mcc.pptx

“You don't generate your own
electricity. Why generate your
own computing?”
2

Outline
• Cloud Computing: Concepts and Terminologies
• What is Cloud Computing?
• Essential Characteristics
• Service Models
• Deployment Models
• Cloud Computing Modeling
• Cloud Computing Modeling: Functional View
• Cloud Computing Modeling: Qos View
• Mobile Cloud Computing Application: Toward Pervasive
Computing
• Motivation
• Related Works: Cloudlet , MapGrid and Calling the Cloud
• Conclusions
• References 3

‫السحابية‬ ‫الحوسبة‬
:
‫المفاهيم‬
‫والمصطلحات‬
4

‫السحابية؟‬ ‫الحوسبة‬ ‫هي‬ ‫ما‬
5
•
‫للشبكة‬ ‫المريح‬ ‫الوصول‬ ‫لتمكين‬ ‫ا‬ً‫ج‬‫نموذ‬ ‫السحابية‬ ‫الحوسبة‬ ‫تعد‬
‫عند‬
‫للتكوين‬ ‫القابلة‬ ‫الحوسبة‬ ‫موارد‬ ‫من‬ ‫مشتركة‬ ‫مجموعة‬ ‫إلى‬ ‫الطلب‬
(
‫مث‬
‫ل‬
‫والخدمات‬ ‫والتطبيقات‬ ‫والتخزين‬ ‫والخوادم‬ ‫الشبكات‬
.)
•
‫إداري‬ ‫جهد‬ ‫بأقل‬ ‫بسرعة‬ ‫وإصداره‬ ‫توفيره‬ ‫يمكن‬
.
•
‫والتخزين‬ ‫الحساب‬ ‫لنموذج‬ ‫المستوى‬ ‫عالي‬ ‫ًا‬‫د‬‫تجري‬ ‫يوفر‬
.
•
‫النشر‬ ‫ونماذج‬ ‫الخدمة‬ ‫ونماذج‬ ‫األساسية‬ ‫الخصائص‬ ‫بعض‬ ‫لها‬
.

‫األساسية‬ ‫الخصائص‬
6
•
‫الخدمة‬
‫الذاتية‬
‫عند‬
‫الطلب‬
:
•
‫يمكن‬
‫للمستهلك‬
‫توفير‬
‫إمكانات‬
‫الحوسبة‬
‫من‬
‫جانب‬
‫واحد‬
،
‫ا‬ً‫ي‬‫تلقائ‬
‫دون‬
‫الحاجة‬
‫إلى‬
‫تفاعل‬
‫بشري‬
‫مع‬
‫مزود‬
‫كل‬
‫خدمة‬
.
•
‫الوصول‬
‫غير‬
‫المتجانس‬
:
•
‫تتوفر‬
‫القدرات‬
‫عبر‬
‫الشبكة‬
‫ويمكن‬
‫الوصول‬
‫إليها‬
‫من‬
‫خالل‬
‫آليات‬
‫قياسية‬
‫تعزز‬
‫اال‬
‫ستخدام‬
‫من‬
‫خالل‬
‫منصات‬
‫العميل‬
‫الرفيعة‬
‫أو‬
‫السميكة‬
‫غير‬
‫المتجانسة‬
.

•
‫الموارد‬ ‫تجميع‬
:
•
‫م‬ ‫نموذج‬ ‫باستخدام‬ ‫المستهلكين‬ ‫من‬ ‫العديد‬ ‫لخدمة‬ ‫للمزود‬ ‫الحوسبة‬ ‫موارد‬ ‫تجميع‬ ‫يتم‬
‫تعدد‬
‫المستأجرين‬
.
•
‫دينا‬ ‫تخصيصها‬ ‫وإعادة‬ ‫المختلفة‬ ‫واالفتراضية‬ ‫المادية‬ ‫الموارد‬ ‫تخصيص‬ ‫يتم‬
‫ا‬ً‫ق‬‫وف‬ ‫ا‬ً‫ي‬‫ميك‬
‫المستهلك‬ ‫لطلب‬
.
•
‫المقاسة‬ ‫الخدمة‬
:
•
‫االس‬ ‫خالل‬ ‫من‬ ‫وتحسنها‬ ‫المستخدمة‬ ‫الموارد‬ ‫في‬ ‫ا‬ً‫ي‬‫تلقائ‬ ‫السحابة‬ ‫أنظمة‬ ‫تتحكم‬
‫من‬ ‫تفادة‬
‫الخدمة‬ ‫لنوع‬ ‫المناسب‬ ‫التجريد‬ ‫من‬ ‫معين‬ ‫مستوى‬ ‫عند‬ ‫القياس‬ ‫قدرة‬
.
•
‫بها‬ ‫التنبؤ‬ ‫ويمكن‬ ‫للتحليل‬ ‫قابلة‬ ‫حوسبة‬ ‫منصة‬ ‫سيوفر‬
.
7
‫األساسية‬ ‫الخصائص‬
(
‫تابع‬
)

