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iv

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KCSE 2015 17 1

v

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Mesfin Abebe, ()

KCSE 2015 17 1

vi

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KCSE 2015 17 1

vii

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KCSE 2015 17 1

viii

1 (C1)

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KCSE 2015 17 1

ix

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x

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1 API

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KCSE 2015 17 1

xi

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KCSE 2015 17 1

KCSE 2015

1 28 ()

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() :1

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13:30-15:00

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* 11:20

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KCSE 2015 17 1

B

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C

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1 30 ()

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KCSE 2015 17 1

E

E1:

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KCSE 2015 17 1

T-1

KCSE 2015

: 1 28() 13:30~15:00

: 1

: MBTI SW

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SW , MBTI

. MBTI(Myers-Brigs Type Indicator) C. G. Jung

Katherine Cook Briggs Isabel Briggs Myers

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T-2

: 1 28() 13:30~15:00

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KCSE 2015 17 1

T-5

: 1 28() 15:15~16:45

: 2

: Architecture for Smart Services and Systems

: ()

:

Being Smart in various types of services and systems becomes a mega-trend in our daily life. The

product development for this trend is accelerated by the wide availability of sensors and IoT devices.

That is, it becomes quite feasible to monitor various objects in our environment, store the gathered

contexts in the Big Data scheme, inferring situations and future indicators by applying contexts analytics,

and perform situation-specific services. The net benefit of these activities is the augmented smartness

of our daily activities.

In this lecture, we will explore three topics for smart services and systems; 1) What is smartness in

services and systems, 2) Technology of Sensors and IoT devices, 3) Requirements and Architectural

Solutions for Smart Services and Systems, and 4) a Case Study of Developing a Smart Personal

Healthcare service.

:

Missouri Computer Science , University of Iowa

Computer Science . .

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T-6

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mailto:Muhui.Han@hyundai-autron.com

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KCSE 2015 17 1

1

1 (A2)

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KCSE 2015 17 1

2

* (Quality Based Software Project Staffing and Scheduling with Time Bound)

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291 {dwseo, donghwan, bae}@se.kaist.ac.kr

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KCSE 2015 17 1

3

3.3 . 1 , . . . 4. 80 ,

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1

(#, #)

Duration

(days) Quality Score

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Set 1 -10 (18, 10) 206.9 0.59(1) 43% 0.73(1.23) Set 11-20 (32, 10) 278.5 0.53(1) 77% 0.64(1.22) Set 21-30 (32, 15) 252.6 0.49(1) 68% 0.64(1.31) Set 31-40 (32, 20) 293.9 0.45(1) 58% 0.64(1.42) Set 41-50 (62, 10) 444.6 0.46(1) 99% 0.58(1.25) Set 51-60 (62, 15) 343.9 0.41(1) 92% 0.51(1.25) Set 61-70 (62, 20) 410.4 0.37(1) 96% 0.49(1.34) Set 71-80 (92, 15) 474.3 0.37(1) 99% 0.48(1.30)

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41

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5. [5]. [7], . 6.

. , . . . , , DAM(Defect Amplification Model) . [1] R.-G. Ding and X.-H. Jing, Five Principles of Proj-

ect Management in Software Companies, Project Management Technology, vol. 1, 2003.

[2] Bellenguez-Morineau, Odile, and Emmanuel N_eron. A branch-and-bound method for solving multi-skill project scheduling problem., RAIRO-Operations Research, vol.41, no.2, pp. 155-170, 2007

[3] Jihun Park, Dongwon Seo, Gwangui Hong, Donghwan Shin, Jimin Hwa, Doo-Hwan Bae, Practical Human Resource Allocation in Software Projects Using Genetic Algorithm, 26th International Conference on Software Engineering and Knowledge Engineering, pp. 688-694, 2014.

[4] , , , , , , , , : , 40, 7, pp.369-380, 2013 7.

[5] Peixoto, Daniela CC, Geraldo R. Mateus, and Rodolfo F. Resende, The Issues of Solving Staffing and Scheduling Problems in Software Development Projects, Computer Software and Applications Conference, 2014.

[6] Constantinos Stylianou, Andreas S. Andreou, A Multi objective Genetic Algorithm for Software Development Team Staffing Based on Personality Types, Artificial Intelligence Applications and Innovations, Springer Berlin Heidelberg, 2012.

KCSE 2015 17 1

4

ISO/IEC 9126

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206 mylee@ajou.ac.kr, jungwony@ajou.ac.kr

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8

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9

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10

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KCSE 2015 17 1

11

.

