Design of Multi-Criteria Decision making algorithm for Cloud.pptx

Design of Multi-Criteria Decision Making
Algorithms for Cloud Computing
Submitted By: Guided By:
Munmun Saha Dr. Suvasini Panigrahi
Reg No – 1810040005 Co-Guide: Dr. Sanjaya Kumar Panda
Department of Computer Science and Engineering
Veer Surendra Sai University of Technology
Burla,Odisha, India
1

CONTENT
 Introduction to Cloud Computing
 Overview of Multi-Criteria Decision Making
 Motivation and Objectives
 Previous Work
 Problem Statement
 Proposed Methodology and Case Study
 Road Map
 Conclusions
 Reference
2

Definition of Cloud Computing
Some Definitions of Cloud Computing
• Cloud Computing is a general term which simply means, distributed computing
over the internet, or delivering computing service on the internet .
• The practice of using a network of remote servers hosted on the Internet to
store, manage, and process data, rather than a local server or a personal
computer. This is known as Cloud computing.
• National Institute of Standards and Technology (NIST), which
defines cloud computing as, a model for enabling convenient, on-demand
network access to a shared pool of configurable computing resources that can
be rapidly provisioned and released with minimal management effort or service
provider interaction.
3

Services of Cloud Computing
4
Figure 1 Services of Cloud Computing

Deployment Models of Cloud
Computing
5
Figure 2 Deployment Models of Cloud Computing

Multi-Criteria Decision Making
Multi Criteria Decision Making (MCDM) refers to making decisions in the
presence of multiple usually conflicting criteria.
6
Figure 3 Multi-Criteria Decision Making

Real Life of Multi-Criteria
Decision Making
Suppose we want to buy a Car.
We have different alternatives- BMW, Ford, Honda, Toyota etc.
To Select the Best
Car
Price MPG Style
Riding
Comfort
7
Figure4 Real life example of Multi-Criteria Decision Making

Motivation and Objectives
In spite of huge significance, scant attention has been given in the area of MCDM
in cloud computing by considering all the performance parameters including both
beneficial and non beneficial attribute. The primary objective of MCDM is to
select the best Cloud Service Provider without compromising any SLA index, and
considering both the objective and subjective criteria.
Moreover, whatever the algorithms that have been implemented in the existing
works that is also limited to many applications specially minimizing the non-
beneficial attribute and maximizing the beneficial attribute value. Following these
ideas and motivated from previous works, the objective of MCDM algorithm are
as follows:
8

Motivation and Objectives
• To select the best CSP among various homogenous alternatives.
• To evaluate different QoS parameter of the Cloud Service Provider.
• Not to Compromise the Key Performance index mentioned in the SLAs.
• To categorized the QoS parameter in B-O-C-R model (Benefits, Opportunities,
Cost, Risk) where Benefit and Opportunities are beneficial attribute and Cost and
Risk is Non-Beneficial attributes.
• To select the CSP having the maximum beneficial and minimum non-beneficial
criteria value.
• To select best criteria of each cloud provider, such that a composite service is to
be provided by collaborating among the CSPs.
9

Elements of Multi-Criteria
Decision Making
MCDM problem has five elements
• A Goal
• At least two Alternatives
• Two or more criteria
• Criteria weights
• Decision Makers
10

Type of Multi Criteria Decision
Making
They further classified into
1. Multi Object Decision Making (MODM)
2. Multi Attribute Decision Making (MADM)
MODM is applied on Continuous search space whereas MADM is applied
on finite number of alternatives or discrete search space.
MCDM
MODM MADM
11
Figure 5(a) Multi-Criteria Decision Making , Figure 5(b) Multi-Criteria Decision Making

Some existing algorithms of MCDM
MCDM
MODM MADM
Weighted Sum Method (WSM)
Weighted Product Method (WPM)
Analytic Hierarchy Process
(AHP)
Technique for Order Preference by
Similarity to Ideal Solution (TOPSIS)
Preference Organization Ranking Method
for Enrichment of Evaluation
(PROMETHEE)
Vise Kriterijumska Optimizacija I
Kompromisno Resenjie (VIKOR)
Multi Objective
Optimization using
Ratio Analysis
(MOORA)
Graph Theory Matrix
Approach (GTMA)
12

