Performance Improvement of Data Analysis of IoT Applications Using Re-Storm in Big Data Stream Computing Platform

Abstract:

Big Data and Internet of Things (IoT) are Two Popular Technical Terms in Current IT Industry. the Analysis of Iot Data Consumes more Energy since it is Huge in Size. this Paper Proposes a Methodology re-Storm that Addresses Energy Issues and Response Time of Iot Applications Data. it Uses Big Data Stream Computing for re-Storm against Existing Method Storm. the Storm Failed to Address Dynamic Scheduling but re-Storm Deals with Energy-Efficient Traffic Aware Resource Scheduling. this Paper Presents a Model that Different Traffic Arriving Rate of Streams re-Storm at Multiple Traffic Levels for High Energy Efficiency, Low Response Time. it Deals at Three Levels, Firstly, a Mathematical Model for High Energy Efficiency, Low Response Time. Secondly, Allocation of Resources Bearing in Mind DVFS (Dynamic Voltage and Frequency Scaling) Methods and Existing Effective Optimal Consolidation Methods. Thirdly, Online Task Allocation Using Hot Swapping Technique, Streaming Graph Optimizing. Finally, the Experimental Results Show that re-Storm has been Improved the Performance 30-40% against Storm for Real Time Data of Iot Applications.

• Periodical: International Journal of Engineering Research in Africa (Volume 22)
• Pages: 141-151
• DOI: https://doi.org/10.4028/www.scientific.net/JERA.22.141
• Online since: February 2016
• Authors: Patan Rizwan*, M. Rajasekhara Babu
• Keywords: Big Data Stream Computing, Energy Efficiency, Internet of Things, Real-Time Data

References

[1] H. Mohanty, Big Data: An Introduction, in Big Data SE - 1, vol. 11, H. Mohanty, P. Bhuyan, and D. Chenthati, Eds. Springer India, 2015, p.1–28.

DOI: 10.1007/978-81-322-2494-5

[2] batch processing, Webopedia. QuinStreet Inc. [Online]. Available: http: /www. webopedia. com/TERM/B/batch_processing. html. [Accessed: 05-Nov-2015].

[3] Interactive or Online Processing, Wikispaces. [Online]. Available: http: /dis-dpcs. wikispaces. com/3. 3. 5+Batch, +Online+%26+real+time+Processing. [Accessed: 05-Nov-2015].

[4] X. Liu, N. Iftikhar, and X. Xie, Survey of real-time processing systems for big data, " Proc. 18th Int. Database Eng. Appl. Symp. - IDEAS , 14, no. October 2015, p.356–361, (2014).

DOI: 10.1145/2628194.2628251

[5] J. Li, Z. Bao, and Z. Li, Modeling Demand Response Capability by Internet Data Centers Processing Batch Computing Jobs, Smart Grid, IEEE Transactions on, vol. 6, no. 2. p.737–747, (2015).

DOI: 10.1109/tsg.2014.2363583

[6] D. Sun, G. Fu, X. Liu, and H. Zhang, Optimizing Data Stream Graph for Big Data Stream Computing in Cloud Datacenter Environments, Int. J. Adv. Comput. Technol., vol. 6, no. 5, p.53–65, (2014).

[7] K. Singh and R. Kaur, Hadoop: Addressing challenges of Big Data, Advance Computing Conference (IACC), 2014 IEEE International. p.686–689, (2014).

DOI: 10.1109/iadcc.2014.6779407

[8] M. Bhandarkar, MapReduce programming with apache Hadoop, Parallel & Distributed Processing (IPDPS), 2010 IEEE International Symposium on. p.1, (2010).

DOI: 10.1109/ipdps.2010.5470377

[9] Storm (event processor), " Wikipedia. [Online]. Available: https: /en. wikipedia. org/w/index. php, title=Storm_(event_processor)&redirect=no. [Accessed: 25-Nov-2015].

[10] L. Neumeyer and B. Robbins, S4 : Distributed Stream Computing Platform, IEEE Int. Conf. Data Min. Work., p.170–177, (2010).

[11] E. Benkhelifa, M. Abdel-Maguid, S. Ewenike, and D. Heatley, The Internet of Things: The eco-system for sustainable growth, Computer Systems and Applications (AICCSA), 2014 IEEE/ACS 11th International Conference on. p.836–842, (2014).