,

.

.

[1] IEEE, IEEE Std 1012 - IEEE standard for software verification and validation, IEEE Standards, 2005

[2] ISO/IEC, IEEE, ISO/IEC 12207, IEEE Std 12207 Systems and software engineering software life cycle processes, ISO/IEC, 2008

[3] ISO/IEC, IEEE, ISO/IEC 15288, IEEE Std 15288 - Systems and software engineering system life cycle processes, ISO/IEC, IEEE Standards, 2008

[4] ISO/IEC, ISO/IEC 9126-1 Software engineering-product quality-part 1: quality model, ISO/IEC Technical Report, 2001

[5] ISO/IEC, ISO/IEC 9126-2 Software engineering-product quality-part 2: external metrics, ISO/IEC Technical Report, 2003

[6] ISO/IEC, ISO/IEC 9126-3 Software engineering-product quality-part 3: internal metrics, ISO/IEC Technical Report, 2003

[7] ISO/IEC, ISO/IEC 9126-4 Software engineering-product quality-part 4: quality in use metrics, ISO/IEC Technical Report, 2004

[8] X. Chen, Extraction and visualization of traceability relationships between documents and source code, in Proceedings of the IEEE/ACM International Conference on Automated Software Engineering(ASE10), pp.505-510, 2010

[9] K. Jaber, B. Sharif, C. Liu, A Study on the Effect of Traceability Links in Software Maintenance, IEEE Access, Vol.1, pp.726-741, 2013

[10] V. Leino, Documenting Requirements Traceability Information - A Case Study, Masters Thesis, Helsinki University of Technology, 2001

[11] J.I. Maletic, M.L. Collard, B. Simoes, An XML-based approach to support the evolution of model-to-model traceability links, in Proceedings of the 3rd International Workshop on Traceability in Emerging Forms of Software Engineering(TEFSE05), pp.67-72, 2005

[12] A. Ahmad, M.A. Ghazali, Documenting requirements traceability information for small projects, in Proceedings of IEEE International Multitopic Conference(INMIC07), pp.1-5, 2007

[13] , ,

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34, 5, pp.419-430, 2007

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, 17 , 5 , pp.115-126, 2012

[15] , ,

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, 2, 7, pp.461-470, 2013 [16] Major Artifacts of the RUP(Rational Unified

Process), http://sce.uhcl.edu/helm/rationalunifiedprocess/

[17] MIL-STD-498 Software development and documentation, Military Standard, 1994

[18] ,

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KCSE 2015 17 1

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KCSE 2015 17 1

15

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16

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17

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18

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19

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[1] Dart, Susan. "Concepts in configuration

management systems." Proceedings of the 3rd international workshop on Software configuration management. ACM, 1991.

[2] Gotel, Orlena CZ, and Anthony CW Finkelstein. "An analysis of the requirements traceability problem." Requirements Engineering, 1994., Proceedings of the First International Conference on. IEEE, 1994.

[3] Aiello, Robert, and Leslie Sachs. Configuration Management Best Practices: Practical Methods that Work in the Real World. Pearson Education, 2010.

[4] IEEE Standard for Configuration Management in Systems and Software Engineering, The Institute of Electrical and Electronics Engineers, 2012

[5] TTA Standard for Software Configuration Management Plans, TTA. IE-828,

, 1999. [6] McMillan, Collin, Denys Poshyvanyk, and

Meghan Revelle. "Combining textual and structural analysis of software artifacts for traceability link recovery." Traceability in Emerging Forms of Software Engineering, 2009.

TEFSE'09. ICSE Workshop on. IEEE, 2009. [7] , et al. " Pain chain

."

38.1A (2011): 206-209. [8] , and . "

." : 34.5 (2007): 419-430.

[9] , , and . "

." D 17.4 (2010): 271-282.

[10] , et al. "

."

34.1B (2007): 28-32.

KCSE 2015 17 1

20

KAIST S/W

28 25

boytoman@kaist.ac.kr

LG

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kwangimoon@kaist.ac.kr

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.

KCSE 2015 17 1

21

4

5

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.

.

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.

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49%

,

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,

31%

KCSE 2015 17 1

22

(Scori

ng Method)

-

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,

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)

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.

KCSE 2015 17 1

23

.

[ 4]

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80%

.

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3

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4

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5 Smart TV

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5 SNS

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7 1.3 0.05 1.4

2)

Code

.

code

,

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(%)

,

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.

