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http://dx.doi.org/10.9728/dcs.2016.17.6.581

A Dynamical Load Balancing Method for Data Streaming and User Request in WebRTC Environment  

Ma, Linh Van (School of Electronics and Computer Engineering, Chonnam National University)
Park, Sanghyun (School of Electronics and Computer Engineering, Chonnam National University)
Jang, Jong-hyun (Electronics and Telecommunications Research Institute)
Park, Jaehyung (School of Electronics and Computer Engineering, Chonnam National University)
Kim, Jinsul (School of Electronics and Computer Engineering, Chonnam National University)
Publication Information
Journal of Digital Contents Society / v.17, no.6, 2016 , pp. 581-592 More about this Journal
Abstract
WebRTC has quickly grown to be the world's advanced real-time communication in several platforms such as web and mobile. In spite of the advantage, the current technology in WebRTC does not handle a big-streaming efficiently between peers and a large amount request of users on the Signaling server. Therefore, in this paper, we put our work to handle the problem by delivering the flow of data with dynamical load balancing algorithms. We analyze the request source users and direct those streaming requests to a load balancing component. More specifically, the component determines an amount of the requested resource and available resource on the response server, then it delivers streaming data to the requesting user parallel or alternately. To show how the method works, we firstly demonstrate the load-balancing algorithm by using a network simulation tool OPNET, then, we seek to implement the method into an Ubuntu server. In addition, we compare the result of our work and the original implementation of WebRTC, it shows that the method performs efficiently and dynamically than the origin.
Keywords
Big data streaming; Dynamical algorithm; Load balancing; Round robin; WebRTC;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 A. Passarella, "A survey on content-centric technologies for the current Internet: CDN and P2P solutions," Computer Communications, Vol.35, pp.1-32, 2012.   DOI
2 M. C. Linn, "Internet environments for science education: Routledge," 2013.
3 E. Rescorla, "WebRTC Security Architecture," 2015.
4 A. Johnston, J. Yoakum, and K. Singh, "Taking on WebRTC in an enterprise," Communications Magazine, IEEE, Vol.51, pp.48-54, 2013.   DOI
5 Lee HN, Kim DH, "Selection of Scalable Video Coding Layer Considering the Required Peak Signal to Noise Ratio and Amount of Received Video Data in Wireless Networks," Journal of Digital Contents Society, Vol.17, No.2, pp.89-96, 2016.   DOI
6 Linh. M. Van, J. Kim, S. Park, J. Kim, and J. Jang, "An efficient Session_Weight load balancing and scheduling methodology for high-quality telehealth care service based on WebRTC," The Journal of Supercomputing, Vol.72, No.10, pp.3909-3926, 2016.   DOI
7 Linh. M. Van, Jang JH, Kim J, "Adjusting Local Network Speed by Using Fuzzy Theory with An Illustration in WebRTC Environment," Journal of Digital Contents Society, Vol.16, No.6, pp.917-25, 2015.   DOI
8 J. Dean and S. Ghemawat, "MapReduce: simplified data processing on large clusters," Communications of the ACM, Vol.51, No.1, pp.107-113, 2008.   DOI
9 P. Zikopoulos, C. Eaton, and others, "Understanding big data: Analytics for enterprise class hadoop and streaming data," McGraw-Hill Osborne Media, 2011.
10 M. Randles, D. Lamb, and A. Taleb-Bendiab, "A comparative study into distributed load balancing algorithms for cloud computing," Advanced Information Networking and Applications Workshops (WAINA), 2010 IEEE 24th International Conference, pp.551-556, 2010.
11 P. Mell and T. Grance, "The NIST definition of cloud computing," 2011.
12 J. Hu, J. Gu, G. Sun, and T. Zhao, "A scheduling strategy on load balancing of virtual ma-chine resources in cloud computing environment," in Parallel Architectures, Algorithms and Programming (PAAP) Third International Symposium, pp.89-96, 2010..
13 A. S. Szalay, G. Bell, J. Vandenberg, A. Wonders, R. Burns, D. Fay, et al., "Graywulf: Scalable clustered architecture for data intensive computing," in System Sciences, HICSS'09. 42nd Hawaii International Conference, pp.1-10, 2009.
14 N. Liu, Z. Wen, K. L. Yeung, and Z. Lei, "Request-peer selection for load-balancing in P2P live streaming systems," in Wireless Communications and Networking Conference (WCNC) IEEE, pp.3227-3232, 2012.
15 K. Wang, X. Zhou, T. Li, D. Zhao, M. Lang, and I. Raicu, "Optimizing load balancing and data-locality with data-aware scheduling," Big Data 2014 IEEE International Conference, pp.119-128, 2014.
16 Z. Zhang and X. Zhang, "Realization of open cloud computing federation based on mobile agent," Intelligent Computing and Intelligent Systems ICIS 2009 IEEE International Conference, pp.642-646, 2009.
17 Z. Zhang and X. Zhang, "A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation," in Industrial Mechatronics and Automation (ICIMA), 2010 2nd International Conference, pp.240-243, 2010.
18 Kong HS, Song EJ, "A Study on Hotel Customer Reputation Analysis based on Big Data," Journal of Digital Contents Society, Vol.15, No.2, pp.219-25, 2014.   DOI
19 U. A. Acar and Y. Chen, "Streaming big data with self-adjusting computation," Proceedings of the 2013 workshop on Data driven functional programming, pp.15-18, 2013.
20 O. Modeler, "OPNET Technologies Inc," 2009.
21 M. Cantelon, M. Harter, T. Holowaychuk, and N. Rajlich, "Node. js in Action: Manning," 2014.