Self-similar traffic sources: modeling and real-time resource allocation

K. Nagarajan, G.T. Zhou
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引用次数: 4

Abstract

Communication networks have to rely on efficient resource allocation schemes to share the network resources (bandwidth, buffer size, etc.) among users offering different types of traffic (eg, voice, video and data). Existing schemes based on self-similar traffic models assume that the network traffic is Gaussian and exhibits long-term memory characteristics only. Certain classes of network traffic (eg, MPEG video traces) are however, non-Gaussian and long-range-dependent. In such cases, resource allocation based on simplified assumptions will be either excessive or fail to provide the specified guarantees on the quality of service (QoS). In an earlier work, we had presented an efficient resource allocation scheme for traffic sources having: (i) Gaussian as well as non-Gaussian (log-normal) distributions; and (ii) exhibiting short-term and/or long-term memory characteristics. In this paper, we assess the real-time performance of our as well as several existing schemes using a Texas Instruments TMS320C6701 DSP. The results show that: (i) although our algorithm has a higher computational load, real-time implementation is still feasible; and (ii) the increased computational load is justified since the proposed algorithm is more reliable in providing QoS guarantees than existing simplified schemes.
自相似流量源:建模和实时资源分配
通信网络必须依靠有效的资源分配方案,在提供不同类型流量(如语音、视频和数据)的用户之间共享网络资源(带宽、缓冲区大小等)。现有的基于自相似流量模型的方案假设网络流量是高斯分布的,并且只表现出长时记忆特征。然而,某些类别的网络流量(例如,MPEG视频跟踪)是非高斯的和长距离依赖的。在这种情况下,基于简化假设的资源分配要么过多,要么无法提供对服务质量(QoS)的指定保证。在早期的工作中,我们提出了一种有效的流量源资源分配方案,具有:(i)高斯分布和非高斯分布(对数正态);(ii)表现出短期和/或长期记忆特征。在本文中,我们使用德州仪器的TMS320C6701 DSP对我们的方案以及几种现有方案的实时性进行了评估。结果表明:(1)虽然算法的计算量较高,但实时实现仍然是可行的;(ii)增加的计算负荷是合理的,因为所建议的算法在提供QoS保证方面比现有的简化方案更可靠。
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来源期刊
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5812
期刊介绍: Journal of Signal Processing is an academic journal supervised by China Association for Science and Technology and sponsored by China Institute of Electronics. The journal is an academic journal that reflects the latest research results and technological progress in the field of signal processing and related disciplines. It covers academic papers and review articles on new theories, new ideas, and new technologies in the field of signal processing. The journal aims to provide a platform for academic exchanges for scientific researchers and engineering and technical personnel engaged in basic research and applied research in signal processing, thereby promoting the development of information science and technology. At present, the journal has been included in the three major domestic core journal databases "China Science Citation Database (CSCD), China Science and Technology Core Journals (CSTPCD), Chinese Core Journals Overview" and Coaj. It is also included in many foreign databases such as Scopus, CSA, EBSCO host, INSPEC, JST, etc.
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