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3)

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.

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= 10

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0 20 40 60 80 100

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Branch

KCSE 2015 17 1

24

Code .

PL

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.

=

10

5)

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.

10 .

[ 4]

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11 code

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.

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.

,

.

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.

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.

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10 5 10 2.5

(70) 42 64 47 35.5

[ 8] ()

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.

5

KCSE 2015 17 1

25

2

.

5.1

6.536 .

. ( 9)

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[1]

, , 2000.

[2] ,

, 2011 12 .

[3] Using Dependency Models to Manage Complex

Software Architecture - Neeraj Sangal, Ev Jordan,

Vineet Sinha, Daniel Jackson, ACM Sigplan

Notices 2005.

[4] Structured Testing: A Testing Methodology

Using the Cyclomatic Complexity Metric, NIST

Special Publication 500-235, Arthur H. Watson,

September 1996.

[5] McCabe (December 1976). "A Complexity

Measure". IEEE Transactions on Software

Engineering: 308-320.

KCSE 2015 17 1

26

(A3)

, (KAIST)

, , (KAIST)

Open API RESTful

, , , ()

Cross-cutting SLA

, , , , , ()

KCSE 2015 17 1

27

,

291 {Hyeonjun.jo, iko}@kaist.ac.kr

: . . , .

: , , , , 1.

(Internet of Things, IoT) , (Computing Task) . , (Service Oriented Architecture, SOA) [1][2].

, , (Service Discovery) . : -

, , [3] -

, [4]

, . , , . , . , .

KCSE 2015 17 1

28

1

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. -

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, .

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SOA . , , (Overhead) . , . , . SOA [6]. SOA

. (Registry) (Centralized) (Distributed) . (Single point of failure) . , SOA . (Gateway)

KCSE 2015 17 1

29

. , .

, . . , .

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.

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. . 2.

2 [7]

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. . , . . . . . , [8].

Cetralized Distributed

Backbone-

basedCluster-based DHT-based

KCSE 2015 17 1

30

[7]. 2.2

. (Backbone) . . . [9][10].

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Hash Table, DHT) . . .

, . . GPS . [13][14]. 2.3

. . . [6].

(Counting Bloom Filter) . , [15].

. ,

KCSE 2015 17 1

31

.

3.

3 . (Physical layer) . , . . . , . .

3

(Logical layer)

, . , , . . , . (Composite

Service Layer) , , , . , .

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. , .

, . . , . .

KCSE 2015 17 1

32

4

4.2

5 . , (Hash) . m . 5 x, y 1 , z 0 . . .

.

5

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1

.

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Rule 2 4 5 2 A2

KCSE 2015 17 1

33

. 1 . (ruleMode) , , . . . , . , . 4.4 , . . . .

1 Network Simulator-3: http://www.nsnam.org/

5. 5.1 ns31 . ns3

. C++ , , , .

. . , . ,

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KCSE 2015 17 1

34

. . , . 1% , 7 . 7 .

. , UDP (User Datagram Protocol) . (Handshaking), , TCP (Transport Control Protocol) UDP UDP .

(Hop) . . .

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50 , 0.01 . (Random walk) . 6 10 , 10 , 30 . 5 .

6

. 10 .

KCSE 2015 17 1

35

7

7 10~50

. 50 100% , 50 .

8

. 30% 8 . 30% 5% ~ 10%

. 7 . 6.

, . .

. , .

. , . (Semantic) . Acknowledgement (Dual Use Technology Program) (UM13018RD1).

KCSE 2015 17 1

36

[1]

Albert D.D., Garstka, J.J, Stein, F.P, Network Centric Warfare : Developing and Leveraging Information Superiority, CCRP Publ, 2nd Edition Aug 1999

[2] Zhang, Y., Liu, X., Wang, Z., & Chen, L. A Service-Oriented Method for System-of-Systems Requirements Analysis and Architecture Design. Journal of Software, 7(2), 358365. 2012

[3] Zhu, Fen, Matt W. Mutka, and Lionel M. Ni. "Service discovery in pervasive computing environments." IEEE Pervasive computing 4.4 (2005): 81-90.

[4] Bettstetter, Christian, and Christoph Renner. "A comparison of service discovery protocols and implementation of the service location protocol. "Proceedings of the 6th EUNICE Open European Summer School: Innovative Internet Applications. 2000.

[5] Kozat, Ulas C., and Leandros Tassiulas. "Network layer support for service discovery in mobile ad hoc networks." INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies. Vol. 3. IEEE, 2003.