‫الخدمة‬ ‫نماذج‬
8
•
‫كخدمة‬ ‫السحابية‬ ‫البرمجيات‬
(
SaaS):
• ‫تتمثل‬
‫القدرة‬
‫المقدمة‬
‫للمستهلك‬
‫في‬
‫استخدام‬
‫تطبيقات‬
‫الموفر‬
‫التي‬
‫تعمل‬
‫على‬
‫البن‬
‫ية‬
‫التحتية‬
‫السحابية‬
.
• ‫يمكن‬
‫الوصول‬
‫إلى‬
‫التطبيقات‬
‫من‬
‫أجهزة‬
‫عميل‬
‫مختلفة‬
‫مثل‬
‫مستعرض‬
‫الويب‬
(
‫على‬
‫سبيل‬
‫المثال‬
،
‫البريد‬
‫اإللكتروني‬
‫المستند‬
‫إلى‬
‫الويب‬
)
.
• ‫ال‬
‫يدير‬
‫المستهلك‬
‫أو‬
‫يتحكم‬
‫في‬
‫البنية‬
‫التحتية‬
‫السحابية‬
‫األساسية‬
‫بما‬
‫في‬
‫ذلك‬
‫الش‬
‫بكة‬
‫والخوادم‬
‫وأنظمة‬
‫التشغيل‬
‫والتخزين‬
...
• Examples: Caspio, Google Apps, Salesforce, Nivio,
Learn.com.

•
‫األساسي‬ ‫النظام‬
‫السحابي‬
‫كخدمة‬
(
PaaS):
•
‫ال‬ ‫التحتية‬ ‫البنية‬ ‫تطبيقات‬ ‫على‬ ‫النشر‬ ‫في‬ ‫للمستهلك‬ ‫المقدمة‬ ‫القدرة‬ ‫تتمثل‬
‫أنشأها‬ ‫التي‬ ‫سحابية‬
‫ي‬ ‫التي‬ ‫البرمجة‬ ‫وأدوات‬ ‫لغات‬ ‫باستخدام‬ ‫إنشاؤها‬ ‫تم‬ ‫والتي‬ ‫عليها‬ ‫حصل‬ ‫أو‬ ‫المستهلك‬
‫دعمها‬
‫الموفر‬
.
•
‫فيها‬ ‫يتحكم‬ ‫أو‬ ‫األساسية‬ ‫السحابية‬ ‫التحتية‬ ‫البنية‬ ‫المستهلك‬ ‫يدير‬ ‫ال‬
.
•
‫استض‬ ‫بيئة‬ ‫تكوينات‬ ‫وربما‬ ‫نشرها‬ ‫تم‬ ‫التي‬ ‫التطبيقات‬ ‫في‬ ‫المستهلك‬ ‫يتحكم‬
‫التطبيقات‬ ‫افة‬
.
•
Examples: Windows Azure, Google App.
9
(
‫تابع‬
)

