... 9

2.2.2. Private Cloud ....................................................................................................................... 9

2.2.3. Hybrid Cloud ........................................................................................................................ 9

2.3. Layers of Cloud Computing ....................................................................................................... 10

2.3.1. Hardware Layer ................................................................................................................. 11

2.3.2. Virtualization Layer ............................................................................................................ 11

2.3.3. Infrastructure as a Service (IaaS) ....................................................................................... 11

2.3.4. Platform as a Service (PaaS) .............................................................................................. 11

2.3.5. Software as a Service (SaaS) .............................................................................................. 12

2.4. Evaluation of cloud services ...................................................................................................... 12

2.4.1. Measuring Cloud computing Properties ........................................................................... 12

2.5. Challenges in Cloud Computing................................................................................................. 12

2.6. Mobile Cloud Computing .......................................................................................................... 14

2.7. Features and Benefits of Mobile Cloud ..................................................................................... 15

2.8. Challenges and Issues ................................................................................................................ 16

3. Design of Mobile Cloud ................................................................................................................. 18

3.1. Introduction ............................................................................................................................... 19

3.2. Android Overview ...................................................................................................................... 19

3.3. Tools and Software requirement .............................................................................................. 20

3.4. Programming Languages ........................................................................................................... 21


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Resource Optimization in Mobile Cloud Environments

1

Chapter 1

1.Introduction


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1.1. Introduction

This chapter will give some introduction about cloud computing especially in the mobile

environment and the challenges that are being faced in the current cloud computing

environment. It also explains the organization, management and structure of the report

1.1.1.Cloud Computing

Cloud Computing the name indicates having the computational powers in the cloud, means

those providing single/multiple services/resources to many users so anyone can access that

service and get benefit without using its own resources. This was the simple definition of

could computing but cloud computing is actually more than that. Figure 1 shows the logical

diagram of cloud in which many different users are accessing the resources from the cloud

and using it.

Figure 1: Cloud Computing Environment


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Cloud computing relies on running/managing service/application remotely, allowing the

scope of dynamic scaling, remote management and diversity[25]

.

1.1.2.Mobile Cloud Computing

The market of mobile recently has been evolving rapidly and cloud computing is spreading

into mobile as well[2]

.

Mobile cloud computing has developed a new for IT industry because it allows the

superiority and economic of cloud computing to meet the mobility and convenience of

mobile. Also mobile computing refers to an infrastructure that data storage and data

processing is done outside mobile device by using cloud computing in the regardless of

kinds of mobile devices. Mobile devices used in the mobile environment include personalinformation and enable to provide the environment that collects a variety of context-aware

information. Therefore, context-aware reasoning technique has been studied to provide a

suitable service for user by using user context and personal profile information in mobile

environment[28]

.

1.1.3.Challenges

The biggest challenge is security. How to secure each companies data. Multi-level

authentication could be a solution to this. However, more development in this field has to

done as proper security solutions will be a key element of industrial cloud. Other challenges

consist of dealing with many versions of truth for reasoning purposes, what is result of

shared environment of data in many formats. Enabling old data to be used in the new

environment is also a challenge. It is important as companies want to use all the data they

already have. However in mobile cloud Bandwidth and network is also important to

consider.


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1.2. Motivation

Cloud computing is a vast concept. Many of the algorithms for load balancing, optimization

etc in cloud computing have been proposed. The whole Internet can be considered as a

cloud of many connectionless and connection oriented services. Many big IT companies like

Google, Microsoft, and Amazon etc are prompting the whole world to be switched on cloud

computing by providing services like MSN sky drive, Google docs, etc. Users can upload,

view and manage their documents online without spending too much on buying expensive

hardware. The focus of this project is to develop an android application based on

virtualization technique and sharing the resources among different virtual machines in the

cloud and also to analyze the performance of single virtual machine compared with multiple

virtual machines in the cloud by using Optimization techniques such as Particle Swarm

Optimization.

1.3. Aims and Objectives

To study in detail the topics of cloud computing, Mobile cloud computing and the

challenges that could be faced in this technology.

Detailed study of research papers and writing the literature review based on these

research papers.

Getting familiar with Virtualization technologies VMware also getting familiar with

android client server architecture.

Understating of different programming languages and platforms like JAVA, PHP and

eclipse. Also different encoding techniques like XML and JSON to be used in this

project.

Design of mobile cloud and developing an android client and server application to

show how a mobile client uses the server resources in the cloud.

Resource optimization by sharing resources among virtual machines.

Performance optimization using Particle Swarm Algorithm and load balancing based

on number of server running in the cloud.

Comparing the performance of single virtual machine with multiple virtual machines

running in the cloud.

Providing different solution to optimize the Virtual Servers performance.


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Providing solutions to challenges that could be faced.

Conclusion and future work.

1.4. Project ManagementThis project has to be accomplished in 16 weeks and in order to finish it time is important

factor. This project has been divided into different tasks based on timely manner i.e.

number of weeks is assigned to each task that is shown in the Gantt chart below:

Week numbers

Tasks 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 Discussion with supervisor

2 Literature Review

3

Learning tools and

technologies to be used in this

project

4Designing Class Diagrams and

topologies

5

Developing android

application

6 Writing Chapter 1,2,3

7Developing java program for

Simulating and Testing

8 Analysing

9 Conclusion

1

0Proof Reading and Binding

1

1Submission

Figure 2Gantt Chart

I divided this project in 11 tasks which have to be done in specific period of time as shown in

the Figure 2 above.


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1.5. Organization of Report

This report consists of six chapters. A brief description of each chapter can be seen below:

Chapter 1:

Chapter 1 gives a brief introduction of concepts of cloud computing, mobile cloud and

challenges.

