QoS-Aware Middleware for Optimal Service Allocation in

Mobile Cloud Computing

Reza Rahimi,SCHOOL OF INFORMATION AND COMPUTER

SCIENCE, University of California,

Irvine, CA.

2

Prologue

Mobile Music: 52.5%Mobile Video:25.2%Mobile Gaming: 19.3%

Sensory Based Applications

Augmented Reality

Mobile Social Networks and Crowdsourcing

Multimedia and Data Streaming

Location Based Services (LBS)

Next Generation of Mobile Apps

M. Reza Rahimi, Jian Ren, Chi Harold Liu, Athanasios V. Vasilakos, and Nalini Venkatasubramanian, "Mobile Cloud Computing: A Survey, State of Art and Future Directions", in ACM/Springer Mobile Application and Networks (MONET), Speciall Issue on Mobile Cloud Computing, Nov. 2013.

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• Cloud computing is a style of computing where massively scalable and elastic IT-related capabilities are provided “as a service” to external customers using Internet technologies.

• Mobile cloud computing simply refers to an infrastructure where both the data storage and the data processing could happen outside of the mobile device mainly on cloud. Mobile Cloud Computing

Mobile Computing

Cloud Computing

Research Objectives (Big Picture)

4

Context as a Service:

ex: Mobility patterns, Service Usage in different

location and time, Group-Aware, Social

Context, …

Network as a Service:

ex: Wireless connectivity

(Wi-Fi, 3G/4G, Bluetooth,…)

Computation /Storage as a

Service:ex: computation,

Storage, Platform,..

Optimal service allocation based on mobile users or providers

criteria

5

Related Work

Framework Description

TheoryISLPED2012:

Analytical framework based on game theory for energy saving. WiFi connection.

TheoryInfoCom 2012:

Analytical framework based on convex optimization for reducing energy and execution time.

CloneCloudEurosys2011:

Objective: Energy saving, Reduction in execution time , Virtualization Framework using Wi-Fi and 3G-Not scalable due to cloning, they only considered local cloud, Mobility issue on performance.

MAUIMobiSys2010:

Objective: Energy saving, Reduction in execution time, Virtualization framework using Wi-Fi and 3G

MobiCloudSOSE2010:

Objective: Energy saving and price, Virtualization Framework using Wi-Fi and 3G

CloudletPerCom2009:

Objective: Reduction in execution time, Virtualization framework using Wi-Fi

Framework Description

CuckooMobiCase 2010:

Objective: Energy saving, Reduction in execution time , Client/Server Wi-Fi ,3G and Bluetooth

Calling The CloudMiddleware 2009:

Objective: Reduction in execution time, code size and proxy cost Client/Server Framework using Wi-Fi and Bluetooth-any scalability studies, energy issues, public and local cloud modeling, mobility affect on performance.

ChromaMobiSys2003:

Objective: Reduction in execution time Client-Server using Wi-Fi.

• Mobility issues,• Different Cloud Types

(public/local), • Public Cloud Important criteria like

price,• Scalability Study.

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Research Contributions:

• 2-Tier Cloud architecture as the cloud computing platform.

• Location-time workflow as the modeling framework for mobile applications in mobile cloud computing.

• Different heuristics to solve optimal service allocation in mobile cloud computing.

• MAPCloud as a QoS-middleware for service allocation in mobile cloud computing.

• Scalable version of service allocation algorithm in mobile cloud computing.

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2-Tier Cloud Architecture

8

A style of computing where massively scalable and elastic IT-related capabilities are provided “as a service” to external customers using Internet technologies (Computing as a Utility).

What is Cloud Computing?

3 different services could be considered as:Software as a Service (SaaS):

Platform as a Service (PaaS):

Infrastructure as a Service (IaaS):

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• First Approach: Connect to Public Cloud for resource intensive tasks!• Long WAN delay [Satyanarayanan_2011] , [ Cavilla_2007] :

• unlikely to be improved while the prime target of WAN improvement is security, management.

• There are two different approaches to use remote resources for mobile applications:

• Second Approach: Connect to Local Clouds (Local proxies, Cloudlets) in proximity of the users for resource intensive tasks, [Clone Cloud], [MAUI], [PARM].