•
‫كخدمة‬ ‫السحابية‬ ‫التحتية‬ ‫البنية‬
(
IaaS):
•
‫موا‬ ‫من‬ ‫وغيرها‬ ‫والشبكات‬ ‫والتخزين‬ ‫المعالجة‬ ‫توفير‬ ‫هي‬ ‫للمستهلك‬ ‫المقدمة‬ ‫القدرة‬
‫رد‬
‫األساسية‬ ‫الحوسبة‬
.
•
‫الت‬ ‫أنظمة‬ ‫تشمل‬ ‫أن‬ ‫يمكن‬ ‫والتي‬ ، ‫التعسفية‬ ‫البرامج‬ ‫وتشغيل‬ ‫نشر‬ ‫على‬ ‫قادر‬ ‫المستهلك‬
‫شغيل‬
‫والتطبيقات‬
.
•
‫ي‬ ‫ولكنه‬ ‫األساسية‬ ‫السحابية‬ ‫التحتية‬ ‫البنية‬ ‫في‬ ‫يتحكم‬ ‫أو‬ ‫المستهلك‬ ‫يدير‬ ‫ال‬
‫أنظمة‬ ‫في‬ ‫تحكم‬
‫ا‬ ‫مكونات‬ ‫في‬ ‫ًا‬‫د‬‫محدو‬ ‫ا‬ً‫م‬‫تحك‬ ‫وربما‬ ‫المنشورة‬ ‫والتطبيقات‬ ‫والتخزين‬ ‫التشغيل‬
‫المحددة‬ ‫لشبكات‬
(
‫المضيفة‬ ‫الحماية‬ ‫جدران‬ ، ‫المثال‬ ‫سبيل‬ ‫على‬
.)
•
Examples: Amazon EC2, GoGrid, iland, Rackspace Cloud
Servers, ReliaCloud.
10
(
‫تابع‬
)

11
(
‫تابع‬
)

‫النشر‬ ‫نماذج‬
12
•
‫خاصة‬ ‫سحابة‬
:
•
‫فقط‬ ‫لمؤسسة‬ ‫السحابة‬ ‫تشغيل‬ ‫يتم‬
.
•
‫خارج‬ ‫أو‬ ‫العمل‬ ‫مكان‬ ‫في‬ ‫موجودة‬ ‫تكون‬ ‫وقد‬ ‫ثالث‬ ‫طرف‬ ‫أو‬ ‫المنظمة‬ ‫قبل‬ ‫من‬ ‫تدار‬ ‫قد‬
‫المبنى‬
.
•
‫المجتمع‬ ‫سحابة‬
:
•
‫مجتم‬ ‫وتدعم‬ ‫المنظمات‬ ‫من‬ ‫العديد‬ ‫قبل‬ ‫من‬ ‫السحابية‬ ‫التحتية‬ ‫البنية‬ ‫مشاركة‬ ‫تتم‬
‫لديه‬ ‫ا‬ً‫ن‬‫معي‬ ‫ا‬ً‫ع‬
‫مشتركة‬ ‫مخاوف‬
.
•
‫خارجها‬ ‫أو‬ ‫العمل‬ ‫أماكن‬ ‫في‬ ‫موجودة‬ ‫تكون‬ ‫وقد‬ ‫ثالث‬ ‫طرف‬ ‫أو‬ ‫المنظمات‬ ‫قبل‬ ‫من‬ ‫تدار‬ ‫قد‬
.

•
‫العامة‬ ‫السحابة‬
:
•
‫وهي‬ ‫كبيرة‬ ‫صناعية‬ ‫لمجموعة‬ ‫أو‬ ‫الناس‬ ‫لعامة‬ ‫متاحة‬ ‫السحابية‬ ‫التحتية‬ ‫البنية‬
‫مملوكة‬
‫السحابية‬ ‫الخدمات‬ ‫تبيع‬ ‫لمؤسسة‬
.
•
‫هجينة‬ ‫سحابة‬
:
•
‫أكثر‬ ‫أو‬ ‫لسحبتين‬ ‫تكوين‬ ‫عن‬ ‫عبارة‬ ‫السحابية‬ ‫التحتية‬ ‫البنية‬
(
‫م‬ ‫أو‬ ‫خاصة‬
‫أو‬ ‫جتمعية‬
‫عامة‬
.)
13
(
‫تابع‬
)

14
(
‫تابع‬
)

