WebRTC는 웹과 모바일과 같이 여러 플랫폼에서 세계 최고 수준의 실시간 커뮤니케이션으로 빠르게 성장했다. WebRTC의 현재 기술은 peer와 시그널링 서버에서 사용자가 요청한 많은 양의 큰 스트리밍을 효율적으로 처리하지 못한다. 따라서, 본 논문에서는 동적로드 밸런싱 알고리즘을 사용하여, 데이터 흐름 전달을 제공함으로써 문제를 처리하는 작업을 수행한다. 또한, 사용자가 요청하는 소스를 분석하고 이러한 스트리밍 요청을 로드 밸런싱 구성 요소에 전달한다. 구체적으로 구성 요소는 요청된 리소스와 사용가능한 리소스의 양을 응답 서버에서 결정한 후 스트리밍 데이터를 요청하는 사용자에게 병렬 또는 교대로 전달한다. 이와 같은 방법을 검증하기 위해 네트워크 시뮬레이션 도구 OPNET을 사용하여 로드 밸런싱 알고리즘을 시연 후 우분투 서버에 적용하여 구현한다. 또한 실험을 통해 도출된 결과와 WebRTC의 구현을 비교하여 제안함으로써 기존 방법보다 효율적이고 동적으로 수행되는 지를 보여준다.
WebRTC는 웹과 모바일과 같이 여러 플랫폼에서 세계 최고 수준의 실시간 커뮤니케이션으로 빠르게 성장했다. WebRTC의 현재 기술은 peer와 시그널링 서버에서 사용자가 요청한 많은 양의 큰 스트리밍을 효율적으로 처리하지 못한다. 따라서, 본 논문에서는 동적로드 밸런싱 알고리즘을 사용하여, 데이터 흐름 전달을 제공함으로써 문제를 처리하는 작업을 수행한다. 또한, 사용자가 요청하는 소스를 분석하고 이러한 스트리밍 요청을 로드 밸런싱 구성 요소에 전달한다. 구체적으로 구성 요소는 요청된 리소스와 사용가능한 리소스의 양을 응답 서버에서 결정한 후 스트리밍 데이터를 요청하는 사용자에게 병렬 또는 교대로 전달한다. 이와 같은 방법을 검증하기 위해 네트워크 시뮬레이션 도구 OPNET을 사용하여 로드 밸런싱 알고리즘을 시연 후 우분투 서버에 적용하여 구현한다. 또한 실험을 통해 도출된 결과와 WebRTC의 구현을 비교하여 제안함으로써 기존 방법보다 효율적이고 동적으로 수행되는 지를 보여준다.
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. ...
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.
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.
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문제 정의
Thirdly the system can be restructured to optimize the job assignment at the servers. It aims to provide an evaluation and comparative study of these approaches.
제안 방법
It causes the overloading problem. To show how the method works and its performance, we set up the simulation in the network simulation tool OPNET, it showed that after several seconds the transmission is balanced appropriately to the available resource of the user who broadcast their data to a bunch of large users. In addition, we also implemented the method in WebRTC original API, four devices use to access the Signaling server, and the monitoring result showed that the client and the server handle the large requests, efficiency without any corruption at both service provider and client which receives streaming data.
성능/효과
Therefore, in the work [15] authors investigated a data-aware work stealing technique that is able to achieve good load balancing, and yet still tries to best exploit data locality. The results showed that their technique is scalable to achieve both good load balancing and high location-hit rate.
참고문헌 (21)
A. Passarella, "A survey on content-centric technologies for the current Internet: CDN and P2P solutions," Computer Communications, Vol.35, pp.1-32, 2012.
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.
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.
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.
P. Zikopoulos, C. Eaton, and others, "Understanding big data: Analytics for enterprise class hadoop and streaming data," McGraw-Hill Osborne Media, 2011.
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.
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.
P. Mell and T. Grance, "The NIST definition of cloud computing," 2011.
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..
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.
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.
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.
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.
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.
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.
O. Modeler, "OPNET Technologies Inc," 2009.
M. Cantelon, M. Harter, T. Holowaychuk, and N. Rajlich, "Node. js in Action: Manning," 2014.
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