Chapter 2:

Chapter 2 is the literature review about the cloud computing, mobile cloud computing,

features and challenges that is done by the research papers studied during the project.

Chapter 3:

In chapter 3 we developed the design of mobile cloud with GUI using the programming

languages PHP and JAVA and Virtualization tool VMware workstation and explained the

classes with figures.

Chapter 4:

Chapter 4 is about the particle swarm optimization technique is used to enhance the mobile

cloud architecture and also we did load balancing based on 5 parallel running virtual

machines to enhance scalability and load sharing.

Chapter 5:

This chapter presents the conclusion about the work done in this whole project.


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Chapter 2

2. Literature

Review


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2.1. Introduction

In this chapter we are going to explain cloud computing, mobile clouds, Layers, and

Evaluation that we studied in the research papers and going to discuss the challenges and

benefits of cloud computing and mobile clouds.

2.2. Types of Cloud Computing

There are three types of cloud computing to be considered now a day.

1. Public Cloud

2. Private Cloud

3. Hybrid Cloud

Figure 3 Types and services of cloud computing [6]


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2.2.1.Public Cloud

Public cloud refers to that type of cloud in which users pay for the services that they use in

this cloud, like data storage. It is based on an on demand storage scalability and isnt

required to buy storage hardware. Its typically based on a pay-per-use model, similar to a

prepaid electricity metering system which is flexible enough to cater for spikes in demand

for cloud optimisation. This helps cloud clients to better match their IT expenditure at an

operational level by decreasing its capital expenditure on IT infrastructure[7].

Public clouds

are less secure than the other cloud models because it places an additional burden of

ensuring all applications and data accessed on the public cloud are not subjected to

malicious attacks. Therefore trust and privacy concerns are rife when dealing with Public

clouds with the cloud SLA at its core[6]

.

2.2.2.Private Cloud

Private cloud is infrastructure operated solely for a single organization, whether managed

internally or by a third-party and hosted internally or externally[8].

To deploy this type of

cloud more cost is required on the hardware. To overcome the cost many virtualization

techniques have been developed. Now the industries and companies can develop their

private cloud on the virtualization softwares like VMWARE in which the private cloud can

be deployed in a single hardware. The deployment of virtualization has reduced the cost of

hardware in private clouds.

2.2.3. Hybrid Cloud

The hybrid cloud is the combination of Private cloud and public cloud in which one or more

private clouds are connected to one or more public clouds to share their resources. The

cloud infrastructure is a composition of two or more clouds (private, community, or public)

that remain unique entities but are bound together by standardized or proprietary

technology that enables data and application portability (e.g., cloud bursting for load

balancing between clouds)[8]

.


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2.3. Layers of Cloud Computing

In context of cloud computing environment there are five layers that need to be considered:

1. Hardware Layer.

2. Virtualization Layer.

3. Infrastructure as a Service layer (IaaS).

4. Platform as a Service layer (Paas).

5. Software as a Service layer (SaaS).

Figure 4 Layers of Cloud Computing [9]

Note that each layer starting from the top is dependent on the layer below to it. A pyramid

above best illustrates the conceptual 'size' of each layer and it also conveys the message

that each layer is based upon the previous layer. So conceptually, you understand that the

hardware is the foundation and the widest layer. Software as a Service (SaaS) is the

capstone and consequently the lightest layer[9]

.


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2.3.1.Hardware Layer

The base layer in cloud computer architecture is hardware layer. In this layer we have all the

hardware installed for cloud computing environment. This layer could have many servers,

data storages, power etc. This is the base of all the layers on which whole of the cloud is

dependant.

2.3.2.Virtualization Layer

After hardware layer is the virtualization layer. This layer can have many operating systems

running in kind of pool and at the end of each operating system a cluster of hardware is

installed to perform functions. Virtualization layer is a fundamental layer of this system and

failure of system at this layer will cut off the link between the upper layers and the physical

hardware itself. This can produce disastrous results for customer oriented markets. There

have been a number of cases where goodwill of business has been adversely affected[11]

.

2.3.3.Infrastructure as a Service (IaaS)

Infrastructure as a Service (IaaS) offerings are computing resources such as processing or

storage which can be obtained as a service. Examples are Amazon Web Services with its

Elastic Compute Cloud. IaaS providers typically offer virtualized infrastructure as a service.

Typically by virtualization, hardware level resources are abstracted and encapsulated and

can thus be exposed to upper layer and end users through a standardized and can thus be

exposed to upper layer and end users through a standardized interface as unified resources

(Foster et al. 2008) in the form of IaaS[10]

.

2.3.4.Platform as a Service (PaaS)

Platforms are an abstraction layer between the software applications (SaaS) and the

virtualized infrastructure (IaaS). PaaS offerings are targeted at software developers.

Developers can write their applications according to the specifications of a particular

platform without needing to worry about underlying hardware infrastructure (IaaS).

Developers upload their application code to a platform, which then typically manages the

automatic up scaling when the usage of the application grows. Examples are the Google App

Engine, which allows applications to be run on Googles infrastructure and Sales forces

Force.com platform[10]

.


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2.3.5.Software as a Service (SaaS)

SaaS is the most visible layer of Cloud Computing for end-users, because it is about the

actual software applications that are accessed and used. From the perspective of the user,

obtaining software as a service is mainly motivated by cost advantages due to the utility-

based payment model. Well known examples for SaaS offerings are Salesforce.com and

Google Apps such as Google Mail and Google Docs and spreadsheets[10]

.

2.4. Evaluation of cloud services

In order to evaluate cloud service properties a clear distinction need to be made between

the Quality of Cloud Service (QoWS/QoCS) and the Quality of Real Service (QoR). The quality

of the Cloud Service refers to implementation properties whereas the Quality of the Real

Service refers to properties of the Service itself. Here we use QoWS/QoCS for evaluating

web services suitability assessment for cloud computing[11]

.