Literature Review (Comparison Table)
Sl.
No.
Author’s
Name
Year MCDM
Method
Objective Approach Service Tool Criteria
and Alter-
natives
1 Manish
Godse, Shrikant
Mulik [27]
2008 AHP To select
Appropriate
SAAS product.
Weight of the
parameter and final
product of the score
of the alternatives
are calculated by
AHP to avoid
subjective opinion.
SAAS - 5 criteria
and
16 sub
criteria and
3
alternatives
.
2 Vuong Xuan
Tran et al.
[32]
2009 AHP To rank web
Service.
QoS based ranking
Algorithm
combining AHP to
rank web service.
- - 8 QoS
property
and 5 web
service.
3 Chen-Tung
Chen, Kuan-
Hung Lin
[28]
2010 FAHP For evaluating
Cloud Service
Interval value
Fuzzy sets
combining with
AHP are used for
evaluation of
cloud service.
Overall
services -
3 criteria
9
Sub-
criteria
and 3
alternativ
13
Table 1 Comparison Table

Literature Review (Comparison
Table)
4 Saurabh
Kumar,
Garg,Steve
Versteeg,Rajkum
ar Buyyaa [20]
2013 AHP Framework
To Rank Cloud
Computing
service.
AHP for both
assigning weight to
criteria and ranking
the alternatives.
Overall
Services.
- 6 criteria
and
9 sub
criteria
and rank 3
alternatives
5 Hong-Kyu
Kwon1, Kwang
Kyu Seo [23]
2013 Fuzzy
AHP
To select suitable
IAAS provider.
Fuzzy AHP for
assigning weight to
the criteria and
AHP for ranking
the alternatives.
IAAS Exper
t
Choic
e
3 criteria
and 8 Sub
criteria
and
5 IAAS
Provider.
6 Mingzhe Wang,
Yu Liu [24]
2013 ANP Evaluation
of QoS
requirement
in Cloud Service
Architecture
(CSA).
Control Hierarchy
is constructed using
ANP, relative
superiority is
calculated from the
super-matrix.
IAAS
PAAS
SAAS
CPN+
and
Clous
- Sim
35 criteria
and 5
alternatives.
7 Gultekin Atas
,Vehbi Cagri
Gungor [21]
2014 AHP,LSP To Evaluate
the performance
of the PAAS
Provider.
AHP for
decomposing
performance
variable and LSP
for logical scoring
PAAS - 3 criteria
19 sub
criteria and
3
alternatives.
14

Table)
8 Ramachandran
N. et al.[22]
2014 AHP To deploy a
appropriate
model of cloud
computing in
an academic
institution.
AHP for
comparing the
the cloud model.
Overall
service
Super
Deci-
sion
6 main
factor and
28 sub
factors, and
consider
4 cloud
deployment
model.
9 Mohamed
AbdelBasset et.
al.[25]
2016 NAHP To evaluate
Cloud Computing
service.
Neutrosophic
MCDM analysis
approach based on
AHP.
IAAS
PAAS
SAAS
- 5 criteria 3
alternatives.
10 Rajanpreet
Kaur Chahal and
Sarbjeet
Singh[26]
2016 AHP To rank
CSP.
AHP is used for
comparison of the
the alternatives.
Overall - 4 criteria
and 5
alternatives.
11 Rakesh Ranjan
Kumar et al. [29]
2017 AHP and
Fuzzy
TOPSIS
Prioritizing
the solution of
cloud service
selection.
AHP for calculation
weight of the
criteria and Fuzzy
TOP- SIS for final
rank of alternatives.
- - 10 Criteria
and 6
alternatives.
15