• LAN delay is always order of magnitude better that WAN delay [Satyanarayanan_2011] .

• Near user resources could not scale up well.[Satyanarayanan_2011] Mahadev Satyanarayanan, “Mobile Computing: The Next Decade”, in  SIGMOBILE Mobile 2011.[ Cavilla_2007] Lagar-Cavilla and et al. “ Interactive Resource-Intensive Applications Made Easy”, In Proceedings MIDDLEWARE2007.[Clone Cloud] Byung-Gon Chun and et al. " CloneCloud: Elastic Execution between Mobile Device and Cloud", EuroSys 2011.[MAUI] E. Cuervo, A. Balasubramanian and et al. " MAUI: Making Smartphones Last Longer with Code Offload",MobiSys 2010.[PARM] S. Mohapatra and et al. ”Power-Aware Middleware for Mobile Applications”, Chapter 10 of the Handbook of Energy-Aware and Green Computing, Chapman Hall/CRC, 2011.

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M. Reza. Rahimi, N. Venkatasubramania "Cloud Based Framework for Rich Content Mobile Applications", poster in the IEEE/ACM CCGrid 2011.M. Satyanarayanan, P. Bahl, R. Cáceres, N. Davies " The Case for VM-Based Cloudlets in Mobile Computing", PerCom 2009.

Tier 2: Local Cloud

(+) Low Delay, Low Power,

Almost Free (-) Not Scalable and

Elastic

Tier 1: Public Cloud (+) Scalable and Elastic

(-) Price, Delay

Wi-Fi Access Point

3G Access Point

RTT: ~290ms

RTT: ~80ms

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• Due to different characteristics of local and public cloud services, service allocation for mobile users/group of users on this 2-Tier cloud is a hard task (we will show it is NP-Hard!).

• In this research we investigate how to optimally assign services for mobile user/ group of users on this 2-Tier cloud architecture considering power consumed on mobile device, delay that user experienced and price as the main criteria for optimization.

• We need a formal framework to model mobile users, different cloud services, mobile applications an their QoS.

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• Service Oriented Computing (SOC) provides strong formal framework for defining the concepts of Services, Workflow, QoS for generic applications.

• We will use and extend these concepts to mobile cloud computing in the next section.

• More precisely we will:• Formally define cloud and service concepts,• Mobile users and its related properties,• Mobile group concept to define group-ware

application.

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Mathematical Formulation of the Service Allocation

Problem in MCC

14

Clouds, Services, and Location • Cloud Service Set is the set of all supporting

services on Cloud (usually implemented as a web service ):

• User Service Set is the set of all supporting services of the user (computation, storage, transcoding, …):

,…, }• Local Cloud Capacity: It is defined as the

maximum number of mobile clients that could be served using local cloud ().

• Location in space is defined as:L

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• Trajectory is defined as:

• Center of Mobility: is the location where (or near where) a mobile user spends most of its time near it. It is calculated as follows:

l1

l2

l3

ln

Single Mobile User Properties

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• Mobile Group: It is defined as a group of users which have the shared services for example in storage sharing and mobile social applications. It is presented by in which is the power set (the set that contains all subset of Mobile users). We could define it formally as:

• Center of Mobility : is the location that a mobile group users belong to spends most of their time in or near it. It could be formally defined as:

Mobile Group Properties

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Workflow

• It consists of number of logical and precise steps known as a function (for application modeling).

• Functions could be composed together in different patterns [Mabrouk_2009] , [Zheng_2004] :

F1 F2 F3

F1

F2

F4

F3

F1

F1

F2

F4

F3

SEQ LOOP

AND: CONCURRENT FUNCTIONS XOR: CONDITIONAL FUNCTIONS

k

1

1

P1

P2

𝑷𝟏+𝑷𝟐=𝟏 ,𝑷𝟏 ,𝑷𝟐 ∈{𝟎 ,𝟏}

N. B. Mabrouk, S. Beauche, E. Kuznetsova, N. Georgantas, and V. Issarny " QoS-aware Service Composition in Dynamic Service Oriented Environments", In Middleware 2009.L. Zeng, B. Benatallah, A. H. NGU, M. Dumas, J. Kalagnanam, and H. Chang "QoS-Aware Middleware for Web Services Composition ", In IEEE Trans. Software. Eng., 2004.