[6] Sailhan, Francoise, and Valerie Issarny. "Scalable service discovery for MANET." Pervasive Computing and Communications, 2005. PerCom 2005. Third IEEE International Conference on. IEEE, 2005.

[7] Ververidis, Christopher N., and George C. Polyzos. "Service discovery for mobile ad hoc networks: a survey of issues and techniques. " Communications Surveys & Tutorials, IEEE 10.3 (2008): 30-45.

[8] Zhang, Wenyu, et al. "A reputation-based peer-to-peer architecture for semantic service discovery in distributed manufacturing environments. " Concurrent Engineering (2012):

[9] U. C. Kozat and L. Tassiulas, Service Discovery in Mobile Ad Hoc Networks: An Overall Perspective on Architectural Choices and Network Layer Support Issues, Ad Hoc Networks, vol. 2, no. 1, 2004, pp. 2344.

[10] F. Sailhan and V. Issarny, Scalable Service Discovery for MANET, Proc. 3rd IEEE Intl. Conf. Pervasive Computing and Commun. (PerCom 2005), Kauai Island, Hawaii, Mar. 812, 2005.

[11] M. Klein, B. Knig-Ries, and P. Obreiter, Service Rings A Semantic Overlay for Service Discovery in Ad Hoc Networks, Proc. 6th Intl. Wksp. Network-Based Information Systems (NBIS 2003) at DEXA 2003, Prague, Sept. 2003.

[12] G. Schiele, C. Becker, and K. Rothermel, Energy-Efficient Cluster-Based Service Discovery for Ubiquitous Computing, Proc. 11th ACM SIGOPS European Wksp., Leuven, Belgium, Sept. 2004.

[13] Sivavakeesar, Siva, Oscar F. Gonzalez, and George Pavlou. "Service discovery strategies in ubiquitous communication environments." Communications Magazine, IEEE 44.9 (2006): 106-113.

[14] Tyan, Jerry, and Qusay H. Mahmoud. "A comprehensive service discovery solution for mobile ad hoc networks." Mobile Networks and Applications 10.4 (2005): 423-434.

[15] Cheng, et al "An efficient service discovery algorithm for counting bloom filter-based service registry." Web Services, 2009. ICWS 2009. IEEE International Conference on. IEEE, 2009.

KCSE 2015 17 1

37

, ,

291 {sonjoosik, kohangyu, iko}@kaist.ac.kr

: , . , . , , . , , . , . : , , 1.

Mark Weiser [1], TV, , (Consumer Electronics) , . (Actuation Capability) 1 http://www.netflix.com/

[2]. ,

/ (Internet of Things, IoT) , (Capability) [2].

[3].

, . / . , Netflix1, Amazon.com2 [4].

. . Amazon.com

2 http://www.amazon.com/

KCSE 2015 17 1

38

Netflix (Rating) .

. , , , .

/ . , Stochastic Gradient Descent(SGD) . . , 2

3 . 4 , 5 . 2.

2.1

, , , (Cold Start)

3 http://www.youtube.com/

. Cai[6] (Object Typicality)

. , , . , .

Yu, Qi[7] , . (Matrix Factorization) , (Latent Feature) (Decision Tree) . . Quality-

of-Service(QoS) . QoS , . QoS , Zheng[8][9] Youtube3 Wikipedia4 WSRec . QoS , WSRec QoS , . , IP QoS .

. , . 2.2 (Context-aware)

, .

4 http://www.wikipedia.org/

KCSE 2015 17 1

39

, 2 (Rating) , , (Context-aware) .

(Context) Palmisano[10] . Pre-filtering, Post-filtering, Contextual Modeling , (2009, 2014) .

Dou[11] . Term Frequency-Inverse Document Frequency(TF-IDF) , . ,

. .

2.3

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, . , RFID .

TASKREC[12] . . ,

. , , , .

3. 3.1

(Rating) [13]. . 2 , .

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. , . . 5 , . , , ,

, , = ( ) . 5 .

, =1(, 0)0(, 0) (1) , = , (2) , = log , (3) , = log , (4)

, = log , (5)

KCSE 2015 17 1

40

1 5 (1), (2), Log2 (3), Log10 (4), Ln (5) . . , . , Log , . . . 3.2

1

. , , (Similarity) (Threshold)

[13]. (Euclidean Similarity), (Pearson Correlation) , [14]. , , , [13]. Singular Value Decomposition(SVD), Matrix Factorization(MF), SGD . .

