shree krishna 20140214

A Study on Behavior Mining of Cloud computing users

2014・ 02.14

Shree Krishna Shrestha

12054071Graduate School of Engineering

Muroran Institute of Technology, Muroran, Hokkaido, Japan

CONTENTS

IntroductionTest-bed Cloud System: Jyaguchi IntroductionProblem DefinitionAlgorithm Description : 1. TWSMA

2.RecommendationExperiment and ResultsConclusion

INTRODUCTION AND PURPOSE

Purpose: A framework to recommend service

Method to mine services, based on the behavior of service user Method to Recommend Services based on the result data of mining of

services

Jyaguchi: A cloud system proposed by Bishnu Prasad Gautam Based on service on demand and pay per use business model

TEST BED CLOUD SYSTEM :JYAGUCHI (OVERVIEW)

Jyaguchi is a SAAS based cloud that provides a platform to develop application as service with multi-language support.

component

Calculator Service Component

componentAddServiceComponent

component SubtractServiceComponent

component MultiplyServiceComponent

Component DivideServiceComponent

JavaScript

Ruby

Python

Groovy

Ref: As per the Definition of Inventor of Jyaguchi, Asst. Prof. Bishnu Prasad Guatam

Features Software as Service(SaaS) Distributed Resource ManagementPay per use Business Model Service on Demand

TEST BED CLOUD SYSTEM: JYAGUCHI

Logs the activity of users with in Interface

MAJOR ISSUE IN MINING OF SERVICES

What is difference between mining Item and Service?

Why current Item mining cannot be used for Service

mining?

Usage Time

Mining for frequent service usage pattern considering not only service usage frequency but also the service usage time

Service Mining

ALGORITHM FOR SERVICE MINING

Propose an algorithm for service mining which consider the time of service usage.

Time Weight Sequence Mining Algorithm (TWSMA)

Create Multi-dimensional Weighted Service Sequence Database

Mining Multi-dimensional Sequence

CREATION OF SERVICE WEIGHT INPUT SEQUENCE

Input: Service Usage logs; Unit time uOutput: Multi-dimensional Weighted Service Sequence Database (MDWSSDB)1: Calculate service usage time from service usage logs for each service on each position.2: Create Multi-dimensional service usage time sequence from service usage logs3: Calculate , Service Count, for each service on each position4: Calculate Absolute Service Weight, for each service on each position5: Calculate Relative Service Weight for each service on each position6: Make Weighted Sequence (ws) integrating service id sj with its Related Service Weight.7: Create MDWSSDB with integrating ws and associated user id.

CALCULATION OF RELATIVE SERVICE WEIGHT

Seq. id User_id Sequence

1 10 (2,6),(123,16),(456,31),(2,33),(456,35)

2 10 (2,21),(2,20),(2,22),(1,22),(2,21)

3 16 (2,1),(123,9),(456,1),(123,1),(456,15

4 15 (456,19),(456,24)(234,24),(456,43

5 15 (234,20),(234,11),(234,30),(456,38)

6 16 (456,19),(123,39),(456,30),(234,30)

Service Weight of service 2 for user 10,

ST2,10 = (6 + 33 + 21 + 20 + 22 + 21) min = 123 min

T10 = (6 + 16 + 31 +33+35+21+20+22+22+ 21) min = 227 min.

ASW2,10= 123/227 = 0.542For unit time (ut) 5 min, service usage count for service 2 at position 1 and sequence 1 is (SC2,1,1) = 6/5 = 1.2

RSW2,1,1 = 1.2 * 0.542 = 0.650

Multi-dimensional service usage time sequence

（ 456, 35)

Service ID

Use time

EXAMPLE OF INPUT SEQUENCE


1 10 (2,0.650),(123,0.224),(456,1.804),(2,3.577),(456,2.037)

2 10 (2,2.276),(2,2.168),(2,2.385),(1,0.427),(2,2.276)

3 16 (2,0.0014),(123,0.608),(456,0.089),(123,0.068),(456,1.344)

4 15 (456,2.253),(456,2.846)(234,1.954),(456,5.1)

5 15 (234,1.628),(234,0.895),(234,2.442),(456,4.507)

6 16 (456,1.702),(123,2.636),(456,2.688),(234,1.242)


1 10 (2,6),(123,16),(456,31),(2,33),(456,35)

2 10 (2,21),(2,20),(2,22),(1,22),(2,21)

3 16 (2,1),(123,9),(456,1),(123,1),(456,15

4 15 (456,19),(456,24)(234,24),(456,43

5 15 (234,20),(234,11),(234,30),(456,38)

6 16 (456,19),(123,39),(456,30),(234,30)

（ 456, 35)

Service ID

Use time

Calculation of service weights

（ 456, 2.037)

Service ID

Serviceweight

Jyaguchi log data

MINING MULTIDIMENSIONAL SEQUENCE

Input: Multi-dimensional Weighted Service Sequence Database: MDWSSDB; Minimum support min support

Output: The complete set of labeled frequent patterns1: Calculate sequence database weight SDW of MDWSSDB2: Calculate minimum weight Wm3: Call ModiedPrexSpan4: End if no frequent pattern is found or at end of database5: Form Projected Sequence Database6: Mine labeled frequent patterns from Projected Sequence