2.4.1.Measuring Cloud computing Properties

The properties of cloud computing can be measure using the following parameters.

1. Scalability

2.

Virtualization3. Reliability

4. Availability

5. Response Time

Virtualization is the major property, but measuring virtualization is not possible, because it

varies with load on the system and user requests, hence other means of source is taken for

consideration to make management decision[12]

.

2.5. Challenges in Cloud Computing

Many techniques and concepts have been developed from so many years to enhance

security issues in this field.


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Some of the challenges that the cloud environment could face are below:

Scalability:

Cloud computing environment depends upon the scale of the cloud i.e. the number ofservers involved in it. If there are more servers in the cloud then they can provide high

scalability to the users so more and more future coming users can become the part of that

cloud and the cloud would perform better. Larger is the cloud; faster is the cloud[13]

. The

cloud provider companies have a large number of servers offering service to the user or

consumers. Many Large IT industries like Google, Amazon, IBM, and Microsoft have owned

more than 100s of 1000s server to provide their service to the end users.

Virtualization

Using a Virtualization technique in cloud computing enables the user to get service from

anywhere around the world either on the mobile device or laptop or PC or any kind of

terminal. Some of the major benefits of the virtualization are:

Partitioning (Applications and Platforms supported in a Single Physical system)

Isolation (Virtual machines are separated from the physical system and other virtual

machines there not affecting the other systems in case of failure)

Encapsulation (Virtual machines are encapsulated in a single file that is easy to

understand which virtual machine provides which services)[13]

.

Reliability

Cloud environment must be fault tolerant. In case of any function or system failure it should

not affect the cloud. Cloud uses data multi transcript fault tolerance. It replicates the same

data at different locations or at different machines that ensure high reliability[13]

. Chances

of data crash become less.

Extendibility

The cloud scale should be extendible in case of increased demands. This application brings

up hundreds of virtual servers and on demand runs a parallel computation on them[13]

.


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On demand Service

Cloud is like a prepaid mobile service in which we can use the service according to our need.

The services are being charged according to the amount of our need. It works like pay as you

go manner simple as in homes we pay for electricity bills as how much we used. Similar in

cloud, we pay as we use the resource of the cloud provider[13]

.

2.6. Mobile Cloud Computing

Mobile cloud computing can be thought of as infrastructure where data and processing

could happen outside of the mobile device, enabling new types of applications such as

context-aware mobile social networks. As a result, many mobile cloud applications are not

restricted to powerful smart phones, but to a broad range of less advanced mobile phones

and, therefore, to a much larger subscriber segment. From a smart phone perspective in

particular, mobile cloud computing opens up possibilities for a new class of applications by

leveraging handset centric features and network related information, such as GPS and/or

cell-based location information, etc[17]

.

Mobile cloud computing is an emerging cloud service model following the trend to extend

the cloud to the edge of networks. It includes numerous mobile devices that are closelyassociated with their users. They will be directly involved in many cloud activities that

extend the cloud boundaries into the entire cyber physical system. As predicted by Gartner,

mobile phones will overtake PCs as the most common Web access devices worldwide by

2013[14]

. Thus, mobile devices will become more important and will be involved in almost all

aspects of our daily life[15]

.

Mobile and cloud computing technology will dominate enterprises as these two emerge as

the most in-demand platforms for software application development and IT delivery over

the next five years, according to a survey by IBM. The survey, which was conducted online

by IBM developerWorks, provided insight into the most enterprise technology and industry

trends based on responses from 2,000 IT developers and specialists across 87 countries.


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According to the survey, 55 percent of them expect mobile software application

development for devices such as iPhone and Android, and even tablet PCs like iPad and

Playbook will surpass application development on all other traditional computing platforms

by 2015

[16]

.

From the information above it is clearly stated that mobile cloud computing is becoming

more and more popular now a days and users are switching from Desktops/laptops to

mobile devices like Ipads, Iphone, and many android phones and tablets.

2.7. Features and Benefits of Mobile Cloud

Mobile cloud becomes a new medium to share resources and applications through mobile

channel. As users become more mobile, the applications they use will become mobile-

enabled.

With the evolving technology of mobile computing lets discuss some features and benefits

of mobile clouds:

Mobile cloud computing extends cloud computing by providing enhanced service

availability and by exploiting information about a users location, context andnetwork intelligence, thereby considerably improving user experience. Leveraging

the mobile device storage, sensing and processing resources for optimizing cloud-

based application also adds to better user experience[17]

.

Create new stream of profits. Due to nature of cloud applications, mobile cloud

computing has started creating a new stream of profit for businesses and application

vendors.

Users of Mobile Cloud can access many applications from their mobile phones

enabling them not to stick to single PC but on the go.

Enabling cloud user to login and use many social services like facebook, twitter, orkut

and many more.

Mobile cloud allows users to access their PC/Laptops remotely from their mobile

devices using the softwares like TeamViewer in their mobile phones.


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Many big IT industries like Google, Microsoft, and Yahoo etc are providing the mobile

users to save/edit their documents, images and videos directly from their mobile

phones using the tools like MSN Skydrive, Google Docs, Flicker , Youtube and many

more.

By the use of mobile cloud computing now the mobile users do not need to worry

about their processor capacity because mobile computing provides this service by

processing the users request at the server side.

Provides Virtualization in which users do not need to worry about what is happening

in the cloud. They just ask the cloud to process their request and the remaining is the

responsibility of the cloud to do it.

2.8. Challenges and Issues

"Mobile cloud computing is unique in its challenges," said Renaud Larsen, chief architect for

cloud at Juniper Networks. "The mobile ecosystem has to traverse many complex layers,

each of which adds network latency and transmission delay. In addition, the cloud allows

users to swap devices and retain access to informationthis is a disruptive development,

and means that cloud apps need to cover the whole cross-device mobile infrastructure."[18]

.

