A performance modeling framework for lambda architecture based applications

Future Generation Computer Systems

Volumn 86, Issue , 2018, Pages 1032-1041

  Gribaudo, M ^a   Iacono, M ^b   Kiran, M ^c  

^a POLITECNICO DI MILANO   (Italy)

^b SECOND UNIVERSITY OF NAPLES   (Italy)

^c LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

Author keywords

Analytical approach; Cloud; Lambda architectural pattern; Modeling languages; Multiformalism modeling; Multisolution methods; Performance evaluation

Indexed keywords

ARCHITECTURE; BATCH DATA PROCESSING; CLOUDS; DATA HANDLING;

ANALYTICAL APPROACH; ARCHITECTURAL PATTERN; MULTI-FORMALISM MODELS; MULTISOLUTION METHODS; PERFORMANCE EVALUATIONS;

MODELING LANGUAGES;

EID: 85026670445     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2017.07.033     Document Type: Article

References (25)

1. Amazon, Lambda architecture for batch and real-time processing on AWS withff spark streaming and spark SQL.URL http://img.bss.csdn.net/201508121544255257.pdf.
2. Kiran, M., Murphy, P., Monga, I., Dugan, J., Baveja, S.S., Lambda architecture for cost-effective batch and speed big data processing. Proceedings of the 2015 IEEE International Conference on Big Data (Big Data), BIG DATA’15, 2015, IEEE Computer Society, Washington, DC, USA, 2785–2792.
3. Barbierato, E., Rossi, G.-L.D., Gribaudo, M., Iacono, M., Marin, A., Exploiting product forms solution techniques in multiformalism modeling. Electron. Notes Theor. Comput. Sci. 296:0 (2013), 61–77, 10.1016/j.entcs.2013.07.005.
4. Gribaudo, M., Iacono, M., An introduction to multiformalism modeling. Gribaudo, M., Iacono, M., (eds.) Theory and Application of Multi-Formalism Modeling, 2014, IGI Global, Hershey, 1–16.
5. Khazaei, H., Misic, J., Misic, V.B., Performance analysis of cloud computing centers using M/G/m/m+r queuing systems. IEEE Trans. Parallel Distrib. Syst. 23:5 (2012), 936–943, 10.1109/TPDS.2011.199.
6. Gill, D., Pandey, H.M., Approaches for software performance modelling, cloud computing and openstack. Int. J. Comput. Appl. 119:22 (2015), 31–35 approaches for software performance modellingg.
7. M.A. Ismail, M.F. Ismail, H. Ahmed, Openstack cloud performance optimization using Linux services, in: 2015 International Conference on Cloud Computing, ICCC, 2015, pp. 1–4, http://dx.doi.org/10.1109/CLOUDCOMP.2015.7149648.
8. Y. Mei, L. Liu, X. Pu, S. Sivathanu, Performance measurements and analysis of network I/O applications in virtualized cloud, in: 2010 IEEE 3rd International Conference on Cloud Computing, 2010, pp. 59–66 http://dx.doi.org/10.1109/CLOUD.2010.74 ISSN: 2159-6182.
9. Olston, C., Reed, B., Srivastava, U., Kumar, R., Tomkins, A., Pig latin: a not-so-foreign language for data processing. Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD’08, 2008, ACM, New York, NY, USA, 1099–1110, 10.1145/1376616.1376726.
10. Chaiken, R., Jenkins, B., Larson, P.-A., Ramsey, B., Shakib, D., Weaver, S., Zhou, J., SCOPE: easy and efficient parallel processing of massive data sets. Proc. VLDB Endow. 1:2 (2008), 1265–1276, 10.14778/1454159.1454166.
11. Hoover, J., Start-Ups bring google's parallel processing to data warehousing. Informat. Week, 2008 URL http://www.informationweek.com/software/information-management/start-ups-bring-googles-parallel-processing-to-data-warehousing/d/d-id/1071483.
12. Mian, R., Martin, P., Vazquez-Poletti, J.L., Provisioning data analytic workloads in a cloud. Future Gener. Comput. Syst. 29:6 (2013), 1452–1458, 10.1016/j.future.2012.01.008 Including Special sections: High Performance Computing in the Cloud and Resource Discovery Mechanisms for P2P Systems.