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Workflow (Cont.)• Typical workflow (showed by ):

• Each in workflow could be realized and implemented by some services.

• We define as:

• Feasible solution or execution plan for a workflow W consists of n Functions is defined as:

F1

F2

F4

F3

F5

F7

F8

F61

1

P1

P2

3

Start End

Location-Time Workflow (LTW)

• Intuitively it is composed of user requested workflow in location and time.

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t1 t2 t4t3 tN

l2

l1

l3

ln

W1

Wk+1

Wk

Wj+1

Wj

Location-Time Workflow

• It could be formally defined as:,….,

M. Reza. Rahimi, N. Venkatasubramania "Exploiting an Elastic 2-Tiered Cloud Architecture for Rich Mobile Applications“, Poster in the IEEE/ACM WoWMoM 2012

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Quality of Service (QoS)

power consumed on cellphone when he is in l using.

price of executing for when he is in l

delay of executing for when he is in l.

• The QoS could be defined in two different Levels: • Atomic service level • Composite service level or workflow level.

• Atomic service level could be defined as:

M. Reza. Rahimi, Nalini Venkatasubramanian, Athanasios Vasilakos, "MuSIC: On Mobility-Aware Optimal Service Allocation in Mobile Cloud Computing", In the IEEE Cloud 2013.M. Reza. Rahimi, Nalini Venkatasubramanian, Sharad Mehrotra and Athanasios Vasilakos, "MapCloud: Mobile Applications on an Elastic and Scalable 2-Tier Cloud Architecture", In the 5th IEEE/ACM International Conference on Utility and Cloud Computing , USA, Nov 2012.

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QoS (Cont.)

• The workflow QoS is based on different patterns.Qos SEQ AND XOR LOOP

• LTW QoS:),.…,

),.…,

),.…,

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• As it can be understood different QoSes have different dimensions (Price->$, power->joule, delay->s)

• We need the normalization process to make them comparable.

• It could be defined in different levels: Service, Workflow.

• Services, Max and Min Services (example):

The maximum power consumed on cellphone when he is in region considering all services which implements Function.

The minimum power consumed on cellphone when he is in region considering all services which implements function.

Normalization

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Normalization (Cont.)• The higher Normalized Power/Price/Delay are the

better services are (low power/price/delay).• The same procedure could be used to define the

normalized workflow as:The maximum power consumed on cellphone considering all feasible plans for workflow W.

The minimum power consumed on cellphone considering all feasible plans for workflow W.

‖𝑾 (𝒖𝒌)𝒑𝒐𝒘𝒆𝒓‖≝¿‖[𝑾 (𝒖𝒌)𝜯

𝑳 ]𝒑𝒐𝒘𝒆𝒓‖≝¿

24

Summary

• We define location-time workflow for modeling mobile applications in pervasive environment.

• We define QoS for LTW and how to do the normalization.

• The normalized power, price and delay is the real number in interval [0,1].

• The higher the normalized QoS the better the execution plan is.

• We need to answer the following two questions:– For single mobile users: Knowing the Mobile user

LTW; what is the optimal service allocation considering price, power and delay?

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– For mobile groups what is the optimal service allocation when they have shared services (such a shared storage) considering price, power and delay?

• These questions have missing parts which are Utility Functions.

• Many has been defined in the operational research literature, we use the Fairness Utility for our problem.

26

Optimal Service Allocation for Single

Mobile User

• In this optimization problem our goal is to maximize the minimum saving of power, price and delay of the mobile applications.

27

𝑭𝒂𝒊𝒓𝒏𝒆𝒔𝒔 𝑈𝑡𝑖𝑙𝑖𝑡𝑦

28

Optimal Service Allocation for Mobile

Group

29

𝑮𝒓𝒐𝒖𝒑 𝑭𝒂𝒊𝒓𝒏𝒆𝒔𝒔𝑈𝑡𝑖𝑙𝑖𝑡𝑦

•Both these optimization problems are NP-Hard (Knapsack is the special case) so we look for heuristic to solve this problem.