Major Challenges involved in mobile cloud are:

Network Latency

Bandwidth Capacity

The most notable problem is the lack of speedy mobile Internet access everywhere. For

example, 3G coverage is spotty outside urban areas, leading to intermittent connection

issues and slow speeds. Other markets may have it even worse. However, new technologies

like HTML5, which does local caching, could help mobile cloud apps get past those sorts of

issues. And there's even a chance that the browser could one day be replaced - at least in

some markets - with another technology altogether which provides a better way to access

the mobile web[19]

.


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Mobile phones are continuously increasing day by day, so these two issues Network Latency

and Bandwidth capacity should be considered. As mobile network infrastructures

continuously improve, their data transmission becomes increasingly available and

affordable, and thus they are becoming popular clients to consume any Web resources,

especially Web Services (WS). Today, mobile devices like iPhone, Blackberry, Android, have

included applications that consume WS from popular websites, such as Google, Facebook,

and Twitter. However, there are problems in connecting mobile devices to existing WS.

Firstly, WS need to provide optimization for mobile clients. For example, the size of the WS

messages needs to be reduced to fit the bandwidth of mobile clients. Secondly, mobile

clients have to adapt to different kinds of WS, for example, SOAP and JSON WS. The growing

number of mobile clients and availability of WS also drives the needs of customizing and

personalizing service mashes.


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Chapter 3

3. Design of Mobile

Cloud


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3.1. Introduction

In this chapter we are going to design a mobile cloud environment and develop a web

service application using programming languages like JAVA and PHP. Well use android

platform to accomplish this task. Well setup a development platform for android

application and virtual machine performing the tasks of virtual server in the cloud. Further

well design the diagrams that would show how our android application is sending requests

and receiving the response from the server.

3.2. Android Overview

Android is becoming the most popular operating system in the mobile devices now a day.

This is because of its enhanced features that attract the consumers to buy android bases

devices and also it provides an excellent and easy development platform for the developers.

Below there are some of the features of android operating system.

Open Source:

Android is an open source operating systems so any one can use it develop it according to

the needs. It is based on Linux kernel that consists of around 12 million lines of code.

Applications:

There are around 200,000 applications have been developed for this OS which enhanced the

functionality of the mobile devices. A large community of the developers is writing

applications for android. There is is an online applications store named Android Market that

is under the control of Google from where many free and paid applications can be

downloaded from third party sites. Most of the applications are written in Java Language.

Development Platform:

Android provides an efficient development platform using Android SDK that can be

downloaded from android website directly for Windows, Linux and MAC operating systems.

Applications can be written in open source java language by installing its SDK and plugins for

Netbeans/Eclipse.


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Layouts:

Android provides many smart phone and tablets layouts that are adaptable to VGA, 2D and

3D graphics.

Connectivity:

Android supports connectivity technologies that includes GSM/EDGE, IDEN, CDMA, EV-DO,

UMTS, Bluetooth, Wi-Fi , LTE, NFC and WiMAX.

Multitasking:

Provides multitasking through which many applications can be run and execute at the same

time providing an efficient use of resources.

Multi-Touch:

Android supports the feature of multi-touch that was first introduced in HTC Hero.

Tethering:

Android is only the Mobile OS that supports the feature of tethering in which the mobile

device can be used as a Wireless or Wired hotspot through which users can access the

internet on the move.

3.3. Tools and Software requirement

Tools and softwares that we need to install are:

VMware Workstation

VMware Workstation is required to install in our operating system. Well install a separate

virtual server in this VMware and run this virtual server in a cloud through which android

client will interact to process its request.

Eclipse IDE

Install eclipse IDE on windows OS in which well develop and android client in java.


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Android ADT plugin for eclipse

To develop and android application we need to install android ADT plugin for eclipse. Thisplugin is available to download from android website.

Android SDK

Install android SDK tools for windows to run the android emulator for the testing of our

android application. We can test our android application using the emulator so no need to

worry if we dont have the android mobile phone.

UBUNTU LAMP Server

Download UBUNTU LAMP server from Ubuntu official website and install it in VMware

workstation. UBUNTU LAMP server comes with built in packages of LINUX, APACHE, MYSQL

and PHP. We dont need to install these packages in Linux separately if we are using LAMP

server. To install UBUNTU LAMP server refer to[2]

.

3.4. Programming Languages

Programming languages that we should be familiar with to develop android application are:

JAVA

Java is a well known open source object oriented programming language which is widely

being used by most of the developers of the world. In this application JAVA is being used to

develop client side of the webservice.

PHP

PHP is also a well known and widely used open source scripting language. What

distinguishes PHP from something like client-side JavaScript is that the code is executed on

the server, generating HTML which is then sent to the client. The client would receive the

results of running that script, but would not know what the underlying code was.


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We can even configure our web server to process all our HTML files with PHP, and then

there's really no way that users can tell what you have up your sleeve.

3.5. Configuring the platformWe are going to create a web service for the android client. To create this service we have

to install and configure the UBUNTU LAMP server on VMware Workstation. Figure below

shows the configured virtual machine on VMware.

Figure 5 Netbeans Installed on Virtual Machine

The figure above shows that UBUNTU LAMP server has been installed on VMware

Workstation. We have installed NETBeans IDE 6.9 in virtual machine to develop the server

side code in PHP. This PHP Server will listen to the client request and response back the

queries.


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Now setup an eclipse platform for the development of webservice. Below are the steps that

should be followed to setup android platform in Eclipse.

Download android SDK for windows from android website.

Install ADT plugin for Eclipse.

Add android platform component in Android SDK.

