RE-STORM: REAL-TIME ENERGY EFFICIENT DATA ANALYSIS ADAPTING STORM PLATFORM
DOI:
https://doi.org/10.11113/jt.v78.7672Keywords:
Big Data, Real-Time data, Stream Computing, scheduling strategiesAbstract
It is necessary to model an energy efficient and stream optimization towards achieve high energy efficiency for Streaming data without degrading response time in big data stream computing. This paper proposes an Energy Efficient Traffic aware resource scheduling and Re-Streaming Stream Structure to replace a default scheduling strategy of storm is entitled as re-storm. The model described in three parts; First, a mathematical relation among energy consumption, low response time and high traffic streams. Second, various approaches provided for reducing an energy without affecting response time and which provides high performance in overall stream computing in big data. Third, re-storm deployed energy efficient traffic aware scheduling on the storm platform. It allocates worker nodes online by using hot-swapping technique with task utilizing by energy consolidation through graph partitioning. Moreover, re-storm is achieved high energy efficiency, low response time in all types of data arriving speeds.it is suitable for allocation of worker nodes in a storm topology. Experiment results have been demonstrated the comparing existing strategies which are dealing with energy issues without affecting or reducing response time for a different data stream speed levels. Finally, it shows that the re-storm platform achieved high energy efficiency and low response time when compared to all existing approaches.
References
H. Mohanty. 2015. Big Data: An Introduction. Big Data SE – 1. vol. 11. H. Mohanty, P. Bhuyan, and D. Chenthati, Eds. Springer India. 1-28.
D. Sun, G. Zhang, W. Zheng, and K. Li, 2015. Key Technologies for Big Data Stream Computing. Big Data, Chapman and Hall/CRC. 193-214.
J. Li, Z. Bao, and Z. Li. 2015. Modeling Demand Response Capability by Internet Data Centers Processing Batch Computing Jobs. Smart Grid, IEEE Transactions on. 6(2): 737-74.
D. Bhattacharya. 2013. Analytics On Big Fast Data Using A Real Time Stream Data Processing Architecture. 34.
Interactive or Online Processing. 2015. Wikispaces. [Online]. Available: http://dis-dpcs.wikispaces.com/3.3.5+Batch,+Online+%26+real+time+Processing. [Accessed: 05-Nov-2015].
V. Beal. 2015. Batch Processing, Webopedia. QuinStreet Inc. [Online]. Available: http://www.webopedia.com/TERM/B/batch_processing.html. [Accessed: 05-Nov-2015].
J. P. Verma, B. Patel, and A. Patel. 2015. Big Data Analysis: Recommendation System with Hadoop Framework. 2015 IEEE Int. Conf. Comput. Intell. Commun. Technol. 92-97.
M. Bhandarkar. 2010. Mapreduce Programming With Apache Hadoop. Parallel & Distributed Processing (IPDPS). 2010 IEEE International Symposium on. 1.
What is Hadoop Distributed File System (HDFS)? - Definition from WhatIs.com. [Online]. Available: http://searchbusinessanalytics.techtarget.com/definition/Hadoop-Distributed-File-System-HDFS. [Accessed: 07-Feb-2016].
E. Benkhelifa, M. Abdel-Maguid, S. Ewenike, and D. Heatley. 2014. The Internet Of Things: The Eco-System For Sustainable Growth. Computer Systems and Applications (AICCSA), 2014 IEEE/ACS. 11th International Conference on. 836-842.
Storm (event processor). Wikipedia. [Online]. Available: https://en.wikipedia.org/w/index.php?title=Storm_(event_processor)&redirect=no. [Accessed: 25-Nov-2015].
Storm (software) - Wikipedia, the free encyclopedia. [Online]. Available: https://en.wikipedia.org/wiki/Storm_(software). [Accessed: 07-Feb-2016].
M. Zaharia, T. Das, H. Li, T. Hunter, S. Shenker, and I. Stoica. 2013. Discretized Streams: Fault-Tolerant Streaming Computation At Scale. Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles. 423-438.
F. Chen, M. Kodialam, and T. V Lakshman. 2012. Joint Scheduling Of Processing And Shuffle Phases In Mapreduce Systems. INFOCOM, 2012 Proceedings IEEE. 1143-1151.
J. Xu, Z. Chen, J. Tang, and S. Su. 2014. T-Storm: Traffic-aware Online Scheduling in Storm. IEEE Int. Conf. Distrib. Comput. Syst.
A. M. Aly, A. Sallam, B. M. Gnanasekaran, W. G. Aref, M. Ouzzani, and A. Ghafoor. 2012. M3: Stream Processing on Main-Memory MapReduce. Icde. 8: 1253-1256.
M. Dusi, N. D’Heureuse, F. Huici, A. Di Pietro, N. Bonelli, G. Bianchi, B. Trammell, and S. Niccolini. 2012 Blockmon: Flexible And High-Performance Big Data Stream Analytics Platform And Its Use Cases. NEC Tech. J. vol. 7(2): 102-106.
D. Sun, G. Zhang, S. Yang, W. Zheng, S. U. Khan, and K. Li. 2015. Re-Stream: Real-Time And Energy-Efficient Resource Scheduling In Big Data Stream Computing Environments. Inf. Sci. (Ny). Mar. 2015.
K. Kanoun, M. Ruggiero, D. Atienza, and M. Van Der Schaar. 2014. Low Power and Scalable Many-Core Architecture for Big-Data Stream Computing. Medianetlab.Ee. Ucla.Edu. 20: 468-473.
Round-robin scheduling. Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Round-robin_scheduling. [Accessed: 20-Nov-2015].
X. Meng and T. Chen. 2013. Event-Driven Communication For Sampled-Data Control Systems. Am. Control Conf. (ACC), 2013. 1: 3002-3007.
E. A. Billard and J. C. Pasquale. 1993. Effects of Periodic Communication on Distributed Decision-Making. IEEE Int. Conf. Syst. Man, Cybern.
E. Le Sueur and G. Heiser. 2010. Dynamic Voltage And Frequency Scaling: The Laws Of Diminishing Returns. Proc. 2010 Int. Conf. Power aware Comput. Syst. 1-8.
Hot swapping. Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Hot_swapping#References. [Accessed: 02-Nov-2015].
D. Mills. 1985. Network Time Protocol (NTP). Rfc958.
S. Zhuravlev, J. C. Saez, S. Blagodurov, A. Fedorova, and M. Prieto. 2013. Survey of Energy-Cognizant Scheduling Techniques. IEEE Trans. PARALLEL Distrib. Syst. 24(7): 1447-1464.
Rizwan Patan, Rajasekhara Babu. 2015. M. A Study Analysis of Energy Issues In Big Data. Intrnational J. Appl. Eng. Res. 10(6): 15593-15609.
Rajasekhara Babu M., Krishna P. V. and Khalid. 2013. A Framework For Power Estimation And Reduction In Multi-Core Architectures Using Basic Block Approach. Int. J. Commun. Networks Distrib. Syst. Inderscience Enterp. Ltd. 10(1): 40-51.
Rajasekhara Babu M. and A. J. B. Alok N. Bhatt. 2013, Automation Testing Software that Aid in Efficiency Increase of Regression Process. Recent Patents Comput. Sci. 6(2): 107-114.
CityPuls. CityPulse Dataset Collection. [Online]. Available: http://iot.ee.surrey.ac.uk:8080/datasets.html. [Accessed:10-Nov-2015].
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