Database

MINING SEQUENTIAL PATTERN

Prefix Postfix 2 <_123,456,2,456>,<_2,2,1,2>,

<_123,456,123,456><2>-projected database

123 <_456,2,456>, <_456,123,456>, <_456,234> <123>-projected database

2,123 <_456,2,456>,<_456,123,456>,<_456> <2,123>-projected database

Service id : 1230.224+0.608+0.068+2.636Total weight of service id 123 :3.53

Total Database Weight (SDW )= (0.650+0.224+1.804+...+1.242)=49.83

Frequent Pattern : 123,456

For min_support 5%min_weight = 49.83*.05 = 2.49

Prefix Postfix123 <_456,2,456>,

<_456,123,456>, <_456,234>123,456 <_2,456>, <_123,456>

Seq. id

User_id

Sequence

1 10 (2,0.650),(123,0.224),(456,1.804),(2,3.577),(456,2.037)

2 10 (2,2.276),(2,2.168),(2,2.385),(1,0.427),(2,2.276)

3 16 (2,0.0014),(123,0.608),(456,0.089),(123,0.068),(456,1.344)

4 15 (456,2.253),(456,2.846)(234,1.954),(456,5.1)

5 15 (234,1.628),(234,0.895),(234,2.442),(456,4.507)

6 16 (456,1.702),(123,2.636),(456,2.688),(234,1.242)

MINING SEQUENTIAL PATTERN

Prefix Postfix 2 <_123,456,2,456>,<_2,2,1,2>,

<_123,456,123,456><2>-projected database

123 <_456,2,456>, <_456,123,456>, <_456,234> <123>-projected database

2,123 <_456,2,456>,<_456,123,456>,<_456> <2,123>-projected database

Prefix

<123, 456>

Seq. id

User_id

Sequence

1 10 (2,0.650),(123,0.224),(456,1.804),(2,3.577),(456,2.037)

2 10 (2,2.276),(2,2.168),(2,2.385),(1,0.427),(2,2.276)

3 16 (2,0.0014),(123,0.608),(456,0.089),(123,0.068),(456,1.344)

4 15 (456,2.253),(456,2.846)(234,1.954),(456,5.1)

5 15 (234,1.628),(234,0.895),(234,2.442),(456,4.507)

6 16 (456,1.702),(123,2.636),(456,2.688),(234,1.242)

For frequent service sequence<123; 456>

User_id 16 and * are found frequentfrom postfix database.

Postfix

<10>;<16>; <16>

Labeled frequent pattern(16; <123; 456>); (*,<123; 456>)

RECOMMENDATION OF SERVICES

Based on result labelled Frequent pattern from TWSMA Categorized for 3 user group

1. Anonymous Users/First time User Group,2. Registered Users group without Previous History of Service

Usage (don’t have current service usage log)., 3. Registered Users group with Previous History of Service Usage

(have current service usage log).

RECOMMENDING SERVICE

User_id Sequence support

10 2 3

* 234 3

15 234,456 2

* 456,234 2

16 456,123,456 2

* 2,123,,456 2

Frequent Patterns



10 2 3

* 234 4

15 234,456 2

* 456,234 2

16 456,123,456 2

* 2,123,,456 2

Frequent Patterns Anonymous Users Group

Services with highest support

Recommended Service: 234



10 2 3

* 234 4

15 234,456 2

* 456,234 2

16 456,123,456 2

* 2,123,,456 2

Frequent Patterns First Time User user_id 14

Services with highest support




10 2 3

* 234 4

15 234,456 2

* 456,234 2

16 456,123,456 2

* 2,123,,456 2

Frequent Patterns Authorized User 10

Services with highest support of that user




10 2 3

* 234 4

15 234,456 2

* 456,234 2

16 456,123,456 2

* 2,123,,456 2

Frequent PatternsAuthorized User 15 and has used

service 234

Next service from the frequent pattern with highest support




10 2 3

* 234 3

15 234,456 2

* 456,234 2

16 456,123,456 2

* 2,123,,456 2

Frequent PatternsLogged in user 16 who has used

service 2,456,123

- This sequence is not in frequent pattern- Drop 2 and search from remaining sequence. i.e. 456, 123


EXPERIMENTS (TWSMA)

Experiment MethodologyImplemented on Jyaguchi systemUsed actual log of Jyaguchi UsersVaried minimum support to find variation in No. of

patterns found and processing time.Comapred No. of patterns found and processing time

with seq-dim algorithm.

EXPERIMENT RESULTS (1)

• No. of patterns and Process time with no. of sequences for varied minimum support


• No. of patterns and Process time with no. of sequences for varied minimum support

EXPERIMENTS (TWSMA)

Precision and Recall based evaluation Experiment Methodology

Learning Phase: Find frequent services from log data of prior to implementing TWSMA algorithm with

various minimum support did an online survey among Jyaguchi Users about the favorite services. found common services in between survey data and frequent services for various

minimum support which is used as relevant services. Evaluation Phase

Users Use Jyaguchi system where services are recommended from 3 algorithms: 1. TWSMA, 2. SEQ-DIM and 3. Random

Calculate Precision and Recall for each user. Take average of Precision and Recall for various minimum support.


Comparision of Precision and recall for Various minimum support for 3 algorithm

Minimum_support:10%Minimum_support:7%


Comparision of Precision and recall for Various minimum support for 3 algorithm

Minimum_support:12% Minimum_support:15%

CONCLUSION AND FUTURE WORKS

• proposed a framework for recommending services utilizing service usage time as service weight.

• Implemented the algorithm in the Jyaguchi System.• Evaluated the proposed framework on Jyaguchi System.

• Implement and evaluate algorithm on other SAAS based Cloud system.• Add the dimension of user profile for better recommendation

Future Tasks

Conclusion

Thank you