Table)
12 Neeraj Yadava,
Major Singh
Gorayab[31]
2017 AHP Service Mapping
In the cloud
environment.
Two-way service
mapping approach
between the Service
Requesting
Customer (SRCs)
and the CSP based
on AHP ranking.
and 3
alternatives.
13 Rakesh Ranjan
Kumar,
Chiranjeev
Kumar[30]
2018 AHP and
TOPSIS
To select and
rank cloud
service.
AHP for weighting
the criteria and
TOPSIS for ranking
the alternatives.
IAAS
PAAS
SAAS
- 10 criteria
and 6
alternatives.
14 Jagpreet
Sidhu, Sarbjeet
Singh
[33]
2017 AHP
TOPSIS
PROME-
THEE
To determine
trustworthiness
of CSP
Trust is evaluated
using three MCDM
technique AHP,
TOPSIS
PROMETHEE and
the result is
compared.
- - 10 Attribute
and 18 CSP
15 Zoie ra dulescu,
Cristina,Ra
dulescu [34]
2017 E-TOPSIS To rank CSP. Extended TOPSIS
By using
Murkowski
distance is used to
rank the CSP.
- - 32 criteria
and 10 CSP.
16

Table)
16 Omar
Boutkhoum et al.
[35]
2017 Fuzzy
AHP
Fuzzy
TOPSIS
To select suitable
cloud solu-tion
for big data
project.
decision making
approach consist of
FAHP for assigning
weight to criteria
and FTOPSIS for
ranking
alternatives.
- - 10 criteria
and 5
alternatives.
17 R Krishan kumar
et al. [36]
2017 IF-GDM
IF-AHP
To select Best
cloud vendor.
Intuitionistic Fuzzy
Group-Decision-
Making (IF-
GDM) approach
based on IF-AHP
for pairwise
comparison of
cloud.
- - 5 criteria
and 4
alternatives.
18 Chandrashekar
Jatoth et al.[37]
2018 AHP
and G-
TOPSIS
To select the
cloud service.
AHP for defining
priorities of criteria
and EG-TOPSIS
for selecting and
ranking the
alternatives.
- - 5 criteria
and 19
alternatives.
17

Table)
19 Osama So-
Haib Mohsen
Naderpour
[38]
2017 Fuzzy
TOPSIS
Suitable
Adoption of
cloud computing
in e-commerce.
Categorized the
criteria in TOE
factors and ranked
the cloud service by
Fuzzy TOPSIS.
SAAS
PAAS
IAAS
- 12 criteria
and 3
alternatives.
20 Deepti Rai,
Pavan Kumar V
[39]
2016 TOPSIS,
VIKOR
To select
Best cloud
service.
Daily basis ranking
comparison of
cloud service of
using TOPSIS ANF
VIKOR.
IAAS Cloud
- Sim
3 criteria
and 10
alternatives.
21 Hamzeh
Mohammd
Alabool, Ahmad
Kamil Mahmood
[40]
2013 FM-
VIKOR
Trust based
Cloud
Service Selection.
Modified VIKOR is
extended by using
fuzzy to evaluate
trust of CIS, and
ranked then based
on their degree of
trust.
IAAS - 15 criteria
and 5 CSP.
22 Zohreh Ak-
barizadeh, Mahdi
Faghihi[41]
2017 SWARA,
VIKOR
To rank CSP SWARA for
assigning and
VIKOR to Ranking
the CSP.
- - 28 criteria
and 4
alternatives.
18

Table)
23 Jagpreet
Sidhu1, Sarbjeet
Singh1[42]
2019 I-PROM
ETHEE,
AHP
To select
trustworthyCloud
Databse Server.
AHP for
relative importance
of the criteria
and IMPROVED-
PROMETHEE for
ranking the CDS
by calculating
Positive Outranking
Flow and
Negative
Outranking Flow.
- - 10
parameters
and 18
alternatives.
24 Hua Ma at
al.[43]
2017 N-
ELECTRE
Trustworthy
Ranking
Prediction for
Cloud Service
Improved
ELECTRE is
formed by
combining INS and
KRCC, INS is used
for measuring the
trust and KRCC is
used for ranking.
- - 8 CSP
25 Gulcin
Buyu kozkan1 et
al.[44]
2018 IVIF
MCDM
methods.
CCT selec-
tion based
on IVIF MCDM
methods.
IVIF AHP for
pairwise
comparison of
criteria, IVIF
- - 6 criteria
and
27 sub
criteria, 4
19