Te see more details about installing refer to[3]

Figure 6 Eclipse at the Client Side

Figure above explains the Eclipse development platform in which on the left side is the

project files i.e. JAVA classes, xml files, layouts etc. In the middle is the window in which all

the code has to be written. Right side shows the attributes list. Down is the console window

which shows the information about errors and warnings.


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3.6. Design of webservice Application

In our application we are going to develop a webservice application in which an android

client request the server to process its request in the mobile cloud environment. We used

JSON technique to encode and decode the data.

3.6.1.JavaScript Object Notation (JSON)

Java Script Object Notation (JSON) is a light weight format for data interchange. The major

benefits of JSON technique are:

Very light weight data exchange format.

Easy for humans to read and write.

Easy for systems to parse and generate data.

Supports more than 21 languages.

JSON is built on two structures:

A collection of name/value pairs with unique names (associative array).

An ordered list of values (array).

JSON is used extensively in various AJAX frameworks and toolkits to provide easy objectserialization for remote calls. There is a growing realization that perhaps JSON should be

considered as an option when implementing SOA[4]

.


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3.6.2.Comparison of JSON and XML

JSON XML

Fully automated way of de-serializing/serializing

JavaScript objects, minimum to no coding

Developers have to write JavaScript code to

serialize/de-serialize to/from XML

Very good support by all browsers While XML parsers are built-in into all modern

browsers, cross-browser XML parsing can be

tricky. [5]

Concise format i.e. name/value pair -based

approach

Verbose format because of tags and

namespaces

Fast object de-serialization in Slower de-serialization in JavaScript

Supported by many AJAX toolkits and JavaScript

libraries

Not very well supported by AJAX toolkits

Simple API (available for JavaScript and many other

languages)

More complex APIs

No support for formal grammar definition, hence

interface contracts are hard to communicate and

enforce

XML Schema or DTD can be used to define

grammars

No namespace support, hence poor extensibility Good namespace support, many different

extensibility options in Schema

Limited development tools support Supported by wide array of development and

other (e.g., transformation) tools

Narrow focus, used for RPC only, primarily withJavaScript clients (although one can argue that it's

one of the strengths)

Broad focus - can be used for RPC, EDI,metadata, you name it

Table 1 Comparison of JSON and XML [4]


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3.6.3.Architecture

Figure below explains the architecture of our webservice application.

Cloud NetworkAndroid

Client

Database

PHP

JSON

Encode

Sends the

request in

Cloud Virtual Server

Process the client

request and get data

from database

Forward the request

Get data encoded in

JSON FormatGet Data in

JSON

Format

Figure 7 Architecture of Mobile Cloud

The figure above explains the scenario of using the services in mobile cloud computing. The

android client first sends the request in the cloud using HTTPPOST method and request is

forwarded to the virtual server which has registered its service in the cloud with service

registry. The service has been written in PHP which takes the clients query and finds the

records against the clients query in the database encode it in JSON format and send back to

the client using the cloud. When the data received at the client side it saves the result in

JSON object which can be achieved using the loop and the data would be shown in theclients android mobile.


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3.6.4.Sequence Diagram

Sequence Diagrams basically shows the activities of the processes and events that run

within the system for a specific period of time. Sequence Diagrams are often known as

event diagrams, event Scenarios and timing Diagrams.

Figure 8 Sequence Diagram

The figure above shows the time stamp of each activity from the start to end i.e. when the

client sends the request and server sends the response in terms of web services. We can see

the whole process completed in 10 steps.

Step 1:

At the first step Virtual server connects itself with the cloud by introducing its service in the

cloud.


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Step 2:

The client found the service and sends the request to the specific server that is registeredwith this service in the cloud. In terms of programming the request sent to the server uses

HTTPPOST method.

Step 3:

At step 3 the request is being forwarded to the specific virtual server that is registered with

the service that android client wants to use.

Step 4:

At step four the virtual server receives the request and processes it.

Step 5:

Step 5 shows that the request of the client is being processed by the server and it has been

forwarded to PHP to process the clients query to get the records from database attached to

this server.

Step 6:

At step 6 data is being retrieved from database and forwarded to the PHP.

Step 7:

Step 7 shows that the data retrieved from the database is being encoded by the PHP in JSON

format.

Step 8:

At 8th

step the encoded is sent back in the cloud to send to the client.


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Step 9:

Step 9 ensures that the data has been successfully received in JSON format.

After 9

th

step the when the client receives the encoded data it saves the result in JSONarray. The data is retrieved from JSON array using loop and that data will be shown in the

clients mobile device.

3.7. Working of Webservice Application

We are now going to show the demo of this mobile cloud architecture using a simple

application we developed in java and developed a simple PHP code to process the clients

request.

Figure 9 Softwares architecture at Server and Client

The figure above explains the platforms, tools/software and languages we used in this


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application. All the working has been done on a single PC using the virtualization software

VMWARE Workstation. We installed UBUNTU LAMP server in the VMWARE and further in

UBUNTU we installed Netbeans IDE tool to develop a PHP application at the server side. At

the client side we have windows 7 platform and installed Eclipse IDE and android SDK for

the development of android application and we used JAVA + XML languages to develop

android application. We installed android SDK for the real time testing of android

application in the android emulator.

For the testing purpose of our webservice we used sample database WORLD that is easily

available on the internet to download.

Figure 10 Demo Application 1

The figure above shows the graphical user interface for the android application. When we

press the Submit button after entering the name of country in the textbox which is showing

Enter Name of Country. Then the application sends the request in the cloud to the virtual

machine. The virtual server will process the clients request finds the capital of that country


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in the database and sends the result back to the client in JSON format. After the client

receives the data is retrieved and showed it in the textbox below the submit button. As a

demo the result is shown in the figure below.

