POWer Adaptive Random Early Detection for Diff-Serv Assured Forwarding Service Classes

B. K. Ng, D. Chieng, A. Malik
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引用次数: 1

Abstract

With diverse performance requirements and ever- changing nature of Internet multimedia applications, differential treatment to these applications is inevitable. In this paper we evaluated the performances of popular Adaptive Random Early Detection (ARED) [7] algorithms against our POWer Adaptive Random Early Detection (POWARED) [8] algorithm in providing different throughput and delay assurances to various Differentiated Services (DiffServ) Assured Forwarding (AF) service classes. The results show that POWARED generally outperforms ARED in terms of loss rate and throughput with minimal tradeoffs in delay. DiffServ model is specifically chosen due to its wide market acceptance by industrial players in providing Quality of Service (QoS) across the Internet.
差分服务保证转发服务类别的功率自适应随机早期检测
随着互联网多媒体应用的性能要求的多样化和性质的不断变化,对这些应用的区别对待是不可避免的。在本文中,我们评估了流行的自适应随机早期检测(ARED)[7]算法与我们的POWer自适应随机早期检测(POWARED)[8]算法在为各种差异化服务(DiffServ)保证转发(AF)服务类别提供不同吞吐量和延迟保证方面的性能。结果表明,POWARED在损失率和吞吐量方面通常优于ARED,并且在延迟方面的权衡最小。之所以特别选择DiffServ模型,是因为它在互联网上提供服务质量(QoS)方面被工业参与者广泛接受。
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