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Mobility-Aware Service Allocation Algorithms

on Cloud

31

Service Allocation Algorithms for Single Mobile User and

Mobile Group-Ware Applications

Brute-Force Search (BFS)

Simulated Annealing Based

Genetic Based

Greedy Based

Random Service Allocation (RSA)

• We start with our main one, which we call it MuSIC: Mobility Aware Service AllocatIon on Cloud.

• Its core is based-on simulated annealing approach.

32

Simulated Annealing

Core

MuSIC: Simulated Annealing-Based Algorithm:

Utility0=InitialValue;It=0;Compute

While(It<MaxIt):CandidateService= FindService(Utility1=Compute(CandidateService)Δ=Utility1-Utility0

If Δ > 0Utility0=Utility1

𝒎𝒊𝒏{‖[𝑾 (𝒖𝒌)𝜯𝑳 ]𝒑𝒐𝒘𝒆𝒓‖,‖[𝑾 (𝒖𝒌)𝑻

𝑳 ]𝒑𝒓𝒊𝒄𝒆‖,‖[𝑾 (𝒖𝒌)𝑻

𝑳 ]𝒅𝒆𝒍𝒂𝒚‖ }Else if ()

Utility0=Utility1

It++EndWhile

Return CandidateService,Utility1

33

Find Service (Cont.)

34

Genetic Algorithm Based Approach

• Choose initial population (usually random)• Repeat (until terminated)

– Evaluate each individual's fitness– Prune population (typically all; if not, then the

worst)– Select pairs to mate from best-ranked individuals

(Ranked, Roulette Wheel)– Replenish population (using selected pairs)

• Apply crossover operator• Apply mutation operator

– Add/Replace generated member to population– Check for termination criteria

• Loop, if not terminating

Constraint Satisfaction

𝒎𝒊𝒏{‖[𝑾 (𝒖𝒌)𝜯𝑳 ]𝒑𝒐𝒘𝒆𝒓‖,‖[𝑾 (𝒖𝒌)𝑻

𝑳 ]𝒑𝒓𝒊𝒄𝒆‖,‖[𝑾 (𝒖𝒌)𝑻

𝑳 ]𝒅𝒆𝒍𝒂𝒚‖ }

Genetic Algorithm

Core

35

Greedy-Based Service Allocation

• For each user/mobile user, find the best (Highest Total Normalized QoS ) required service.

• Total Normalized QoS could be defined as:

M. Reza. Rahimi, Nalini Venkatasubramanian, Sharad Mehrotra and Athanasios Vasilakos, "On Optimal and Fair Service Allocation in Mobile Cloud Computing", submitted to IEEE Trans. on Mobile Computing, 2013

36

MapCloud Middleware

37

• MapCloud Middleware is the QoS-Aware service allocation in Mobile Cloud Computing.

• We Implement MapCloud v1.0 prototype as a web application using Grails/Groovy framework (SOC framework based on JAVA).

• We used Amazon Web Services as the cloud framework.

More information could be found at: http://www.youtube.com/watch?v=yEmQug0pomE&feature=youtu.be

38

QoS-Aware Service DB

Mobile User Log DB

Admission Control and Scheduler

Cloud Service Registry

Mobile Client

MA

PC

lou

d W

eb S

ervi

ce I

nte

rfac

e

MAPCloud Middleware

MAPCloud Runtime

Local and Public

Cloud Pool

MAPCloud LTW Engine and

Analytics

MAPCloud Web Service Interface

MapCloud Middleware Architecture

Mobile User

Logger DB and QoS Analyzer

Location-Time

Analytics

QoS-Aware Service

Scheduler

2-Tier QoS-Aware Cloud Registry

2-Tier Cloud Service

Pool

MapCloud Middleware Sequence Diagram

Save User service pattern As location-Time workflow .

Recommended Web Services

with their URL.

User Web ServiceUsage Log with Experienced QoS .

Run MuSIC/Genetic Greedy/RSA or Use previous Result on previous collected data from mobile users to find best service allocation.

It analyzes user experienced QoS and updates cloud registry

User Logs like:Web Service Usage, Experienced QoS like:delay, power consumption

MapCloud Snapshots

41

Experimental and Simulation Results

Mobile Applications (Case Studies)

42

OCR+ Speech(OCRS):

Video Augmented Reality (VAR):You Tube Link

Multimedia File Sharing (MFS):

Mobile Apps

Processing Storage Bandwidth Group-Aware/Sharing

OCRS × ×

VAR × × ×

MFC × × × ×

43

Mobile Applications (Case Studies)

Averaged Delay (in ms) and power consumption (in mjole) of differentwireless network types regarding to data size when using local cloud

( Fig. aand b) and Amazon Public Cloud (Fig. c and d).