Figure 11 Demo Application 2

The application above shows the successful working of our JSON webservice that we

developed in which user enters the country name and press the submit button. Then the

request is sent to the PHP server and there the PHP process the user request and get the

results from the database. When it gets then results i.e. Capital city of that country from the

database it encodes the data into JSON format and sent back to the cloud. The client

receives that data in JSON encoding and the results are shown into the mobile device of the

client.


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Chapter 4

4. Performance

Optimization


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4.1. Introduction

In this chapter we analyze our design that we developed in Chapter 3 and discuss the

possible challenges and issues that could occur in our current design and try to find the

possible solutions to these challenges. Well discuss and implement a well known

performance optimization technique PARTICLE SWARM OPRIMIZATION and check the

performance of single virtual machine in the cloud and compare it with 5 parallel running

virtual machines and check the performance. Then we would analyze and conclude results

using the performance parameters in both scenarios. In short we are going to enhance our

current design using particle swarm algorithm. We would perform load balancing based on

number of parallel virtual machine running at the same time.

4.2. Particle Swarm Optimization

Particle swarm optimization is a population based stochastic optimization technique

developed by Dr. Eberhart and Dr. Kennedy in 1995. They developed this technique inspired

by the social behaviour of animals such as a flock of birds finding a food source or a school

of fish protecting them from a predator. A particle in PSO is analogous to a bird or fish flying

through a search (problem) space[20]

. The elements those are important to note in this

scenario.

1. One is the particle that refers to the bird which is the solution.

2. Each particle has a fitness value which is calculated by a fitness function to be

optimized.

PSO get initialized with a group of random particles in space and then searches for the

optimization by updating generations. For each iteration particle is updated by two best

values. The first one is calculated by the fitness value that a particle achieved and this value

is calledpbestand the other value achieved by the particle itself in the space and it is called

global best value gbest.


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After finding these two values particle updates its velocity and position by the following

equation.

V = V+ c1*randno. * (pbest-present) + c2*randno.* (gbest-present);

present = present+v;

V denotes the particle velocity, present is current particle, c1 and c2 are learning factors

usually c1=c2=2 and randno. are the random numbers to be generated between 0 and 1.

The steps involved in particle swarm optimization are:

Select the search space and initialize the particle.

For each particle calculate its fitness value, if fitness value is better than the previous

fitness value set the current value as the new pbest.

Choose the particle with the best fitness value of all the particles and set it as the

gbest.

Calculate the particle velocity and equation for each particle.


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Figure 12 Particle Swarm Flow Chart

The Figure above shows the flow diagram for particle swarm algorithm step by step and we

can implement it in our program using the flow diagram above.


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4.3. Performance of Single Virtual Machine based on bandwidth

Suppose the client requests the server to query 6MB of data. Total payload and header per

packet would be 256 and 8 bytes. Bandwidth is 2 Mb. Then,

JIJI I

I# {-% % J

I {. -% F % J

I I# -I - J

Now well take 6 more samples of data to be queried by the server i.e. 7MB, 8MB, 9MB,

10MB and 11MB. The link bandwidth and payload would remain the same.

Amount of data (MB) Time (Seconds)

6MB 27.03

7MB 31.53

8MB 36.04

9MB 40.55

10MB 45.05

11MB 49.06

Table 2 Delays for a Single Virtual Machine

In the table above, the total delay is calculated for a single virtual machine for six sample

amount of data having link bandwidth of 2 Mb and. Payload and header size would be 256

bytes and 8 bytes.


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Graph 1:

The graph above shows the delays regarding the packet size to be sent to the server.

Vertical axis shows the time units in seconds and horizontal axis shows amount of data in

MB. The graph clearly explains that by increasing the amount of data results in the increase

in delays. For 6MB the total delay is 27.03 seconds and after that when amount of data is

increased delay is increased and at 11MB it has reached to almost 50 seconds.

4.4. Challenges and Issues in the current design

In the above scenario explained in Chapter 3 we had only one single virtual machine running

that handles the requests from the android client. The major challenges that could be faced

in that design are

1. Scalability

2. Availability

3. Reliability

0

10

20

30

40

50

60

6MB 7MB 8MB 9MB 10MB 11MB

Time (Seconds)

Time (Seconds)


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4.4.1. Scalability

The major challenge that we would face in that design is scalability. Scalability refers to how

well a hardware or software system can adapt to increased demands. For example, a

scalable network system would be one that can start with just a few nodes but can easily

expand to thousands of nodes. Scalability can be a very important feature because it means

that we can invest in a system with confidence we won't outgrow it. In our current design

we describe it with only one single user but suppose when more and more clients join the

cloud and the number counts to 100s or 1000s clients that send the request to the server.

The following question will arise:

Would that server be able to handle that amount request?

Would that system is capable to increase total throughput under an increased load

when resources (typically hardware) are added?

The answer is No because when more and more clients are added then at one stage the

servers capacity of handling the requests would slow down and hence can increase the

chances of server failure.

4.4.2. AvailabilityThe next issue is availability. When we are running a single server then we should make sure

that it should be available at all times for the nodes to send back the response and handling

the queries. Suppose if our server gets fail or it has got some problem in its hardware which

has stopped the server to run then how we are going to cope with this situation? Then

solution is to run a backup server but still there would be scalability issues.

4.4.3. Reliability

Reliability refers to as ability of a component/system to function correctly over a specified

period of time, mostly under pre-defined conditions. Fault tolerance is defined as the ability

of the system to continue operation in the event of a failure. Fault tolerance means that a

computer system or component is designed such that, in case a component fails, a backup

component or backup procedure can immediately take its place with no loss of

functionality. Reliability can be improved through fault tolerance[21]

. Reliability can be

achieved by redundancy of the software or hardware components that run as the backup if

any system functionality fails.