‫نمذجة‬
15

‫نمذجة‬
:
‫وظيفي‬ ‫عرض‬
‫فئتين‬ ‫في‬ ‫النظر‬ ‫يمكن‬
:
‫النمذجة‬
‫الوظيفي‬ ‫غير‬ ‫والنموذج‬ ‫الوظيفية‬
(
‫جودة‬ ‫نموذج‬
‫الخدمة‬
.)
‫الوظيفي‬ ‫النموذج‬
:
(
Cloud Pool
:)
‫السحابة‬ ‫تجمع‬
‫أو‬ ‫المختلفة‬ ‫السحب‬ ‫مجموعة‬ ‫هي‬
•
(
Service Pool
:)
‫الداعمة‬ ‫الخدمات‬ ‫جميع‬ ‫مجموعة‬ ‫هو‬ ‫الخدمات‬ ‫تجمع‬
‫أو‬ ‫النظام‬ ‫في‬
:
•
‫ا‬ ‫ثنائي‬ ‫بياني‬ ‫رسم‬ ‫هو‬ ‫السحابية‬ ‫الخدمات‬ ‫لمطابقة‬ ‫البياني‬ ‫الرسم‬
‫ألجزاء‬
‫الصلة‬ ‫ذات‬ ‫بالخدمات‬ ‫السحب‬ ‫يطابق‬
.
{c1, c2 ,...,c|C | }
pool
Cp o o l
{s1,s2 ,...,s|S |}
pool
Spool
16

c1
c2
c3
c4
s1
s2
s3
s4
cn sn
‫الخدمات‬ ‫لمطابقة‬ ‫البياني‬ ‫الرسم‬
‫السحابية‬ 17
‫نمذجة‬
:
(
‫تابع‬
)

•
‫موجه‬ ‫دوري‬ ‫بياني‬ ‫رسم‬ ‫عن‬ ‫عبارة‬ ‫التطبيق‬ ‫أو‬ ‫الخطة‬ ‫أو‬ ‫العمل‬ ‫سير‬
(
DAG)
‫مع‬
‫التالية‬ ‫والدالالت‬ ‫الخصائص‬
•
‫الداللية‬ ‫العقدة‬
:
•
‫البداية‬ ‫عقدة‬ ‫تسمى‬ ‫فقط‬ ‫واحدة‬ ‫عقدة‬ ‫لديها‬
.
•
‫تسمى‬ ‫فقط‬ ‫واحدة‬ ‫عقدة‬ ‫لديها‬
End Node.
•
‫بكائن‬ ‫عقدة‬ ‫كل‬ ‫على‬ ‫توضيحي‬ ‫تعليق‬ ‫إضافة‬ ‫يتم‬ ، ‫والنهاية‬ ‫البداية‬ ‫عقد‬ ‫باستثناء‬
‫الخدمات‬ ‫تجمع‬ ‫من‬
.
•
‫الداللية‬ ‫الحافة‬
:
•
‫الحاف‬ ‫تسمى‬ ‫فإنها‬ ، ‫األخرى‬ ‫للعقدة‬ ‫فقط‬ ‫واحدة‬ ‫صادرة‬ ‫حافة‬ ‫على‬ ‫تحتوي‬ ‫العقدة‬ ‫كانت‬ ‫إذا‬
‫ة‬
‫المتسلسلة‬
(
‫التسلسلي‬ ‫الداللي‬
.)
si
sj
sk
‫متسلسلة‬ ‫حواف‬ 18
‫نمذجة‬
:
(
‫تابع‬
)

• ‫الحواف‬ ‫تسمى‬ ‫فإنها‬ ، ‫األخرى‬ ‫للعقد‬ ‫صادرة‬ ‫حافة‬ ‫من‬ ‫أكثر‬ ‫على‬ ‫تحتوي‬ ‫العقدة‬ ‫كانت‬ ‫إذا‬
‫المتزامنة‬
( Fork Concept ‫أو‬AND- Parallel):
si sj sk
sl
‫متزامنة‬ ‫حواف‬ ‫متسلسلة‬ ‫حواف‬
p2
sm
• If a node has two outgoing edges with weight then it is called
conditional edges (or XOR-Parallel).
p1
si sj sk
sl
‫شرطية‬ ‫حواف‬ ‫متسلسلة‬ ‫حواف‬
p1+p2=1; 0<p1,p2<1 19
‫نمذجة‬
:
(
‫تابع‬
)

• The loop node is considered as:
• The loop in this graph could be avoided as:
While/Loop
sj
sj
• It could be easily converted to sequence edges with
the upper bound loop limit.
While/Loop
sj sj sj sj
Loop Bound=4
sj si sj
sj si
Cloud Computing Modeling: Functional
View (cont.)
20

• Critical Service: The services in the plan with the
highest number of inputs and outputs edges.
21
Cloud Computing Modeling: Functional
View (cont.)

