Advanced Media-based Smart Big Data on Intelligent Cloud Systems
Abstract:
Today’s advanced media technology preaches an enthralling time that will enormously bear on daily life. Moreover the rapid raise of wireless communications and networking will ultimately bring advanced media to our lives anytime, anywhere, and on any device. According to National Institute of Standards and Technology (NIST), Cloud Computing (CC) is a scheme for enabling convenient, on-demand network access to a shared pool of configurable computing pores (for example networks, applications, storage, servers and services) which could be promptly foresighted and delivered with minimal management effort or service provider interaction. This paper proposed an efficient algorithm for advanced scalable Media based Smart Big Data (3D, Ultra HD) on Intelligent Cloud Computing systems. The proposed encoding algorithm out performs the conventional HEVC standard which demonstrated by the performance evaluations. In order to ratify the proposed approach in addition, a relative study has been carried out. The proposed method could be used and integrated into HEVC, as a Smart Big Data, without violating the standard.
Existing System:
The ACO is the better load balancing algorithm compared to other algorithms. Concluding the first part of the related review, there is a study about Big Data technology. In [52] initially, there is an investigation of the importance of BD in modern life, and in terms of the economy, and also discussed the challenges that arise from Big Data utilization. Moreover, in [52] the potential of the powerful combination of BD and Computational intelligence is explored and a number of areas where novel applications in real world problems can be developed by utilizing these powerful tools and technologies is identified.
Proposed Work:
We proposed an efficient algorithm for advanced scalable Media-based Smart Big Data (3D, Ultra HD) on Intelligent Cloud Computing systems. With performance evaluations that have been made we demonstrate that the proposed encoding algorithm outperforms the traditional HEVC standard. By adopting this proposed method we assumed that it can be used and integrated into HEVC without violating the standard. Furthermore, by surveying the integration of BD, in general, in Cloud environments, we open new challenges in the field of this integration. This can be the sector of future research on the integration of those two technologies, and why not to have a huge improvement on their integration issues in order to have a better use of them.
CONCLUSION:
In the last decades technologies like BD and Cloud became valuable for people that need information at any time in any place. Information such this can be a high quality video, e.g. a 3D-HEVC video format. In this paper, we study and survey the three aforementioned technologies in order to find their common features of their use and to propose an operation which would help the issue of streaming high quality video, as Big Data, through the cloud environments. Based on the fast growth of wireless communications and networking technologies,which are related increased in many of their features like the volume of their data in the structured and unstructured form. Also, as the technology of CC grows more options about its “on-demand” operation arise. Thus, in this work, we proposed an efficient algorithm for advanced scalable Media-based Smart Big Data (3D, Ultra HD) on Intelligent Cloud Computing systems. With performance evaluations that have been made we demonstrate that the proposed encoding algorithm outperforms the traditional HEVC standard. By adopting this proposed method we assumed that it can be used and integrated into HEVC without violating the standard. Furthermore, by surveying the integration of BD, in general, in Cloud environments, we open new challenges in the field of this integration. This can be the sector of future research on the integration of those two technologies, and why not to have a huge improvement on their integration issues in order to have a better use of them.
REFERENCES
[1] M. Hilbert, P. López,“The World‟s Technological Capacity to Store, Communicate, and Compute Information”, Science, vol. 332, issue 6025, pp. 60-65, April 2011.
[2] C. Stergiou, K. E. Psannis, “Recent advances delivered by Mobile Cloud Computing and Internet of Things for Big Data applications: a survey”, Wiley, International Journal of Network Management, pp. 1-12, May 2016.
[3] A. P. Plageras, K. E. Psannis, C. Stergiou, H. Wang, B. B. Gupta, “Efficient IoT-based sensor BIG Data collection-processing and analysis in smart buildings”, Future Generation Computer Systems, in Press, October 2017.
[4] J. M.Batalla, P. Krawiec, A. Bęben, P. Wiśniewski, A. Chydziński, “Adaptive video streaming: rate and buffer on the track of minimum rebuffering”, IEEE Journal on Selection Areas of Communications, vol. 34, issue: 8, pp. 2154-2167, August 2016.