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4.5. Proposed Work

4.5.1.Load Balancing based on 5 Virtual Machines

We discussed our issues and challenges in the previous topic now we are going to find the

possible solutions. We added five Virtual Machines with different capacities in the design

that will run parallel and handle the clients requests and process them based on their

capacities i.e. number of processors. Particle Swarm optimization technique is being used in

this case.

4.5.2. Handling Scalability

Suppose five virtual machines are VM1, VM2, VM3, VM4, and VM5. The processors assigned

to each virtual machine and the initial position of virtual machine is shown in the table

below

Virtual Machine Position Number of Processors

VM1 1 2

VM2 2 4

VM3 3 6

VM4 4 8

VM5 5 10

Table 3: Number of Processors assigned to each virtual machine

JIJJJ - - - % - JIJIHI

Now suppose the client request to process 1000 records from database.

Solution 1

One solution could be that we divide the task equally to all virtual machines and they will

process the data for the client i.e. 1000/5 = 200 records per virtual machine but that

scenario is not the best solution because the virtual machine which has the less capacity

would have to process more than its original capacity thus will cause delay in the processing.

So this solution would not be considered as an optimize solution. To overcome this situation

we would consider the second solution explained below in which parallel running virtual

machines will process the clients tasks based on their capacities.


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Solution 2

The other one we can do is that the virtual machines will process the clients request based

on their capacity. We calculate the capacity of each virtual machine. To find the capacity in

percentage well use the formula.

VXY WYY (1)So for VM1:

II# For VM2:

II$ For VM3:

II For VM4:

II& % For VM5:

II' From the above percentage for each virtual machine capacity it clearly states that virtual

machine VM1 will process 6% of total task, VM2 will process 12%, VM3 will process 20%,

VM4 will process 27% and VM5 will process 34% of the total task.


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Virtual Machine Position Number of Processors Capacity

VM5 1 10 34%

VM4 2 8 27%

VM3 3 6 20%

VM2 4 4 13%

VM1 5 2 6%

Table 4: Position changed based on Capacity

In the table above we can see the capacity of each virtual machine according to the number

of processors assigned to them. Virtual machine VM5 has changed its position to position 1

because it is counted as best above all the virtual machines. It is sorted in descending order

Suppose if any virtual machine is modified i.e. if we increase the processors in VM3 to 15

then after VM3 would come at the top and.

Graph 2: Capacity of each virtual machine

The graph above explains the capacity of each virtual machine separately. VM5 has the

highest capacity to process the clients tasks.

0%

5%

10%

15%

20%

25%

30%

35%

40%

VM5 VM4 VM3 VM2 VM1

Capacity of Each VM

Capacity of Each VM


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4.6. Experimental Work

Well use an example and see how the clients tasks are being processed by 5 virtual

machines running parallel in the cloud by using the solution 2 explained above.

Figure 13 Clients connected to the cloud

The figure above explains how different clients are connected with the cloud and using the

servers (Virtual Machine) resources in that cloud. All Server are connected to one database

and processing the clients tasks in parallel based on their capacities

Suppose a mobile client wants to get 1000 records from a huge database. The formula to

calculate the task processing for each virtual machine would be

YY XYYYYY (2)


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Virtual Machine Subdivided Task (records)

VM1 60 records

VM2 130 records

VM3 200 records

VM4 270 records

VM5 340 records

Table 5

Table above are the records calculated to be processed by each virtual machine. We divided

this task into five subdivided tasks as explained in the figure below and each sub divided

task would be assigned to the virtual machine that matches its capacity

Figure 14 Tasks Assigned to each virtual machine


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4.6.1.Applying PSO Algorithm

One of the key issues in designing a successful PSO algorithm is the representation step

which aims at finding an appropriate mapping between problem solution and PSO particle

[25]. We have 5 tasks assigned to 5 virtual machines having different no of processors. Well

use Novel Bases optimization technique[24]

.

Particles Positions:

In this method solutions are encodes as m x n which is called position matrix where m

denotes the number of virtual machines at the time of scheduling and n is the number of

tasks. The position matrix of each particle has two properties:

All the elements of the matrices have either the value of 0 or 1. In other words, ifXk

is the position matrix ofkth particles, then:

{X X X (3) In each column of these matrices only one element is 1 and others are 0.

In position matrix each column represents a task allocation and each row represents

allocated tasks in a virtual machine. In each column it is determined that a task should be

performed by which virtual machine.

Task 1 Task 2 Task 3 Task 4 Task 5

VM1 1 0 0 0 0

VM2 0 1 0 0 0

VM3 0 0 1 0 0

VM4 0 0 0 1 0

VM5 0 0 0 0 1

Table 6 Position Matrix

According to our scenario we can see task 1 is performed by VM1, and it will not perform

others tasks. Similar is the case with other virtual machines as table above clearly explains

which task is performed by which virtual machine if it is 1 then the virtual machine will

execute that task and if it is 0 then that task would not be executed on that virtual machine.


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Particles Velocity:

To find the velocity of the particle lets consider the following symbols that represent the

properties of the particles.

Xi= Current Position of the particle.

Vi= Current Velocity of the particle.

Pbestis the personal best position of the particle.

Gbestis the global best position of the particle.

tis the current iteration.

r1, r2 are randomly generated for every velocity update and 0 r1, r2 1. They

should both be different each iteration.

c1, c2 are user defined values are acceleration coefficients where 0 c1, c2 2.

Their value depends on the problem to be optimized.

wis the inertia weight

Particle velocity is considered as a m x n matrix in which elements are in range of

[-Vmax, Vmax]. IfVkis the velocity matrix ofkth particle,

{X . {X X {

Updating Particles Velocity:

Equation 5 below is used for updating particles velocity and Equation 10 is used for updating

position matrix for each particle.