Local Cloud

Public Cloud

44

Simulation Setup

Amazon EC2,S3

Local Cloud 1

Local Cloud 5

Local Cloud 2

Local Cloud 7

Local Cloud 4

Local Cloud n

S1...Sn

S1...Sn

S1...Sn

S1...Sn

S1...Sn

large instance:

equivalent to a PC with 7.5GB of memory, 850 GB of storage

Local Cloud: 64bit Windows

dual-core server,with 8GB of

memory and 500GB of

storage.

LAN Speed

Profiling sample applications has been used for tune the system Environment.

Java Network simulator (JNS) used for modeling the delay between Local clouds.

RWP and Manhattan mobility models are used as the mobility models (V[0/ms-10/ms]).

The delay modeling used in MapCloud for large system simulation.

45

• Simulation used to evaluate the performance of the proposed algorithms.

• Brute-Force Search was used to find the optimal solution.

• Optimality Metrics:Single User:

Group of Users:MuSIC-

• Genetic (Genetic Algorithm ), RSA (Random Service Allocation) and Greedy approach are also tested on System:

Simulation Setup

46

Simulation Results

MuSIC, Genetic, Greedy, RSA and G-MuSIC (5-Groups) algorithms average throughputwith uncertainty in the range of [0%,30%]

47MuSIC and G-MuSIC algorithm real averaged values for delay and

power consumption

Simulation Results(Cont.)

Simulation Results(Cont.)

• Local Cloud+Public Cloud: • How could we measure the performance of 2-

Tiered Cloud Architecture? • What are the reasonable metrics?

48

Local Cloud+Public Cloud

Public Cloud

Same Delay

Local Cloud+Public Cloud

Public Cloud

Same Price

Local Cloud+Public Cloud

Public Cloud

Same Power

Simulation Results(Cont.)

49

2-Tier Cloud Performance Results: Single User (100 mobile users)

2-Tier Cloud Architecture

Performance

MuSIC [0%-30%]

Uncertainty

Genetic[0%-30%]

Uncertainty

Greedy [0%-30%]

Uncertainty

RSA [0%-30%]

Uncertainty

Constant Delay

Price 27% 17% 18% 10%Power

2% 6% 5% 3%

Constant Power

Price 22% 16% 14% 13%Delay 4% 1% 2% 2%

Constant Price

Power

17% 8% 10% 7%

Delay 15% 13% 12% 10%

Simulation Results(Cont.)

50

2-Tier Cloud Architecture

Performance

G-MuSIC [0%-30%] Uncertain

ty

G-Genetic[0%-30%]Uncertain

ty

G-Greedy [0%-30%] Uncertain

ty

G-RSA [0%-30%]

Uncertainty

Constant Delay

Price 20% 15% 13% 9%Power 3% 4% 4% 2%

Constant Power

Price 15% 10% 11% 10%Delay 4% 4% 2% 3%

Constant Price

Power 10% 9% 10% 8%Delay 10% 11% 8% 8%

2-Tier Cloud Performance Results: Group of Users (5 groups, average group size ~20)

51

Scalability

52

• Motivation: suppose that in average each mobile user need 10 different functionalities.– For each functionality we have 20

different services (provided by different vendors)

– Question 1: what are the different combinations?

• Best Answer (combination) = • Worst Answer (permutation) =

– Question 2: What are the different combinations for 1000,000 mobile users?

• Best Answer = ~3 Tera of Feasible Solutions

53

• Worst Answer=

• So the mentioned service allocation problems could have very large search space.

• Need to make MuSIC scalable.• Two different solutions:

– Making current proposed algorithm parallel.– Making parallel version based on Data Flow

Model for Big data.