Cloud Computing Modeling: Qos View
• The cloud quality of service is defined as:
• Some important Single Cloud Qos are:
Sp o o l
Q o s : C p o o l (q1 , q 2 ,...,qn )
when a requestis sent and theresults are received of
s j on cloud ci .
Level
For services j wecould also define servicelevel or s j .
Priceof using services j on cloud ci .
Delay in secondsbetween the moment
qprice (ci , s j )
qduration (ci , s j )
22

(qprice ,qduration ,qreliability ,qCapacity , qavailability , qreputation )
Qos:Cpool
qreputation (ci ,s j )
qavailability (ci ,s j )
The probability that therequest
is correctlyansweredwhithin
specific time.
The maximum of clients that s j
on ci could recieve.
The probability that teservice
is accessiblewithin specific time.
SocialRank of theservice
like Amazon[0,5]
qCapacity (ci ,s j )
qreliability (ci ,s j )
S pool
Cloud Computing Modeling: Qos View (cont.)
23

Job Qos (end-to-end Qos)
• The Qos is also defined for work flow.
The avaeragereputation of all services
0 else1.
Sum of thepricesin executable job.
(wf )
24
(wf )
Qos
Qos
Qos (wf )
reputation
in executable job.
services zi 0 else1.
N
i 1
availability
services zi
reliability
Qos (wf )
price
Qos (wf )
duration
eqavailabiltiy (ci ,s j ) zi
for non - critical
The longest path in executable job.
N
eqreliabiliyt (ci ,s j ) zi
for non - critical
i 1

• The typical optimization problem that happens in
this area is to find appropriate cloud compositions
that satisfied the required constraints and maximize
or minimizes the utility function.
• The following is the general architecture for cloud
computing system.
Task Query+ Desired Qos
Task Query to Job (DAG)
Cloud Composition to Satisfy Qos Model
25

Subject to:
26
max )
)
i
wi 1
1
The selectionis over cloudsandservicespaces.
Qosreputation(wf )
5
Qosavailability(wf )
creliability
cavailability
creputation
Qosreliability(wf )
Qosduration(wf )
c price
cduration
Qos price (wf )
Qosreputation
( wf )
Qosreputation
( wf ) creputation
) w5 (
Qosavailabiltiy( wf )
Qosavailabiltiy( wf ) cavailabiltiy
w4 (
Qosreliabiliyt( wf )
Qosreliabiliyt( wf ) creliabiliyt
) w3 (
cduration
cduration Qosduration( wf )
) w2 (
cprice
cprice Qos
price( wf )
w1 (
one possible
optimization problem
when the target function
is linear.

Mobile Cloud Computing:
Toward Pervasive Computing
27

“The most profound technologies are
those that disappear. They weave
themselves into the fabric of every
day life until they are
indistinguishable from it.”
28
“Mark Weiser”, Xerox Scientist

Motivation
29
• Pervasive computing names the third wave in
computing, just now beginning.
• First were mainframes, each shared by lots of people.
• Now we are in the personal computing era, person
and machine staring uneasily at each other across the
desktop.
• Next comes Pervasive computing, or the age of calm
technology, when technology recedes into the
background of our lives.
• Alan Kay of Apple calls this "Third Paradigm"

Evolution of Computing Environment:
[Satyanarayanan_2001] :
Toward Pervasive Computing environment 30
Cloud
Computing
Promises
Motivation (cont.)

• Mobile Cloud computing technology promises the
realization of pervasive computing era.
• Many different application could be considered in this
area, such as M-Commerce, M-learning, M-
entertainment.
• In this work we consider the M-Multimedia
application in mobile computing environment.
• It is called multimedia streaming in mobile
computing area.
31
Motivation (cont.)

• In the next section we are trying to review some
important works done related to mobile cloud
computing.
• Based on the previous work we are going to propose
an architecture for mobile cloud computing for
multimedia streaming application.
33
Motivation (cont.)