SGD . SGD , , MF SVD (Overfit Problem) . , SGD .

, , . , . , . 1 . , , , , .

()

String 100%

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Double 27.56%

KCSE 2015 17 1

41

, , .

, SGD , . , , , , , , . Mean-Absolute-

Error(MAE), Root-Mean-Square-Error(RMSE), Normalized Discounted Cumulative Gain(NDCG), Average Precision , RMSE . 2 , , , RMSE .

RMSE = 1(, ,), ( 6 )

.

4. 4.1 4.1.1

KAIST , ,

, , , , ,

2

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19 . 3 157 , . 1259 , 131 . (Density) 20.89%. 8 , 1 . 4.1.2

.

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KCSE 2015 17 1

42

. 2, 10, (e) .

GraphChi 5 . GraphChi Generalized Stochastic Gradient Descent(GenSGD) 5 RMSE .

(Context-Aware Information) . 5 , RMSE ( 6 ) Log10 , . 3.2 (Dimension) , 3 . 8 , 8 3 . , . 3 5 , 4 , 5 . RMSE 5 , 10 . 13 GenSGD RMSE 4.2 , .

4.2 4.2.1

5

, . 3.1 . 3

RMSE .

(7)

0 1

. 5

5 https://github.com/GraphChi

.

RMSE(normalized) 0.00813553

0.200865111 (Log2) 0.195554888 (Log10) 0.195278669 (Loge) 0.195509674

3 RMSE PredictionMAX = , PredictionMIN =

= (7) 3

RMSE . . 1 1 100 2 3 1 . RMSE .

RMSE 0.0053, 0.0056 . . . . 2

RMSE . , (Log2), (Log10), (Loge Ln) 5 . 2 RMSE .

KCSE 2015 17 1

43

2

4.2.2 4.2.1

. (Baseline) . 5 , .

(1) 3

. , , 3 RMSE RMSE . () , . , . RMSE , . .

3 RMSE (2) 4

. , 2 RMSE RMSE . , . RMSE . 4.2.2.1 .

4 RMSE

KCSE 2015 17 1

44

(3) 5

. , , . , . 4 , RMSE , . , . , .

5 RMSE (4) 6

. , , RMSE 8 ( ) RMSE . , (Log10) . , .

6 RMSE

(5) 7 4

. , RMSE , . RMSE

0.169962161 , RMSE 0.188538578 . , . , .

7 RMSE

KCSE 2015 17 1

45

RMSE 0.1952786695

0.1932457681 0.1926726276 0.1692843141

0.1770938948

0.1957698052

0.1930742341 0.1931605750

0.1938819106 0.1904190914 0.1799608724

0.1934773709

, , 0.1832971773

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4

RMSE 4.3

, . KAIST , . , . ,

. , . . . 5.

.

. , , . , .

. , . KAIST , . , . ,

. , , .

. , , .

Acknowledgement

. [B0101-14-0334, IoT ]

KCSE 2015 17 1

46

[1] Weiser, M., The computer for the 21st century, ACM SIGMOBILE Mobile Computing and Communications Review, Vol. 3, Issue 3, pp. 311, 1991.

[2] Atzori, L., Iera, A., & Morabito, G. The Internet of Things: A survey. Computer Networks, 54(15), 2010.

[3] Vo, C., Torabi, T., and Loke, S. W., Task-Oriented Systems for Interaction with Ubiquitous Computing Environments, In Proceedings of the 7th ICST Conference on Mobile and Ubiquitous Systems (MobiQuitous 2010), December 6-9, Sydney, Australia, pp. 332-339, 2010.

[4] Fleder, D. M., and Hosanagar, K., Recommender systems and their impact on sales diversity, in Proceedings of the 8th ACM conference on Electronic commerce, pp.192-199, 2007.

[5] Kawsar, F. et al. 2009. Design and implementation of a framework for building distributed smart object systems. The Journal of Supercomputing. Vol. 54, Issue 1, pp. 428, 2009.

[6] Cai, Yi, et al. "Object typicality for effective Web of Things recommendations." Decision Support Systems 63: 52-63, 2014.

[7] Yu, Qi. "Decision tree learning from incomplete qos to bootstrap service recommendation." Web Services (ICWS), 2012 IEEE 19th International Conference on. IEEE, 2012.

[8] Zheng, Zibin, et al. "Qos-aware web service recommendation by collaborative filtering." Services Computing, IEEE Transactions on 4.2: 140-152, 2011.

