QoS-driven management of business process variants in cloud based execution environments

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9936 LNCS, Issue , 2016, Pages 55-69

  Ghosh, Rahul ^a   Ghose, Aditya ^b   Hegde, Aditya ^a   Mukherjee, Tridib ^a   Mos, Adrian ^c  

^a Xerox Research Center India   (India)

^b UNIVERSITY OF WOLLONGONG   (Australia)

^c XEROX RESEARCH CENTRE EUROPE   (France)

Author keywords

Cloud; Context; Process adaptation; QoS; Resource

Indexed keywords

CLOUDS; DATA MINING; DECISION TREES; DISTRIBUTED COMPUTER SYSTEMS; NEAREST NEIGHBOR SEARCH; QUALITY OF SERVICE; TREES (MATHEMATICS);

CONTEXT; COST OF OPERATIONS; DECISION TREE LEARNING; EXECUTION ENVIRONMENTS; FUNCTIONAL CONSTRAINTS; K-NEAREST NEIGHBOURS; PROCESS ADAPTATIONS; RESOURCE;

PROCESS DESIGN;

EID: 84989295914     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46295-0_4     Document Type: Conference Paper

References (21)

1. Performance Solutions: A Practical Guide to Creating Responsive, Scalable Software. Addison Wesley Longman Publishing Co., Inc., Redwood City (2002)
2. BPI Challenge 2012 (2016). https://www.win.tue.nl/bpi/doku.php?id=2012:challenge
3. Scikit-learn: Machine Learning in Python (2016). http://scikit-learn.org/stable/index.html
4. Balsamo, S., Di Marco, A., Inverardi, P., Simeoni, M.: Model-based performance prediction in software development: a survey. IEEE Trans. Softw. Eng. 30(5), 295–310 (2004). http://dx.doi.org/10.1109/TSE.2004.9
5. Bocciarelli, P., D’Ambrogio, A.: A model-driven method for enacting the designtime QoS analysis of business processes. Softw. Syst. Model. 13(2), 573–598 (2014)
6. de Medeiros, A.K.A., Pedrinaci, C., van der Aalst, W.M.P., Domingue, J., Song, M., Rozinat, A., Norton, B., Cabral, L.: An outlook on semantic business process mining and monitoring. In: Meersman, R., Tari, Z. (eds.) OTM-WS 2007, Part II. LNCS, vol. 4806, pp. 1244–1255. Springer, Heidelberg (2007)
7. Dimitrov, E., Schmietendorf, A., Dumhe, R.: Uml-based performance engineering possibilities and techniques. IEEE Softw. 19(1), 74–83 (2002)
8. Ghosh, R.: Scalable stochastic models for cloud services. Ph.D. thesis, Duke University (2012)
9. Ghosh, R., Longo, F., Xia, R., Naik, V., Trivedi, K.: Stochastic model driven capacity planning for an infrastructure-as-a-service cloud. IEEE Trans. Serv. Comput. 7(4), 667–680 (2014)
10. Jung, G., Sharma, N., Goetz, F., Mukherjee, T.: Cloud capability estimation and recommendation in black-box environments using benchmark-based approximation. In: IEEE CLOUD (2013)
11. Kähkipuro, P.: UML based performance modeling framework for object-oriented distributed systems. In: France, R.B. (ed.) UML 1999. LNCS, vol. 1723, pp. 356–371. Springer, Heidelberg (1999)
12. Mos, A.: Domain specific monitoring of business processes using concept probes. In: Toumani, F., et al. (eds.) ICSOC 2014 Workshops. LNCS, vol. 8954, pp. 213–224. Springer, Berlin (2015)
13. Mos, A., Pedrinaci, C., Rey, G.A., Gomez, J.M., Liu, D., Vaudaux-Ruth, G., Quaireau, S.: Multi-level monitoring and analysis of web-scale service based applications. In: Dan, A., Gittler, F., Toumani, F. (eds.) ICSOC/ServiceWave 2009. LNCS, vol. 6275, pp. 269–282. Springer, Heidelberg (2010)
14. Pedrinaci, C., Domingue, J., Alves de Medeiros, A.K.: A core ontology for business process analysis. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 49–64. Springer, Heidelberg (2008)
15. Pedrinaci, C., Lambert, D., Wetzstein, B., Van Lessen, T., Cekov, L., Dimitrov, M.: Sentinel: a semantic business process monitoring tool. In: Proceedings of 1st International Workshop on Ontology-Supported Business Intelligence, p. 1. ACM (2008)
16. Petriu, D.C., Shen, H.: Applying the UML performance profile: graph grammarbased derivation of LQN models from UML specifications. In: Field, T., Harrison, P.G., Bradley, J., Harder, U. (eds.) TOOLS 2002. LNCS, vol. 2324, pp. 159–177. Springer, Heidelberg (2002)
17. Ponnalagu, K., Ghose, A.K., Narendra, N.C., Dam, H.: Cloud capability estimation and recommendation in black-box environments using benchmark-based approximation. In: BPM (2015)
18. Smith, C.U., Williams, L.G.: Performance and scalability of distributed software architectures: an SPE approach. Parallel Distrib. Comput. Pract. 3(4), 74106–80700 (2002)
19. Solomon, A., Litoiu, M.: Business process performance prediction ONA tracked simulation model. In: Proceedings of 3rd International Workshop on Principles of Engineering Service-Oriented Systems, PESOS 2011, pp. 50–56. ACM, New York (2011). http://doi.acm.org/10.1145/1985394.1985402
20. Williams, L.G., Smith, C.U.: Performance evaluation of software architectures. In: Proceedings of 1st International Workshop on Software and Performance, WOSP 1998, pp. 164–177. ACM, New York (1998). http://doi.acm.org/10.1145/287318.287353
21. Woodside, M., Petriu, D.C., Merseguer, J., Petriu, D.B., Alhaj, M.: Transformation challenges: from software models to performance models. Softw. Syst. Model. 13(4), 1529–1552 (2014). http://dx.doi.org/10.1007/s10270-013-0385-x