Table)
26 Radulescu
Constan¸ta Zoie
et al.[45]
2016 DEMATAL
and AHP
To assign
weight and rank
criteria for CPS
A hybrid method
DANP is used for
calculating criteria
and cluster weight
and the global
weight and the rank
is evaluated from
the super matrix.
- - 32 criteria
and 3
cluster.
27 Chandrashekar,Ja
toth1 et al. [46]
2016 AHP,
ANP
M-DEA,
M-SDEA
To evaluate
the efficiency of
cloud service.
AHP, ANP for
determining
priority and weight
of the QoS attribute
and DEA and
SDEA for
calculating the
efficiency to rank
the cloud service.
- - 7 criteria
and 11
Alternatives
.
28 Nivethitha
Somu et al.[47]
2017 HGCM,
MDHP
To rank CSP. Helly Property and
Hyper Graph is
used to assign
weight and MDHP
is used for ranking
the alternatives.
and 5
alternatives.
20

Table)
29 Chinu Singla
et al.[48]
2018 FDM,
FAHP
Decision
making model for
multimedia cloud
based on
computational
Intelligence.
FDM for selection
of decision criteria
and FAHP for
determining
importance of each
criteria and rank the
alternatives.
IAAS Cloud
Sim
and
MAT-
LAB
5criteria
and
5
alternatives
30 Gireesha
Obulaporam et.
al.[52]
2019 CRITIC
and Grey
Rela-
tional
Analysis
Ranking
approach
for Cloud
Service
Selection
To overcome the
Rank reversal of
Many MCDM
method, GCRIT-
ICPA is used.
CRITICA to
determine weight
of criteria and
GRA to rank the
CSP.
- -
19 CSP
and 9
attributes.
21

Problem Statement
Consider a set of m clouds where
and a set n criteria
In which each criteria has a weight
Note that
A criteria , where (1 ≤ i ≤ n) refers to one of the
attributes of the cloud.
)
,...,
,
,
( 3
2
1 m
C
C
C
C
C 
)
,...,
,
,
( 3
2
1 n
A
A
A
A
A 
i
A i
W
1
1



n
i
i
W
i
A
22

Problem Statement
The criteria are further categorized into beneficial criteria or non-beneficial
criteria. Here, the value of beneficial criteria is to be maximized, whereas the
value of non-beneficial criteria is to be minimized. The value of each cloud
with respect to the criteria is presented in the form of a matrix called multi-
criteria decision making (MCDM) matrix, is shown in Eq. 1
(1)
An element (1 ≤ i ≤ m, 1 ≤ j ≤ n) in MCDM matrix denotes the
performance value of a cloud on a criteria
ij
M
i
C j
A
23

Problem Statement
Given a MCDM matrix, the problem is to select a best cloud out of a set of
m clouds or rank a set of clouds, such that the best cloud service provider
(CSP) holds the maximum beneficial and minimum non-beneficial criteria
value.
Moreover the problem is to select best criteria of each cloud provider, such
that a composite service is to be provided by collaborating among the
CSPs.
24

Proposed Method
Objective- A B-O-C-R Model for cloud selection using ANP and VIKOR.
The computation of the service index is done using the quality of
service (QoS) data of three cloud providers namely Amazon EC2,
Windows Azure and Rackspace
The QoS data is collected from various evaluation studies [ Garg et.al.]
The unavailable data is assigned randomly.
25
QoS requirements are Accountability, Agility, Assurance, Performance,
VM cost, Data cost, Storage cost, Adaptability, Flexibility, Serviceability,
Provider’s risk, Compliances, HR risk.