[9] Chen, Xi, Zibin Zheng, and Michael R. Lyu. "QoS-Aware Web Service Recommendation via Collaborative Filtering." Web Services Foundations. Springer New York, 563-588, 2014.

[10] Palmisano, Cosimo, Alexander Tuzhilin, and Michele Gorgoglione. "Using context to improve predictive modeling of customers in personalization applications." Knowledge and Data Engineering, IEEE Transactions on 20.11: 1535-1549, 2008.

[11] Dou, Wanchun, Xuyun Zhang, and Jinjun Chen. "KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Application." IEEE Transactions on Parallel and Distributed Systems, 2014.

[12] Vo, Chuong Cong, et al. "TASKREC: a task-based user interface for smart spaces."

Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia. ACM, 2011.

[13] Adomavicius, Gediminas, and Alexander Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering, Vol. 17, Issue .6, pp.734-749, 2005.

[14] P. Resnick, N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl, GroupLens: An Open Architecture for Collaborative Filtering of Netnews, Proc. 1994 Computer Supported Cooperative Work Conf., 1994.

KCSE 2015 17 1

47

Open API

RESTful

*, *, *, **

* , ** 30

{ anstn088, mj.deff, kaluas, yeom}@pusan.ac.kr

: Open API . 3.0 Open API . Open API . Open API Open API . . Open API RESTful . . : Open API, , , RESTful 1.

Open API API(Application Programming Interface) . 3.0 . Open API

. Open API REST, SOAP, RSS, ATOM . REST Open API . SOAP 6.9% REST 93.1% [1]. Open API Open API . Open API Input output , . Open API Open API Q&A F&Q . . . RESTful RESTful . . 2 REST . 3 RESTful . , . . 4 .

" IT " (NIPA-2013-(H0301-13-1012))

KCSE 2015 17 1

48

2. 2.1 Open API

Open API SOAP, REST, RSS, ATOM Application Programming Interface. Open API Raw , . Open API , API . .

2.2

, . SOAP(Simple Object Access Protocol) , REST(REpresentational State Transfer) . WSDL(Web Service Description

Language) SOAP , REST . Open API REST .

2.3 REST

REST ROA(Resource

Oriented Architecture) . REST URI HTTP . GET, PUT, POST, DELETE , URI . SOAP Open API SOAP . Open API ProgrammableWeb[2] . 1 Open API . REST . REST Open API .

1 Open API

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[5] REST Open API . Open API . 3. RESTful

Open API . 2 Open API

KCSE 2015 17 1

49

3 . Open API . ->API . API . . .

3.1 . 3.2 . 3.3 . 3.4 3.2 .

2

3.1 RESTful Open API .

l Service: Service REST Open API

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l Input: Open API response. Input . mandatory Input . Input repeat .

3

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xls . Reference Input . Path .

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l Data: Data Input, Output . type Open API

KCSE 2015 17 1

50

. Name .

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3.3

RESTful . 6 Input .

KCSE 2015 17 1

51

6

. 7 . Input Output Data Input .

7 1

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8 2

3.4

3.1 -> API 3.2 . 9 .

9

id serviceId Id . s01 -> API id s02 . Service NextInput Input Service Output Input . Link StartService EndService StartService id EndService id . Input .

10

KCSE 2015 17 1

52

10 . API . 4.

Open API

REST REST Open API RESTful .

RESTful RESTful . .

Output Input . .

[1] , http://www.data.go.kr/ [2] programmableweb, http://www.programma

bleweb.com/ [3] , , REST

, , 11 , 9, pp. 79-92, 2013

[4] , Open API , , pp. 115-126, 2012

[5] , , , , Mash-Up Open API , pp. 954-95, 2012

[6] , , , , , OpenAPI , , 39, 1, pp 71-73, 2012

[7] , Open API Mashup , Journal of The Korea Society of Computer and Information, Vol. 17, No. 5, 2012

[8] X. Liu, Y. Hui, W. Sun and H. Liang, Towards Service Composition Based on Mashup, IEEE International Conference on Services

Computing, pp. 332-339, 2007. [9] X. Zhao, et al, RESTful web service

composition: extracting a process model from linear logic theorem proving, In 7th International Conference on Next Generation Web Services Practices, pp. 398-403, 2011.