Related Works: Cloudlet
MapGrid and Calling the
Clouds
34

Cloudlet
• It has been shown that one hop connection from
mobile device to internet is not efficient.
• Human are sensitive to the current delay in clouds.
• It seems that latency is unlikely to be improved.
• Considering different layer for software such as security
and firewall it is unlikely that latency improves
(although increase in bandwidth).
• This motivate us to use cloudlet between mobile
devices and cloud pools (Infrastructure).
• A cloudlet (I refer it as the micro edition of 35

Cloudlets are as the infrastructure for mobile cloud computing. From: [Satyanarayanan_2009]
36
The prototype based on this systems is implemented in
CMU and called Kimberley [Satyanarayanan_2009] .
Cloudlet (cont.)

MapGrid (Mobile Application on Grid)
37
• Base on the work [Huang_2002] , [Huang_2005] ,
[Huang_2007] which is middleware approach.
• It uses Grid as the cache proxy for mobile applications.
• Resources on grid (storage and computation) are
intermittently available.
• MapGrid uses the interval tree data structure for storing
the information about available resources on grid.
• Mobility pattern has been used to optimal data
replication policy in MapGrid.
• The proposed middleware does:

Broker
Directory
Service
Request
Scheduler
Resource
Reservation
Volunteer
Servers(Grids)
Grid
Monitoring
MapGrid Middleware Service Architecture
Req Mobile
Client
Info
Mobile Client
Monitoring
Moving_Profile
Req Req*
Req#
Placement Req@
Management
Tertiary Data
Storage
1. Resource Discovery
2. Schedule Planning
Reports changes in
grid resources
Admission Control
Contains information about
available resources on Grid
and Clients
Replace the data
to grid resources
Mobility Pattern detection
38

MapGrid Definitions and Notations
Availability( j,t)
Gf (R,t)
LFj (R,t) Dist( j, R)
j
• Request: R(VID, itinerary) where VID is the video
and itinerary is mobility information.
• Grid Loading Factor:
LFj (R,t) max{CPUa , MEMa , NBWa , DBWa}
whereXa is theratio of requestedresource
to available resourceon grid j. 0 LFj 1
If grid j is available at
• Grid Factor: time t equal 1 else 0.
Distance of grid J from
request R.
39

• Segment size: Sd the unit size of data.
• The MapGrid algorithm has the following tree steps:
• PartitionServicePeriod():
• partition the request according to the segment size or
other fast startup policies (to start the service as fast as
possible).
• VolunteerGridAllocation():
• Maps chunks to the specific grid according to LF and
proximity. The biggest Grid factor could be selected. The
start point could be used to initialized the algorithm.
• MobilityBasedRescheduling(): 40
MapGrid Definitions and Notations (cont.)

MapGrid Optimization
• With the knowledge of the mobility pattern, we could
optimize the first two function, service period and
grid resource allocation.
itinenary [(Time1,Cell1 ),..., (Timen ,Celln )]
• Service Period Optimization:
• Policy 1: MajoritySpreadOver attempts to minimize
number of Grid switches.
• Assign the chunks of data according to the duration that
mobile client spends in each cell.
• Weakness: Delay in startup.
• Policy 2: DistancePartition partition the service time
41

• Volunteer Service Optimization:
• With knowing the mobility pattern and the time
duration that mobile client spend in each cell our task
is to find grid resources to maximize the following
utility function. n
i i i
i 1
whereDi is theduration of time the
mobile client spendsin celli .(ai ,bi )
is cellcenterand (x,y)is thegrid location.
(x a )2
(y b )2
f (x, y) D
MapGrid Optimization (cont.)
42

Calling The Cloud
43
• It is based on the optimal task migration between
server and mobile client [Giurgiu_2009].
• They have used OSGi (Open Services Gateway
Initiative) and AlferedO platform (component based
architecture) for prototype implementation.
• The key Idea is to make an application graph and try
to make a cut (some tasks will be done on server side
and remaining will be done on mobile side) to meet
the required optimization issues.
• The following figure shows the main idea.

