This document describes a study on behavior mining of cloud computing users. It proposes an algorithm called Time Weighted Sequence Mining Algorithm (TWSMA) to mine service usage patterns from log data, considering both service usage frequency and time. TWSMA creates a multi-dimensional weighted service sequence database, then mines it to find frequent sequential patterns. The patterns are used to recommend services to different user groups. Experiments on a test cloud system called Jyaguchi show that TWSMA has higher precision and recall than other algorithms for service recommendation. Future work could include implementing the algorithm on other cloud systems and adding user profile dimensions.

1. A Study on Behavior Mining of Cloud
computing users

Shree Krishna Shrestha
12054071
Graduate School of Engineering
Muroran Institute of Technology, Muroran, Hokkaido, Japan
2014・02.14

2. CONTENTS
 Introduction
 Test-bed Cloud System: Jyaguchi Introduction
 Problem Definition
 Algorithm Description : 1. TWSMA
2.Recommendation
 Experiment and Results
 Conclusion

3. 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

4. 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
AddService
Component
component
Calculator
Service
Component
JavaScript
component
SubtractService
Component
Ruby
component
MultiplyService
Component
Python
Component
DivideService
Component
Groovy
Ref: As per the Definition of Inventor of Jyaguchi, Asst. Prof. Bishnu Prasad Guatam
Features
 Software as
Service(SaaS)
 Distributed
Resource
Management
Pay per use
Business Model
 Service on
Demand

5. TEST BED CLOUD SYSTEM: JYAGUCHI

Logs the activity of users with in Interface

6. 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
Service Mining
Mining for frequent service usage pattern considering not only service usage
frequency but also the service usage time

7. 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

8. CREATION OF SERVICE WEIGHT INPUT
SEQUENCE
Input: Service Usage logs; Unit time u
Output: 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 logs
3: Calculate , Service Count, for each service on each position
4: Calculate Absolute Service Weight, for each service on each position
5: Calculate Relative Service Weight for each service on each position
6: Make Weighted Sequence (ws) integrating service id sj with its Related
Service Weight.
7: Create MDWSSDB with integrating ws and associated user id.

9. CALCULATION OF RELATIVE SERVICE WEIGHT
Multi-dimensional service usage time sequence
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 +
（456, min
22 + 21) min = 12335)
T10 = (6 + 16 + 31
Service
Use time
+33+35+21+20+22+22+ 21)
ID
min = 227 min.
ASW2,10= 123/227 = 0.542
For 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

10. EXAMPLE OF INPUT SEQUENCE
Jyaguchi log data
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)
（456, 35)
Service
ID
Use time
Calculation of service weights
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)
（456, 2.037)
Service
ID
Service
weight

11. MINING MULTIDIMENSIONAL SEQUENCE
 Input: Multi-dimensional Weighted Service Sequence







Database: MDWSSDB; Minimum support min support
Output: The complete set of labeled frequent patterns
1: Calculate sequence database weight SDW of MDWSSDB
2: Calculate minimum weight Wm
3: Call ModiedPrexSpan
4: End if no frequent pattern is found or at end of database
5: Form Projected Sequence Database
6: Mine labeled frequent patterns from Projected Sequence
Database

12. MINING SEQUENTIAL PATTERN
Total Database Weight (SDW )= (0.650+0.224+1.804+...+1.242)=49.83
For min_support 5%
min_weight = 49.83*.05
= 2.49
Service id : 123
0.224+0.608+0.068+2.636
Total weight of service id 123 :3.53
User_i
Sequence
d
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),(123,0.0
68),(456,1.344)
Prefix
Postfix
4
15
(456,2.253),(456,2.846)(234,1.954),(456,5.
Prefix
Postfix
2
<_123,456,2,456>,<_2,2,1,2>,
1) <2>-projected database
<_123,456,123,456>
123
<_456,2,456>,
5
15
(234,1.628),(234,0.895),(234,2.442),(456,4.
<_456,123,456>, <_456,123,456>, <_456,234> 507)
123
<_456,2,456>, <_456,234>
<123>-projected database
6
16
(456,1.702),(123,2.636),(456,2.688),(234,1.
123,456 <_2,456>, <_123,456>
242)
2,123
Seq.
id
1
<_456,2,456>,<_456,123,456>,<_456>
Frequent Pattern : 123,456
<2,123>-projected database