[10] Cesare Pautasso, RESTful Web service composition with BPEL for REST, Data & Knowledge Engineering, pp. 851-866, 2009

KCSE 2015 17 1

53

Cross-cutting

SLA

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2014 ()

(No. NRF-2013R1A2A2A01068256)

KCSE 2015 17 1

54

.

cross-cutting

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SLA

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(QoS Parameter) .

1. Cross-cutting QoS

KCSE 2015 17 1

55

(QoS Metric)

. 1 IaaS, PaaS,

SaaS

. 13

,

.

.

1

.

IaaS customizability configurability

collaborability . Configurability

VM,

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meter

Metrics

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Custo-

mizabi-

lity

Configurability

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OpenAPI

License

Avail-

ability

Uptime

Maximum tolerable processing time

Pings

Relia-

bility

Fault rate (Error rate)

Coverage of fault tolerance

Mean time between failure (MTBF)

Load balancing

Maximum packet loss rate

Mean time to repair (MTTR)

Perfor-

mance

Response time

Throughput

Booting time

VM processing

time

Data transfer

time

Deployment

latency

Query

response time

Create time

Read time

Delete time

Get time

Put time

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time

Runtime

API calls per

time unit

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-ness

Exception handling

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bility

VM scale

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Number of users

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meter

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Virtualization

degree

Server scale Extensibility

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lity

Service of help

Learnability

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Average

functional

depth

Multi-terminal

support

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support

Stability

Geographic

location Browsers

Inter-

opera-

bility

Portability

Platform

integration Data integration

Capa-

city

Network capacity

Server capacity

Records storage

capability

Rate of requests

per minute

Cache memory

size

Communicati-

on channel

capacity

Storage size

Reco-

very

Backup

Failover

Recovery time

Secu-

rity

Encryption

Authentication

Authorization

Traceability

Non-repudiation

Privacy

Auditability

Repu-

tation

Number of users

Score graded by users

Companys awareness

Effi-

ciency

Utilization of resource

Time behavior

3.2 VSLA

VSLA(Variable SLA) SLA

(machine-readable document). VSLA

,

. 2 cross-cutting

KCSE 2015 17 1

56

VSLA

.

SLA

. VSLA , ,

[13] . VSLA 5

(parts) : Party, Guarantee, Violation,

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.

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SLA

(compliance level) .

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, party

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Feedback VSLA party

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Context

.

(source) (target) . ,

Context

. Policy

,

(rule) . Rule (action)

(pre-condition)

2. VSLA

KCSE 2015 17 1

57

(post-condition) .

Pricing party

.

(cost value) ,

.

VSLA

.

VSLA .

4.

3

IaaS, PaaS, SaaS

.

IaaS, PaaS, SaaS

cross-cutting

. 2

IaaS Google Compute Engine [14], PaaS

Google App Engine [15], SaaS

Office 365 [16]

.

Reliability, Capacity

QoS

,

.

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load balancing

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reliability HTTP ,

API . QoS

cross-cutting

.

2.

Parameter IaaS PaaS SaaS

Google Compute Engine Google App Engine Office 365

Customizability Configurability: Cloud SDK

(gcutil)

License:

Availability Uptime: >99.95% (Monthly) Uptime: >99.95% (Monthly) Uptime: >99.9% (Monthly)

Reliability

Load balancing:

(5

- $0.025,

$0.010 )

Fault rate(error rate): App

engine HTTP

, Datastore API ,

Search API

Error

Load balancing:

load balancing

Performance

VM: machine type

standard

1,2,4,8,16

API calls per time unit: Channel API calls (3,000 calls/minute), Mail API calls(32 calls/minute)

Throughput: inbox folder-20,000 items, sent items folder-20,000 items, contacts-5,000

items

Robustness

Exception handling: HTTP

Exception handling: HTTP

Exception handling:

thresholds

Scalability

Auto scaling: cloud

monitoring

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metric

Service scale: 70

Number of Users: Snapchat,

Rovio

Number of Users: Office 365

Business: 300 , Office 365

Enterprise:

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Service of help:

google.com/support

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Geographic location: ,

,

Service of help: stackoverflow

google.com/support

Learnability:

Service of help: support.office.com

Learnability:

Multi-language support:

(, ,

17 )

KCSE 2015 17 1

58

3. Office 365 SLA XML

99.9 % 1 monthly 5 % 100 %

IaaS, PaaS, SaaS

IaaS

PaaS PaaS SaaS

. cross-cutting

.

SLA 3 XML

. 3 2

Office 365

VSLA

VSLA

.

3 SLA

Office 365 SLA

SLO, ,

. Office 365 3

SLO

(uptime). Office 365

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99%

5% credit 95% 100%

credit .

