Đang chuẩn bị liên kết để tải về tài liệu:
báo cáo hóa học:" Research Article Detailed Performance and Waiting-Time Predictability Analysis of Scheduling Options in On-Demand Video Streaming"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Detailed Performance and Waiting-Time Predictability Analysis of Scheduling Options in On-Demand Video Streaming | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2010 Article ID 842697 20 pages doi 10.1155 2010 842697 Research Article Detailed Performance and Waiting-Time Predictability Analysis of Scheduling Options in On-Demand Video Streaming Mohammad A. Alsmirat and Nabil J. Sarhan Electrical and Computer Engineering Department Media Research Lab Wayne State University Detroit MI 48202 USA Correspondence should be addressed to Nabil J. Sarhan nabil@wayne.edu Received 2 May 2009 Accepted 24 November 2009 Academic Editor Benoit Huet Copyright 2010 M. A. Alsmirat and N. J. Sarhan. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. The number of on-demand video streams that can be supported concurrently is highly constrained by the stringent requirements of real-time playback and high transfer rates. To address this problem stream merging techniques utilize the multicast facility to increase resource sharing. The achieved resource sharing depends greatly on how the waiting requests are scheduled for service. We investigate the effectiveness of the recently proposed cost-based scheduling in detail and analyze opportunities for further tunings and enhancements. In particular we analyze alternative ways to compute the delivery cost. In addition we propose a new scheduling policy called Predictive Cost-Based Scheduling PCS which applies a prediction algorithm to predict future scheduling decisions and then uses the prediction results to potentially alter its current scheduling decisions. Moreover we propose an enhancement technique called Adaptive Regular Stream Triggering ART which significantly enhances stream merging behavior by selectively delaying the initiation of full-length video streams. We analyze the effectiveness of the proposed strategies in terms of their performance .