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Non-intrusive transaction aware filtering during enterprise application modernization

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Abstract

Enterprise modernization refers to enhancing the capability of the existing application to meet the business demands. This modernization can lead to vast quantities of data generated from infrastructure, channels, database and other roots of computing involved which leads to the quantity of information generated. The data getting generated impact the resources called for these sorts of computing as it is directly proportional to recurring computing cost model and incurs a huge cost for enterprise opting for enterprise modernization. In this paper, we suggest a method to place the relevant transaction filtering of information in the context of enterprise modernization. This is answered by presenting the non-intrusive transaction aware filtering framework to get just the relevant information needed to further optimize the computing resources. Simulation and experimental results show an improvement in network, memory, throughput, CPU utilization compared against the non-transaction aware filtering during various load conditions.

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References

  1. Glance N S, Hurst M, Tomokiyo T (2004) BlogPulse: Automated Trend Discovery for Weblogs. In: WWW Conference

  2. Uchida M, Shibata N, Shirayama S (2007) Identification and visualization of emerging trend from blogosphere. In: Proceedings of International Conference on Weblogs and Social Media (ICWSM), pp 305–306

  3. Agarwal S, Mozafari B, Panda A, Milner H, Madden S, Stoica I (2013) Blinkdb: queries with bounded errors and bounded response times on very large data. In: EuroSys, pp 29–42

  4. Azvine B, Nauck D, Ho C (2003) Intelligent business analytics—a tool to build decision—support systems for ebusinesses. BT Technol J 21(4):65–71

    Article  Google Scholar 

  5. Weske M (2007) Business process management: concepts, languages, architectures. Springer, Berlin

    Google Scholar 

  6. Rajaraman A, Ullman JD (2012) Mining of massive datasets. Cambridge University Press, Cambridge

    Google Scholar 

  7. Watson HJ, Wixom BH (2007) The current state of business intelligence. Computer 40(9):96–99

    Article  Google Scholar 

  8. Wu X, Zhu G, Wu Q, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107

    Article  Google Scholar 

  9. Chaudri S, Dayal U, Ganti V (2001) Database technology for decision support systems. Computer 34(12):48–55

    Article  Google Scholar 

  10. Zheng Z, Zhu J, Lyu M R(2013) Service-generated big data and big data as-a-service: an overview. In: Proceedings of IEEE International Congress Big Data, pp 403–410

  11. Grozev N, Buyya R (2013) Performance modelling and simulation of three-tier applications in cloud and multi-cloud environments. Comput J 29(5):1254–1264

    Google Scholar 

  12. Luckham D (2002) The power of events: an introduction to complex event processing in distributed enterprise systems. Springer, Berlin

    Google Scholar 

  13. Mai J, Fan Y, Shen Y(2009) A neural networks-based clustering collaborative filtering algorithm in e-commerce recommendation system. In: Proceedings of International Conference Web Intelligence System Mining, pp 616–619

  14. Ananthanarayanan R (2013) Photon: fault-tolerant and scalable joining of continuous data streams . In: SIGMOD, pp 577–588

  15. Lloyd W, Pallickara S, David O, Lyon J, Arabi M, Rojas K (2013) Performance implications of multi-tier application deployments on infrastructure as-a-service clouds: towards performance modeling. Future Gener Comput Syst 29(5):1254–1264

    Article  Google Scholar 

  16. Mittal N, Nayak R, Govil M C, Jain K C (2010) Recommender system framework using clustering and collaborative filtering. In: Proceedings of 3rd International Conference Emerging Trends Engineering Technology, pp 555–558

  17. Li X, Murata T (2012) Using multidimensional clustering based collaborative filtering approach improving recommendation diversity. In: Proceedings IEEE/WIC/ACM International Joint Conference Web Intelligence Intelligent Agent Technology, vol 3, pp 169–174

  18. Zhao Y, Karypis G, Fayyad U (2005) Hierarchical clustering algorithms for document datasets. Data Min Knowl Discov 10(2):141–168

    Article  MathSciNet  Google Scholar 

  19. Zhou Z, Sellami M, Gaaloul W, Barhamgi M, Defude B (2013) Data providing services clustering and management for facilitating service discovery and replacement. IEEE Trans Autom Sci Eng 10(4):1–16

    Article  Google Scholar 

  20. Pham MC, Cao Y, Klamma R, Jarke M (2011) A clustering approach for collaborative filtering recommendation using social network analysis. J Univers Comput Sci 17(4):583–604

    Google Scholar 

  21. Garg SK, Toosi NA, Gopalaiyengar S, Buyya R (2014) SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter. J Netw Comput Appl 45:108–120

    Article  Google Scholar 

  22. Ijaz S, Munir E, Anwar W, Nasir W (2013) Efficient scheduling strategy for task graphs in heterogeneous computing environment. Int Arab J Inf Technol (IAJIT) 10(5):486