XML

VSLA

. VSLA

ContractingParty

.

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cross-cutting QoS

.

SLA

SLA .

cross-cutting

SLA

VSLA . VSLA

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model , SLA

. VSLA ,

.

VSLA

,

VSLA

.

[1] J. Na, Qualitative Study on Service Features for

Cloud Computing, Journal of Digital Contents Society, Vol. 12, No. 3, pp. 319-327, Sep. 2011

[2] M. M. Qiu, Y. Zhou and C. Wang, Systematic Analysis of Public Cloud Service Level Agreements and Related Business Values, In Proceedings of International Conference on Services Computing, pp. 729-736, Jun. 2013

[3] C. Wu, Y. Zhu and S. Pan, The SLA Evaluation Model for Cloud Computing, In Proceedings of International Conference on Computer, Networks

KCSE 2015 17 1

59

and Communication Engineering, pp. 331-334, May 2013

[4] E. Badidi, A Cloud Service Broker for SLA-based SaaS Provisioning, In Proceedings of International Conference on Information Society, pp. 61-66, Jun. 2013

[5] S. Venticinque, R. Aversa, B. D. Martino, M. Rak and D. Petcu, A cloud agency for SLA negotiation and management, In Proceedings of Euro-Par Parallel Processing Workshops, pp. 587-594, Jan. 2011

[6] Z. Wang, X. Tang and X. Luo, Policy-Based SLA-Aware Cloud Service Provision Framework, In Proceedings of International Conference on Semantics Knowledge and Grid, pp. 114-121, Oct. 2011

[7] H. He, Z. Ma, H. Chen and W. Shao, Towards an SLA-Driven cache adjustment approach for applications on PaaS, In Proceedings of the Asia-Pacific Symposium on Internetware, pp. 11-20, Oct. 2013

[8] AOP(Aspect Oriented Programming), http://stophyun.tistory.com/34

[9] I. Ayadi, N. Simoni and T. Aubonnet, SLA approach for Cloud as a Service, In Proceedings of International Conference on Cloud Computing, IEEE Computer Society, pp. 966-967, Jun. 2013

[10] M. Alhamad, T. Dillon and E. Chang, Conceptual SLA Framework for Cloud Computing, In Proceedings of International Conference on Digital Ecosystems and Technologies, pp. 606-610, Apr. 2010

[11] K. Stamou, V. Kantere, J. H. Morin and M. Geogiou, A SLA graph model for data services, In Proceeding of International Workshop on Cloud data management, pp. 27-34, Oct. 2013

[12] , , ,

,

, Jun. 2014

[13] S. Hussain, R. Gustavsson, A. Saleem and L. Nordstrom, A SLA conceptual framework for monitoring QoS in smart grid, In Proceedings of IEEE Grenoble on PowerTech, pp. 1-6, Jun. 2013

[14] Google Compute Engine, https://cloud.google.com/compute

[15] Google App Engine, https://cloud.google.com/appengine

[16] Office 365, http://office.microsoft.com/ko-kr/business/FX104051403.aspx

KCSE 2015 17 1

60

(A4)

(GPLv2) (HIS Approach)

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KCSE 2015 17 1

61

(GPLv2)

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62

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63

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KCSE 2015 17 1

64

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KCSE 2015 17 1

65

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KCSE 2015 17 1

66

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KCSE 2015 17 1

67

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s/data/top-20-open-source-licenses

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licensing, J. Law Econom. Organ. 2005

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Development, Pearson Group, 2013

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York, NY, USA: ACM, 2003, pp.76-85

S. Haefliger, G. von Krogh, and S. Spaeth,

Code reuse in open source software, Manage.

Sci., vol. 54, no. 1, pp.180-193, Jan. 2008.

D.M. German and A.E. Hassan, License

integration patterns: Addressing license

mismatches in component-based development,

in Proceedings of the 31st International

Conference on Software Engineering, ser.

ICSE 09. Washington, DC, USA: IEEE Computer

Society, 2009, pp.188-198

OLIS, SW

2.0()

OLIS, SW

2.0() http://www.gnu.org/licenses/gpl-faq.en.html Lionel C. Briand, A Unified Framework for Coupling Measurement in Object-Oriented Systems, IEEE Transaction on Software Engineering, Vol.25, Issue.1, pp.91-121, Jan/Feb 1999

KCSE 2015 17 1

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5.

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KCSE 2015 17 1

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