13. MINING SEQUENTIAL PATTERN
For frequent service sequence<123; 456>
User_i
Sequence
d
10
(2,0.650),(123,0.224),(456,1.804),(2,3.577),
<123,
(456,2.037)
456>
2
10
(2,2.276),(2,2.168),(2,2.385),(1,0.427),(2,2.
276)
User_id 16 and * are found frequent
3
16
(2,0.0014),(123,0.608),(456,0.089),(123,0.0
68),(456,1.344)
from postfix database.
4
15
(456,2.253),(456,2.846)(234,1.954),(456,5.
1)
Prefix
Postfix
15
(234,1.628),(234,0.895),(234,2.442),(456,4.
2
<_123,456,2,456>,<_2,2,1,2>, 5
<2>-projected database
507)
<_123,456,123,456>
6
16
(456,1.702),(123,2.636),(456,2.688),(234,1.
123
<_456,2,456>, <_456,123,456>, <_456,234> 242)
<123>-projected database
Prefix
2,123
Labeled frequent
pattern
<10>;<16 (16; <123; 456>);
(*,<123; 456>)
>; <16>
Postfix
Seq.
id
1
<_456,2,456>,<_456,123,456>,<_456>
<2,123>-projected database

14. RECOMMENDATION OF SERVICES
 Based on result labelled Frequent pattern from TWSMA
 Categorized for 3 user group
1.
2.
3.
Anonymous Users/First time User Group,
Registered Users group without Previous History of Service
Usage (don’t have current service usage log).,
Registered Users group with Previous History of Service Usage
(have current service usage log).

15. RECOMMENDING SERVICE
Frequent Patterns
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

16. RECOMMENDING SERVICE
Frequent Patterns
User_id
Anonymous Users Group
Sequence
support
10
2
3
*
234
4
15
234,456
2
*
456,234
2
16
456,123,456
2
*
2,123,,456
2
Services with highest support
Recommended Service: 234

17. RECOMMENDING SERVICE
Frequent Patterns
User_id
First Time User user_id 14
Sequence
support
10
2
3
*
234
4
15
234,456
2
*
456,234
2
16
456,123,456
2
*
2,123,,456
2
Services with highest support
Recommended Service: 234

18. RECOMMENDING SERVICE
Frequent Patterns
User_id
Authorized User 10
Sequence
support
10
2
3
*
234
4
15
234,456
2
*
456,234
2
16
456,123,456
2
*
2,123,,456
2
Services with highest support of that
user
Recommended Service: 2

19. RECOMMENDING SERVICE
Frequent Patterns
User_id
Sequence
support
10
2
3
*
234
4
15
234,456
456,234
2
16
456,123,456
2
*
2,123,,456
2
Next service from the frequent pattern
with highest support
2
*
Authorized User 15 and has used
service 234
Recommended Service: 456

20. RECOMMENDING SERVICE
Frequent Patterns
User_id
Sequence
support
10
2
3
*
234
3
15
234,456
2
*
456,234
456,123,456
2
*
2,123,,456
2
- This sequence is not in frequent
pattern
- Drop 2 and search from remaining
sequence. i.e. 456, 123
2
16
Logged in user 16 who has used
service 2,456,123
Recommended Service: 234

21. EXPERIMENTS (TWSMA)
 Experiment Methodology
 Implemented on Jyaguchi system
 Used actual log of Jyaguchi Users
 Varied 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.

22. EXPERIMENT RESULTS (1)
• No. of patterns and Process time with no. of sequences for
varied minimum support

23. EXPERIMENT RESULTS (3)
• No. of patterns and Process time with no. of sequences for
varied minimum support

24. 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.

25. EXPERIMENT RESULTS (3)
Comparision of Precision and recall for Various minimum support for 3 algorithm
Minimum_support:7%
Minimum_support:10%

26. EXPERIMENT RESULTS (4)
Comparision of Precision and recall for Various minimum
support for 3 algorithm
Minimum_support:12%
Minimum_support:15%

27. CONCLUSION AND FUTURE WORKS
Conclusion
• 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.
Future Tasks
• Implement and evaluate algorithm on other SAAS based Cloud system.
• Add the dimension of user profile for better recommendation

28. Thank you