Proposed Method
Main steps to model the Cloud Selection problem
• Group the QoS requirements in B-O-C-R model ( Benefit , Opportunities, Cost,
Risk )
• Compute the relative importance of the QoS requirements in each group and
find the local priority of the alternatives in each group using ANP.
• Rank the alternatives using VIKOR.
26

Proposed Method
A brief explanation of Analytical Hierarchy Process (AHP)
AHP is a well-known MCDM algorithm which perform pair-wise comparison of
criteria and sub-criteria, resulting a local priority or an weighting factor.
Goal
Goal
C1 C2 C3
A1 A2 A3
1516 68
16
52 24 24 27

Proposed Method
C1,C2, C2 are criteria and A1, A2, A3 are alternatives
Goal
C1 C2 C3
A1 A2 A3
1516 68
16
52 24 24
The goal is to find the best alternatives.
 The criteria weight is assigned by
relative comparison matrix and local
priority is calculated by the Eigen
value of the matrix.
 By applying the global priorities to
the alternatives, we finally get a
ranking of alternatives with respect to
the criteria and sub-criteria.
28

Proposed Method
We have used Analytical Network Process (ANP) instead of AHP in our
algorithm
 The ANP is a decision finding method and generalization of the AHP
 ANP can model complex decision problem where a hierarchal model AHP is
not sufficient.
 In ANP criteria, sub criteria , alternatives are treated equally as nodes in a
network.
 Each of the node might compared to any other node , as long as there is a
relation between them.
 In ANP nodes might grouped in clusters e.g. beneficial, non-beneficial.
 Beside local priorities in the comparison of one node to a set of other node
cluster priorities can be introduced. 29

Proposed Method
We have used Analytical Network Process ANP instead of AHP in our algorithm
 The comparison of nodes to other follows the same principal and method as in
AHP
 Local priorities resulted from the Eigen vector of the comparison matrix.
Goal
C1 C2 C3
A1 A2 A3
15
30

Proposed Method with Case Study
We have used “Super Decision” Tool for computing ANP
31

Step 1 Group the QoS requirements in B-O-C-R model (Benefit,
Opportunities, Cost, Risk)
32

Step 1 Group the QoS requirements in B-O-C-R model (Benefit,
Opportunities, Cost, Risk)
33

Step2. Compute the relative importance of the QoS requirements in each
group and find the local priority of the alternatives in each group using
ANP.
34

Step2. Compute the relative importance of the QoS requirements in each
group and find the local priority of the alternatives in each group using
ANP.
35

Similarly for every nodes relative comparison is perform.
And finally a Super Decision Matrix is formed which contains the local
priorities of each alternative in each individual group
Matrix for Benefits
36

Matrix for Opportunities
37

Matrix for Cost
38

Matrix for Risk
39

Overall value after final comparison
Sl No. Alternatives Benefits Opportunities Cost Risk
1 Amazon EC2 0.248367 0.166850 0.156623 0.255299
2 Rackspace 0.114261 0.164199 0.156356 0.124611
3 Windows Azure 0.137372 0.168951 0.187021 0.120089
40

Overall value of final comparison is represented in a Rader chart .
0
0.05
0.1
0.15
0.2
0.25
0.3
Benefits
Opportunities
Cost
Risk
Amazon EC2
Rackspace
Windows Azure
41

Step 3 Rank the alternatives using VIKOR.
Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is a
Serbian term and it is a MCDM algorithm.
It undergoes five phases,
• Normalization
• Difference
• Weighted and Normalized distance,
• Combined weight
• Selection.
42

In this phase, the maximum and minimum value of the criteria is determined
mathematically.
The Maximum value of the beneficial criteria is marked by red and the
minimum value of the non- beneficial criteria is marked by violet
Sl
No.
Alternatives Benefits Opportu
nities
Cost Risk
1 Amazon EC2 0.248367 0.166850 0.156623 0.255299
2 Rackspace 0.114261 0.164199 0.156356 0.124611
3 Windows Azure 0.137372 0.168951 0.187021 0.120089
43