~10 Exa of feasible solutions

54

General Approach For Making Parallel Solution

Mobile User Clustering and Grouping :

1. Fixed Grid Clustering2. K-mean Clustering

Mobile User LTW Ordering : 1. Random Ordering

2. Utility Based Ordering3. Optimal Ordering

Running Service/Resource Allocation Algorithms in Parallel

for each Cluster : 1. RSA

2. Greedy3. MuSIC

4. Genetic Algorithm

Assign Public and Local Cloud Resources to each Cluster

55

-Partition mobile users and local clouds based on their proximities and run service allocation algorithms for each region in parallel.

Public Cloud

Local

Cloud

Local

Cloud

Local

Cloud

Local

Cloud

Local

Cloud

Local

Cloud

Local

Cloud

Local

Cloud

56

• Single User/ Group of Mobile User :– Based on center of mobility of each mobile user/

Group of Mobile User, cluster mobile users/ Group of Mobile User into K cluster using k-Means [.

– Find the user density in each cluster as / – Find number of local clouds close to the as the

dedicated local cloud (the same for public cloud)

– Go to Round Robin for assigning services to each cluster.

– Run the mention service allocation algorithms for each region in parallel regarding to assigned local and public cloud.

57

Service 1

Service n

Mobile Users

Pig Latin pseudo codes:

/*Load Data*/LOAD mobile users , services….

/*Cartesian product to produce solution space*/CROSS Mobile users, Service1,…

/*Apply Optimization Constraints to solution space */FILTER by Constraints1,…

/*Find Best Solution*/FOREACH Mobile User GENERATE utility value

GROUP Solution By Mobile Users

FOREACH Solution GENERATE MAX

Apply System CONSTRAINTS

Compute UTILITY FUNCTION of each solution

for Mobile Users

GROUP Solutions for each Mobile Users

Find the MAXIMUM UTILITY for each Mobile

Users and emit as the best solution

58

Experimental Results

Constants:10,000 mobile users, uniformly distributed.6 different services per mobile users and for each service we have 10 different candidates (on local or public clouds)# of local clouds: 50 , uniformly distributed.# public cloud : 10 EC2 Large Instance (64-bit linux, 8Gib Mem, 800 Gib Storage).# number of different regions: 10

Variables:processing time per user according to different number of parralell machines.

Cluster Information:Amazon EC2 large Instance, 64-bit linux, ~8Gib Mem, ~800Gib Storage

4 6 8 10 120

200

400

600

800

1000

1200

1400

1600

1800

2000

RSA-ParMuSIC-ParGreedy-ParGA-Par

Number of Parralel Machines (Amazon EC2 Large Instance)

Pro

cess

ing

Tim

e in

Sec

ond

s

59

Experimental Results (Continue)

Constants:10,000 mobile users, uniformly distributed6 different services per mobile users and for each service we have 10 different candidates (on local or public cloud)# of local clouds: 50 , uniformly distributed# public cloud :10 amazon Large instance# number of different regions: 10# number of different groups: 500 groups

Variables:processing time per user according to different number of parallel machines.

4 6 8 10 120

50

100

150

200

250

300

350

400

450

500

G-RSA-ParG-MuSIC-ParG-Greedy-ParG-GA-Par

Number of Parralel Machines (Amazon EC2 Large Instance)

Pro

cess

ing

Tim

e in

Sec

ond

s

60

Experimental Results (Continue)

4 6 8 10 120

1000

2000

3000

4000

5000

6000

Pig-Based for Single UserPig-Based for Mobile Groups

Number of Parralel Machines (Amazon EC2 large Instance)

Pro

cess

ing

Tim

e in

Sec

ond

s

RSA-Par/Pig-Based MuSIC-Par/Pig-Based Greedy-Par/Pig-Based GA-Par/Pig-Based

Single 48% 77% 67% 70%Group 47% 71% 69% 68%

61

Conclusion and Future Direction

• Talk Summary:– LTW proposed as the modeling framework for mobile

service usage.– MuSIC (and other service allocation algorithms ) were

proposed and their optimality were studied for different class of mobile applications.

– MapCloud middleware has been reviewed.

• Future Work:– The beauty of this work is its level of Abstraction: LTW

could contain user behavior/context .– Future work will be focused on extracting context (defined

based on ontology) using data mining techniques.– This will lead to have efficient mobile cloud computing

ecosystem which could optimize different players (mobile users, service providers) criteria automatically.