    Google Scholar 

  23. Tawfeek M, El-Sisi A, Keshk A, Torkey F (2015) Cloud task scheduling based on ant colony optimization. Int Arab J Inf Technol (IAJIT) 71(1):241–292

    Google Scholar 

  24. Zeng W, Shang MS, Zhang QM, Lu L, Zhou T (2010) Can dissimilar users contribute to accuracy and diversity of personalized recommendation? Int J Mod Phys 21(10):1217–1227

    Article  Google Scholar 

  25. Linlin Wu, Garg SK, Versteeg S, Buyy R (2014) SLA-based resource provisioning for hosted software as a service applications in cloud computing environments. IEEE Trans Serv Comput (TSC) 7(3):465–485

    Article  Google Scholar 

  26. Grozev N, Buyya R (2014) Multi-cloud provisioning and load distribution for three-tier applications. ACM Trans Auton Adapt Syst (TAAS) 9(3):13:1–13:21

  27. Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616

    Article  Google Scholar 

  28. Thilagavathi G, Srivaishnavi D, Aparna N (2013) A survey on efficient hierarchical algorithm used in clustering. Int J Eng 2(9):165–176

    Google Scholar 

  29. Bi J, Zhu Z, Tian R, Wang Q (2010) Dynamic provisioning modeling for virtualized multitier applications in cloud data center. In: Proceedings of the 3rd International Conference on Cloud Computing, pp 370–3711

  30. Foster I, Zhao Y, Raicu I, Lu S (2008) Cloud computing and grid computing 360-degree compared. In: Proceedings of Grid Computing Environments Workshop, pp 1–10

  31. Gao K, Wang Q, Xi L (2014) Reduct algorithm based execution times prediction in knowledge discovery cloud computing environment. Int Arab J Inf Technol (IAJIT) 11:268–275

    Google Scholar 

  32. Melnik S, Gubarev A, Long JJ, Romer G, Shivakumar S, Tolton M, Vassilakis TD (2010) Interactive analysis of web-scale datasets. Proc VLDB 3(2):330–339

    Article  Google Scholar 

  33. Toosi NA, Calheiros RN, Buyya R (2014) Interconnected cloud computing environments: challenges, taxonomy, and survey. ACM Comput Surv 47(1):7.1–7.47

    Article  Google Scholar 

  34. Havens TC, Bezdek JC, Leckie C, Hall LO, Palaniswami M (2012) Fuzzy C-means algorithms for very large data. IEEE Trans Fuzzy Syst 20(6):1130–1146

    Article  Google Scholar 

  35. Rusu F, Dobra A (2011) GLADE: a scalable framework for efficient analytics. LADIS 46(1):12–18

    Google Scholar 

  36. Urgaonkar B, Paci_ci G, Shenoy P, Spreitzer M, Tantawi A (2005) An analytical model for multi-tier internet services and its applications. In: Proceedings of the International Conference on Measurement and Modeling of Computer Systems ACM SIGMETRICS, pp 291–302

  37. Zhang Q, Cherkasova L, Smirni E (2007) A regression-based analytic model for dynamic resource provisioning of multi-tier applications. In: Proceedings of the 4th International Conference on Autonomic Computing

  38. Zhang Q, Cherkasova L, Smirni E (2007) A regression-based analytic model for dynamic resource provisioning of multi-tier applications. In: Proceedings of the 4th International Conference on Autonomic Computing (ICAC 2007)

  39. Rochwerger B (2009) The reservoir model and architecture for open federated cloud computing. IBM J Res Dev 53(4):1–11

    Article  Google Scholar 

  40. Armbrust M (2010) A view of cloud computing. Commun ACM 53(3):55–58

    Google Scholar 

  41. Simon R D, Tengke X, Shengui W (2013) Combining collaborative filtering and clustering for implicit recommender system. In: Proceedings of IEEE 27th International Conference Advance Information Network Applications, pp 748–755

  42. Paulen B, Finken J (2009) Key performance indicators. In: Pro SQL Server 2008 Analytics. Apress, pp 37–52

  43. Petcu D (2013) Multi-cloud: expectations and current approaches. In: Proceedings of the International Workshop on Multi-cloud Applications and Federated Clouds (MultiCloud ’13), pp 1–6

  44. Menzel M, Ranjan R (2012) CloudGenius: decision support for web server cloud migration. In: International World Wide Web Conference Committee (IW3C2), pp 979–988

  45. Buyya R, Calheiros R, Li X (2012) Autonomic cloud computing: open challenges and architectural elements. In: Proceedings of the Third International Conference of Emerging Applications of Information Technology, pp 3–10

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Correspondence to Ravikumar Ramadoss.

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Ramadoss, R., Elango, N.M., Abimannan, S. et al. Non-intrusive transaction aware filtering during enterprise application modernization. J Supercomput 74, 1157–1181 (2018). https://doi.org/10.1007/s11227-017-2123-6

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  • DOI: https://doi.org/10.1007/s11227-017-2123-6

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