{#{ { - # #{ . { - $ $ { . { {


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{#{ {#{ I {#{ {

{#

{ J{

In the Equation 5 above { represents the element in ith row andjth column ofkthvelocity matrix in iteration t and{ represents the element in ith row andjth columnofkth position matrix in iteration t. Equations 6.1 and 6.2 shows that the position matrix

value would be 1 if the velocity matrix has the maximum value in its corresponding column

otherwise it would be 0.

Fitness Value Evaluation:

According to[24]

makespan and flowtime are used to evaluate the performance of scheduler

simultaneously. Because makespan and flowtime values are in incomparable ranges and the

flowtime has a higher magnitude order over the makespan, the value of mean flowtime,

flowtime / m, is used to evaluate flowtime where m is the number of available nodes. The

Fitness value of each solution can be estimated using (7).

I II - { . I {

In the equation 7 above is used to regulate the effectiveness of parameters used in thisequation. The greater the, more attention is paid by the scheduler in minimizing makespan

and vice versa. The smaller makespan and flowtime in (7), the greater fitness value, and

hence a better solution it is regarded[24]

.


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Pseudo Code:

Now from the observations and equations above generated pseudo code of PSO algorithm

is described below

For each particle

Initialize particles with m xn dimensions

Do

For eachparticle i =1, 2, 3..... P

//Calculate Fitness Value

If f(Xi) > f(pbesti)

pbesti= Xi;

End

//Choose the particle with the best fitness value as global best

If f(pbesti) >f(gbesti)

gbesti=pbesti;

End

End

//Calculate Particle Velocity and update its position

For eachparticle i =1, 2, 3..... P

Update the velocity matrix using (5)

Update the position matrix using (6)

End

While maximum iterations or until stopping condition is true;

The pseudo code above shows the structure of the algorithm. We can follow this pseudo

code to develop PSO application.


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At the initial steps first we have to generate the swarm of particles that would have m x n

dimensions where m shows the number of virtual machines and n shows the number of

tasks. Then calculate the fitness values which would be based on markspan and flowtime by

using equation 7 for each particle in the swarm and choose the best local best i.e. pbest

virtual machine. Then the best particle judged is set as global best. If in the iteration the

particle assigned locally the best i.e pbest has higher fitness value than the global best

(gbest) then the pbest is assigned to gbest and that would be the global best particle. After

it is done particle swarm algorithm updates the velocity matrix and the position matrix until

the loop iterates.


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Chapter 5

5. Conclusion


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5.1. Conclusion

In this project we conclude that sharing the resources among other virtual machines in a

mobile could result in fast processing of the requests sent by the clients. Furthermore the

issues we faced in topic 4.4 could be reduced down and ensures high scalability, availability

and reliability of virtual machines in a cloud by dividing the task among virtual server

running in the cloud. If there are more virtual servers in a cloud then the chances of failure

becomes less and high availability could be achieved. The cloud would be fast and secure.

For example by using the topology above if any virtual machine in the cloud fails then the

task would be divided into the other virtual machines that are running.

If we compare a cloud with five virtual machines and a cloud with single virtual machine

then the cloud with 5 virtual machines will definitely perform better because all the

resources of 5 virtual machines are shared among themselves and the chances of failure are

reduced and the cloud would be more scalable. Furthermore if we apply Particle Swarm

Optimization technique to among the server then it would become more efficient and

optimized because every job would be scheduled according to the servers capacity and

time the server achieved to process the jobs i.e. makespan and flowtime.

5.2. Future Work

In this dissertation we optimized our mobile cloud based on server capacity and time span.

But we can consider more techniques and algorithms like bandwidth optimization, server

storage, network latency etc to enhance the performance of our cloud. We discussed only

Particle Swarm Algorithm and we optimized the cloud based on its capacity i.e. no of

processors to handle the requests. Mobile cloud is a vast concept and load balancing plays

very important to make every type of cloud efficient. However in case of mobile cloud

computing two more issues are need to be considered that is Bandwidth Capacity and

Network Latency. More and more development is being done in this field. But although

network is unpredictable so we have to consider many other parameters as well to make

the network optimized and efficient.


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[3] Installing Android SDK

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[5] Call SOAP Web services with Ajax, Part 1: Build the Web services client

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[9] Understanding the Layers within Cloud Computing

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[10] Grid and Cloud Computing

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[11]Cloud Computing System and Its Layers

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[12] Web service Suitability Assessment for Cloud Computing

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[14] Mark Walsh. Gartner: Mobile To Outpace Desktop Web

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By Dijiang Huang Arizona State University, Arizona, USA


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[16] Mobile, cloud computing to dominate software apps, IT deliveries in next 5 yrs,

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[18] Overcoming mobile cloud computing challenges with distributed apps

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[19] Why Cloud Computing is the Future of Mobile

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[20] A Particle Swarm Optimization-based Heuristic for Scheduling Workflow

Applications in Cloud Computing Environments

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[21] Hardware Reliability

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Zhang

[22] Validity of the single processor approach to achieving large scale computing

capabilities

By Gene M Amdahl, IBM Sunnyvale California

[23] Characterizing Network Processing Delay

By Ramaswamy, Ning Weng and Tilman Wolf

[24] A Novel Particle Swarm Optimization Approach for Grid Job Scheduling

By Hesam Izakian, Behrouz Tork Ladani, Kamran Zamanifar, Ajith Abraham

[25]. Green Cloud on the Horizon

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[25] Non Dominated Particle Swarm Optimization For Scheduling Independent Tasks

On Heterogeneous Distributed Environments

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[26] Scheduling Jobs on Computational Grids Using Fuzzy Particle Swarm Algorithm

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[27] Black Box Optimization Benchmark (BBOB) applied to classical optimization

algorithms: PSO, DE-PSO and CMA.

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resource optimization in mobile cloud environments

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