Sl
No.
Alternatives Benefits Opportu
nities
Cost Risk
1 Amazon EC2 0.248367 0.166850 0.156623 0.255299
2 Rackspace 0.114261 0.164199 0.156356 0.124611
3 Windows Azure 0.137372 0.168951 0.187021 0.120089
Normalize data in range (0, 1)
x = x/xmax for beneficial attributes
x = xmin/x for non- beneficial attributes
44

Sl
No.
Alternatives Benefits Opportuniti
es
Cost Risk
1 Amazon EC2 0.248367/0.2
48367
0.166850/0.1
68951
0.156356/0.1
56623
0.120089/0.2
55299
2 Rackspace 0.114261/0.2
48367
0.164199/0.1
68951
0.156355/0.1
56356
0.120089/0.1
24611
3 Windows Azure 0.137372/0.2
48367
0.168951/0.1
68951
0.156356/0.1
87021
0.120089/0.1
20089
45

Sl
No.
Alternatives Benefits Opportuniti
es
Cost Risk
1 Amazon EC2 1 0.9875 0.9982 0.6004
2 Rackspace 0.4600 0.9718 1 0.9637
3 Windows Azure 0.5531 1 0.8360 1
46

Sl
No.
Alternatives Benefits Opportunities Cost Risk
1 Amazon EC2 1 0.9875 0.9982 0.6004
2 Rackspace 0.4600 0.9718 1 0.9637
3 Windows Azure 0.5531 1 0.8360 1
4 Max 1 1 1 1
5 Min 0.4600 0.9718 0.8360 0.6004
6 Difference (Max-
Min)
0.54 0.0282 0.164 0.3996
Find the difference MAX-MIN
47

Sl No. Alternatives Benefits Opportunities Cost Risk
weight 0.5 0.167 0.25 0.083
1 Amazon EC2 1 0.9875 0.9982 0.6004
2 Rackspace 0.4600 0.9718 1 0.9637
3 Windows Azure 0.5531 1 0.8360 1
4 Max 1 1 1 1
5 Min 0.4600 0.9718 0.8360 0.6004
6 Difference(Max-
Min)
0.54 0.0282 0.164 0.3996
Assign weights to the criteria
48

Sl
No.
Alternatives Benefits Opportunities Cost Risk
Weight 0.5 0.167 0.25 0.083
1 Amazon EC2 1 0.9875 0.9982 0.6004
2 Rackspace 0.4600 0.9718 1 0.9637
3 Windows
Azure
0.5531 1 0.8360 1
4 Max 1 1 1 1
5 Min 0.4600 0.9718 0.8360 0.6004
6 Difference
(Max-Min)
0.54 0.0282 0.164 0.3996
Find weighted and normalized distance E.
MX- Maximum value, MN-Minimum value, N – Criteria value ,WNMD-Weighted
Normalized Distance value
49

Combined weight is calculated the minimum value of the weight is ranked 1
and as the value increases the rank increases
All the simulation have been performed by using MATLAB 2016a
Sl No. Alternatives Combined
Weight is
Calculated
Rank
1 Amazon EC2 0 1
2 Rackspace 0.8642 2
3 Windows Azure 1 3
50

Conclusion
• This work is done on the basis of Multi-Criteria Decision Making
algorithm for selecting the best cloud service provider by analyzing and
comparing different beneficial and non beneficial Quality of Service
requirement.
• Different cloud service selection model like AHP, Fuzzy TOPSIS, ANP,
VIKORE, MOORA, PROMETHEE and DEA have been reviewed. It has
been analyzed that most of the framework assigned weight to the service
attribute and assigned rank after processing and comparing the attribute.
• A comparison table is drawn based on the survey and a hybrid
algorithm is proposed to select the best cloud among 3 different CSP, which
combines ANP and VIKOR to select the cloud.
51

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