S1 S2 S3
S4
S5
S7
S6
S8
S1 S2 S3
S4
S5
S7
S6
S8
Mobile Cut
Mobile Cut
S3
S4
S5
S7
S6 S1 S2
S8
Original Plan
44

“You usually use your cell phone for
communication service. Why not
getting computation service?”
45
Reza, UCI Student 

• Lesson that we have learned:
• For pervasive environment location-based services
considered as the important element in design.
• So we should consider the location of cloud as an important
optimization factor which is lead to cloudlet as mentioned
before.
• This suggests 3-tier architecture.
• Cloudlet has minimum capability, usually storage and limited
processing (for example Multimedia Streaming).
• The following figure shows this architecture.
46

Cloud Pool
(Amazon, Google, Microsoft,….)
Cloudlet or
Wireless Cloud
Cloudlet or
Wireless Cloud
Cloudlet or
Wireless Cloud
47

Location-Base Service Composition
• Based on different criteria we could optimize the
solution for different mobile and movment pattern.
S1|L0|T6 S2|L1|T7 S3|L2|T1
S4|L3|T5
S5|L4|T4
S7|L1|T2
S6|L0|T0
S8|L0|T3
• For mobile computing application we could define the
Time Location Workflow.
• It is the same as general work-flow with additional
information about the location of the service.
Time_LocationWorkflow
48

Mobile Cloud Computing Applications
(cont.)
• We are thinking about the middleware approach as the
solution for achieving Qos.
• Before going ahead let’s see what sort of atomic and
implementable elements and technology we have in
the cloud pool.
• Web Service:
Application Programming Interfaces (API) or web APIs that can
be accessed over a network and executed on a remote system
hosting the requested services. It has 3 important protocols:
1. WSDL (Web Service Description Language): XML-based interface
for definig Web Service functionality.
2. SOAP (Simple Object Access Protocol): Protocol for exchanging 49

• UDDI has tree parts:
• White Pages: Address, contact, and known identifiers;
• Yellow Pages: Industrial categorizations based on standard
taxonomies;
• Green Pages: Technical information about services exposed by
the business.
• Some people are thinking about the Blue Pages which
described the quality of services.
• IBM [IBM], also introduced WSLA (Web Service
Language Agreement) where obligation factors
related to Qos between service provider and client are 50
Mobile Cloud Computing Applications (cont.)

UDDI Registry Important API
51
• [Alonso_2004] UDDI Inquiry API:
 find_business(), find_service(), find_tmodel()
 get_businessDetail(), get_serviceDetail(),
get_tmodelDetail().
• [Alonso_2004] UDDI Publishers API:
 save_business(), save_service(), save_tmodel()
 delete_business(), delete_service(), delete_tmodel().
• [Alonso_2004] UDDI Security API:
 get_authToken(), discard_authToken().

Mobile Client Middleware(Broker) Cloudlet and Cloud Pool
Cloud Service Registry and Discovery
Qos Monitoring , Service Discovery
Scheduler
Mobile
Client
Cloudlet
Pools
Mobile Profile Monitoring
Mobile Profile Analyzer
Qos Analyzer
Admission
Control Cloud
Pools
Qos
Monitoring

Middleware Blocks Functionalities
Admission Control Accepts or rejects mobile client requests according to the resources available
and mobile profile (it could be power- aware, resource –aware and…. Policies,
Maybe Game Theory could be used for optimal admission policy).
Mobile Profile Monitoring Monitors mobile profile.
Mobile Profile Analyzer Analyzes mobile profile specially power level, location
pattern,… for optimal scheduling.
Scheduler It schedules the accepted requests and design a plan according to the negotiations
done with Cloudlets (Schedule Queue).
Qos Monitoring, Service
Discovery (Cloud and Cloudlets)
It discovers cloudlets (maybe based on ontology and semantics) in the
area (like UDDI), its Qos during service from mobile client. It also
registers its services and middleware business on UDDI for accessing mobile
clients.
Qos Analyzer It is analyzing the Cloudlets services and make a list of suitable cloudlets
and policy of admission.
Mobile Client Block Functionalities
Qos Monitoring Reports the Middleware about the Qos of the cloudlets.
53

Sample Prototype
54
• Simple barcode reader service has been implemented.
• In this scenario the user takes a picture of the barcode
for getting some information about the object, for
example the cheapest price location.
• The picture is send to cloudlet for processing.
• The cloudlet extract the information and query
Amazon or other clouds for the price.
• The price will be return back to user.
• The next picture shows the scenario sequence
diagram [Rahimi_2008].