13. Xin, R.S., Rosen, J., Zaharia, M., Franklin, M.J., Shenker, S., Stoica, I., Shark: SQL and rich analytics at scale. Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, SIGMOD’13, 2013, ACM, New York, NY, USA, 13–24, 10.1145/2463676.2465288.
14. Y. Mei, L. Liu, X. Pu, S. Sivathanu, Performance measurements and analysis of network I/O applications in virtualized cloud, in: 2010 IEEE 3rd International Conference on Cloud Computing, 2010, pp. 59–66 http://dx.doi.org/10.1109/CLOUD.2010.74 ISSN: 2159-6182.
15. Castiglione, A., Gribaudo, M., Iacono, M., Palmieri, F., Exploiting mean field analysis to model performances of Big Data architectures. Future Gener. Comput. Syst. 37:0 (2014), 203–211, 10.1016/j.future.2013.07.016.
16. Gribaudo, M., Iacono, M., Palmieri, F., Performance modeling of Big Data oriented architectures. Kolodziej, J., Pop, F., Di Martino, B., (eds.) Resource Management for Big Data Platforms and Applications Computer Communications and Networks, 2016, Springer International Publishing, 3–34, 10.1007/978-3-319-44881-7.
17. Xu, L., Cipar, J., Krevat, E., Tumanov, A., Gupta, N., Kozuch, M.A., Ganger, G.R., Agility and performance in elastic distributed storage. Trans. Storage 10:4 (2014), 16:1–16:27, 10.1145/2668129.
18. F. Yan, A. Riska, E. Smirni, Fast eventual consistency with performance guarantees for distributed storage, in: Distributed Computing Systems Workshops, ICDCSW, 2012 32nd International Conference on, 2012, pp. 23–28 http://dx.doi.org/10.1109/ICDCSW.2012.21 ISSN: 1545-0678.
19. Barbierato, E., Gribaudo, M., Iacono, M., Modeling apache hive based applications in big data architectures. Proceedings of the 7th International Conference on Performance Evaluation Methodologies and Tools, ValueTools’13, 2013, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), ICST, Brussels, Belgium, Belgium, 30–38, 10.4108/icst.valuetools.2013.254398.
20. M. Quartulli, J. Lozano, I.G. Olaizola, Beyond the lambda architecture: Effective scheduling for large scale EO information mining and interactive thematic mapping, in: 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS, 2015, pp. 1492–1495 http://dx.doi.org/10.1109/IGARSS.2015.7326062 ISSN: 2153-6996.
21. A. Batyuk, V. Voityshyn, Apache Storm based on topology for real-time processing of streaming data from social networks, in: 2016 IEEE First International Conference on Data Stream Mining Processing, DSMP, 2016, pp. 345–349 http://dx.doi.org/10.1109/DSMP.2016.7583573.
22. Bertoli, M., Casale, G., Serazzi, G., JMT: performance engineering tools for system modeling. SIGMETRICS Perform. Eval. Rev. 36:4 (2009), 10–15 URL http://doi.acm.org/10.1145/1530873.1530877.
23. Baarir, S., Beccuti, M., Cerotti, D., Pierro, M.D., Donatelli, S., Franceschinis, G., The GreatSPN tool: recent enhancements. SIGMETRICS Performance Evaluation Review 36:4 (2009), 4–9 URL http://doi.acm.org/10.1145/1530873.1530876.
24. S. Gilmore, J. Hillston, The PEPA workbench: a tool to support a process algebra-based approach to performance modelling, in: Computer Performance Evaluation, Modeling Techniques and Tools, 7th International Conference, Vienna, Austria, May 3–6, 1994, Proceedings, 1994, pp. 353–368.
25. Lazowska, E.D., Zahorjan, J., Graham, G.S., Sevcik, K.C., Quantitative System Performance: Computer System Analysis using Queueing Network Models, 1984, Prentice-Hall, Inc., Upper Saddle River, NJ, USA.