DOI: 10.1109/aiccsa.2014.7073288

[12] A. Adshead, Data set to grow 10-fold by 2020 as internet of things takes off, computerweekly. com. [Online]. Available: http: /www. computerweekly. com/news/2240217788/Data-set-to-grow-10-fold-by-2020-as-internet-of-things-takes-off. [Accessed: 05-Nov-2015].

[13] D. Sun, G. Zhang, S. Yang, W. Zheng, S. U. Khan, and K. Li, Re-Stream: Real-time and energy-efficient resource scheduling in big data stream computing environments, Inf. Sci. (Ny)., Mar. (2015).

DOI: 10.1016/j.ins.2015.03.027

[14] J. Xu, Z. Chen, J. Tang, and S. Su, T-Storm : Traffic-aware Online Scheduling in Storm, IEEE Int. Conf. Distrib. Comput. Syst., (2014).

DOI: 10.1109/icdcs.2014.61

[15] R. B. M. Rizwan Patan, A Study Analysis of Energy Issues In Big Data, Intrnational J. Appl. Eng. Res., vol. 10, no. 6, p.15593–15609, (2015).

[16] M. Zaharia, T. Das, H. Li, T. Hunter, S. Shenker, and I. Stoica, Discretized streams: Fault-tolerant streaming computation at scale, in Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, 2013, p.423–438.

DOI: 10.1145/2517349.2522737

[17] F. Chen, M. Kodialam, and T. V Lakshman, Joint scheduling of processing and shuffle phases in mapreduce systems, in INFOCOM, 2012 Proceedings IEEE, 2012, p.1143–1151.

DOI: 10.1109/infcom.2012.6195473

[18] A. M. Aly, A. Sallam, B. M. Gnanasekaran, W. G. Aref, M. Ouzzani, and A. Ghafoor, M3: Stream Processing on Main-Memory MapReduce, Icde, no. 8, p.1253–1256, (2012).

DOI: 10.1109/icde.2012.120

[19] M. Dusi, N. D'Heureuse, F. Huici, A. Di Pietro, N. Bonelli, G. Bianchi, B. Trammell, and S. Niccolini, Blockmon: Flexible and high-performance big data stream analytics platform and its use cases, NEC Tech. J., vol. 7, no. 2, p.102–106, (2012).

[20] K. Kanoun, M. Ruggiero, D. Atienza, and M. Van Der Schaar, Low Power and Scalable Many-Core Architecture for Big-Data Stream Computing, Medianetlab. Ee. Ucla. Edu, no. 20. p.468–473, (2014).

DOI: 10.1109/isvlsi.2014.77

[21] Round-robin scheduling, Wikipedia. [Online]. Available: https: /en. wikipedia. org/wiki/Round-robin_scheduling. [Accessed: 20-Nov-2015].

[22] Hot swapping, Wikipedia. [Online]. Available: https: /en. wikipedia. org/wiki/Hot_swapping#References. [Accessed: 02-Nov-2015].

[23] D. Mills, Network time protocol (NTP), Rfc958, 1985. [Online]. Available: http: /tools. ietf. org/html/rfc958. [Accessed: 01-Nov-2015].

[24] CityPulse Dataset Collection., [Online]. Available: http: /iot. ee. surrey. ac. uk: 8080/datasets. html. [Accessed: 10-Nov-2015].

[25] K. Suresh, M. Rajasekharababu, Towards Effective Communication Technique for Energy Efficient Internet of Things, International Journal of Engineering Research in Africa, Vol. 21, pp.184-190, Dec. (2015).

DOI: 10.4028/www.scientific.net/jera.21.184

[26] Dhanaraj Cheelu, M. Rajasekhara Babu and P. Venkatakrishna (2013).

[27] Alok N. Bhatt, M Rajasekhara Babu and Anuja Jain Bhatt , Automation Testing Software that Aid in Efficiency Increase of Regression Process, Recent Patents on Computer Science, BSP, Vol. 6, No. 2, p.107 – 114, (2013).

DOI: 10.2174/22132759113069990008

[28] Babu, M.R., Krishna, P.V. and Khalid, M, A framework for power estimation and reduction in multi-core architectures using basic block approach, Int. J. Communication Networks and Distributed Systems, Inderscience Enterprises Ltd., Vol. 10, No. 1, p.40–51, (2013).

DOI: 10.1504/ijcnds.2013.050506