Cloud(Amazon)
Cloud(Yahoo)
Cloudlet
Yahoo Cloud (Flicker) as a Cache.
56

57
http://www.youtube.com/watch?v=fQywFeN1wdM

Class Diagrams:
Mobile Client
58

Conclusions and Research Direction
• We talked about the new era in Information
Technology which is know as cloud computing.
• Several different aspects of this area have been
reviewed and discussed.
• The deployment of cloud computing in mobile
environment were discussed.
• This middleware could be implemented as the Local
web services (as in barcode reader example).
• Good simulation environment should be considered in
this area (Currently I am working on OMNet++ to
develop for service composition in mobile 67

References
68
1. [Alonso_2004] G. Alonso, F. Casati, H. Kuno, V. Machiraju “Web Services,
Concepts, Architectures and Applications” , Springer 2004.
2. [Berkely_2009] “Above the Clouds: A Berkeley View of Cloud
Computing”, Tech. Report 2009.
3. [Chen_2003] H. Chen, T. Yu and K. Lin. “QCWS: An Implementation of
QoS-Capable Multimedia Web Services”, In Proc. of IEEE 5 th
Symposium on Multimedia Software Engineering (MSE03), Taiwan, Dec
2003
4. [Giurgiu_2009] Giurgiu, I., Riva, O., Juric, D., Krivulev, I., and Alonso, G ”
Calling the Cloud: Enabling Mobile Phones as Interfaces to Cloud
Applications” In Proceedings of the 10th ACM/IFIP/USENIX international
Conference on Middleware (Urbana, Illinois, November 30 - December 04,
2009).
5. [Huang_2007] Huang, Y. and Venkatasubramanian, N. “ Supporting

6. [Huang_2005] Huang, Y., Mohapatra, S., and Venkatasubramanian, N
”An Energy-Efficient Middleware for Supporting Multimedia
69
Services in Mobile Grid Environments” In Proceedings of the
international Conference on information Technology: Coding and
Computing (Itcc'05) - Volume II - Volume 02 (April 04 - 06, 2005).
7. [Huang_2002] Y. Huang, N. Venkatasubramanian, "QoS-Based
Resource Discovery in Intermittently Available Environments,"
pp.50, 11th IEEE International Symposium on High Performance
Distributed Computing , 2002.
8. [IBM], "WSLA Web Service Level Agreement“
http://www.research.ibm.com/wsla/
9. [Ko_2008] Ko, J. M., Kim, C. O., and Kwon, “Quality-of-Service
Oriented Web Service Composition Algorithm and Planning
Architecture”. J. Syst. Softw (Nov. 2008).
10. [Mell_2009] P. Mell, T. Grance, "The NIST Definition of Cloud
Computing", Version .15, Oct 2009.
References (cont.)

11. [Mabrouk_2009]N. Mabrouk, S. Beauche, E. Kuznetsova, N.
Georgantas, and Issarny “ QoS-Aware Service Composition in Dynamic
Service Oriented Environments ” In Proceedings of the 10th
ACM/IFIP/USENIX International Conference on Middleware (Middleware
'09).
12. [Rahimi_2008] M. R. Rahimi, J. Hengmeechai, N. Sarshar “Ubiquitous
Application of Mobile Phones for Getting Information from Barcode
Picture”, in the iCORE2008, Edmonton, Canada.
13. [Satyanarayanan_2001] Satyanarayanan, M. "Pervasive Computing:
Vision and Challenges“ IEEE Personal Communications, August 2001.
14. [Satyanarayanan_2009] Satyanarayanan, M., Bahl, P., Caceres, R., and
Davies, N. 2009. “The Case for VM-Based Cloudlets in Mobile
Computing” IEEE Pervasive Computing 8, 4 (Oct. 2009), p. 14-23.
15. [Zeng_2003] Zeng, L., Benatallah, B., Dumas, M., Kalagnanam, J., and 70
References (cont.)

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