[5] G.Kokkonis, K. E. Psannis, M.Roumeliotis, D.Schonfeld, “Real-time wireless multisensory smart surveillance with 3D-HEVC streams for internet-of-things (IoT)”, Springer, Jounral of Supercompuitng, vol. 73, issue: 3, pp. 1044-1062, March 2017.
[6] G. Kokkonis, K. E. Psannis, M. Roumeliotis, Y. Ishibashi, “Efficient algorithm for transferring a real-time HEVC stream with haptic data through the internet”, Springer, Journal of Real-Time Image Processing, vol. 12, issue: 2, August 2016.
[7] C.-S. Park, B.-G. Kim, “Performance Analysis of Inter-Layer Prediction for Scalable Extension of the HEVC Standard for Adaptive Media Service”, Elsevier, Displays Journal, vol. 44, pp. 27–36, September 2016.
[8] K. Psannis, Y. Ishibashi, “Efficient Error Resilient Algorithm for H.264/AVC: Mobility Management in Wireless Video Streaming”, Springer, Telecommunication Systems Journal, vol. 41, issue: 2, pp. 65- 76, June 2009.
[9] P. Mell, T. Grance, “The NIST Definition of Cloud Computing”, National Institute of Standards and Technology, Recommendations of the National Institute of Standards and Technology, Special Publication 800-145, U.S. Department of Commerce, September 2011, Retrieved 24 July 2011.
[10] G. Skourletopoulos, C. X. Mavromoustakis, G. Mastorakis, J. MongayBatalla, J. N. Sahalos, “An Evaluation of Cloud-Based Mobile Services with Limited Capacity: A Linear Approach”, Springer, Soft Computing journal, vol. 21, issue: 16, pp. 4523–4530,August 2017.
[11] C. Wang, K. Ren, W. Lou, J. Li, “Toward publicly auditable secure Cloud data storage services”, IEEE Network, vol. 24, no. 4, pp. 19–24, August 2010. [12] S. Sakr, A. Liu, D. M. Batista, M. Alomari, “A survey of large scale data management approaches in cloud environments”, IEEE Communications Surveys & Tutorials, vol. 13, issue: 3, pp. 311–336, April 2011.
[13] J. M.Batalla, G. Mastorakis, C. Mavromoustakis, “On cohabitating networking technologies with common wireless access for Home Automation Systems purposes”, IEEE Wireless Communications Magazine, vol. 23, issue: 5, October 2016.
[14] Y. Kryftis, G. Mastorakis, C. Mavromoustakis, J. M. Batalla, E. Pallis, G. Kormentzas, “Efficient Entertainment Services Provision over a Novel Network Architecture”. IEEE Wireless Communications, vol. 23, issue: 1, pp. 14-21, February 2016.
[15] K. Gai, M. Qiu, H. Zhao, “Energy-aware task assignment for mobile cyber-enabled applications in heterogeneous cloud computing”, Journal of Parallel and Distributed Computing, vol. 111, pp. 126-135, January 2018.
[16] C. Stergiou, K. E. Psannis, “Efficient and Secure Big Data delivery in Cloud Computing”, Springer, Multimedia Tools and Applications, pp. 1- 20, April 2017.
[17] M. Qiu, Z. Ming, J. Li, K. Gai, Z. Zong, “Phase-Change Memory Optimization for Green Cloud with Genetic Algorithm”, IEEE Transactions on Computers, vol. 64, issue: 12, pp. 3528-3540, December 2015.
[18] Y. Li, K. Gai, L. Qiu, M. Qiu, H. Zhao, “Intelligent cryptography approach for secure distributed big data storage in cloud computing”, Information Sciences, vol. 387, pp. 103-115, May 2017.
[19] Y. Zhang,M. Naccari, D.Agrafiotis, M.Mrak, D. R. Bull, “High Dynamic Range Video Compression Exploiting Luminance Masking”,IEEE Transactions on Circuits and Systems for Video Technology, vol. 5, issue. 26, pp. 950-964, May 2016.
[20] G. Pastuszak, A. Abramowski, “Algorithm and Architecture Design of the H.265/HEVC Intra Encoder”,IEEE Transactions on Circuits and Systems for Video Technology, vol. 26, issue: 1, pp. 210-222, May 2015.