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An adaptive and predictive approach for autonomic multirate multicast networks

An adaptive and predictive approach for autonomic multirate multicast networks An Adaptive and Predictive Approach for Autonomic Multirate Multicast Networks NAIXUE XIONG, Georgia State University ATHANASIOS V. VASILAKOS, University of Western Macedonia LAURENCE T. YANG, St. Francis Xavier University EKRAM HOSSAIN, University of Manitoba Autonomic communications aim at easing the burden of managing complex and dynamic networks, and designing adaptive, self-turning and self-stabilizing networks to provide much needed ‚exibility and functional scalability. With the ever-increasing number of multicast applications made recently, considerable efforts have been focused on the design of adaptive ‚ow control schemes for autonomic multicast services. The main dif culties in designing an adaptive ‚ow controller for autonomic multicast service are caused by heterogeneous multicast receivers, especially those with large propagation delays, since the feedback arriving at the source is somewhat outdated and can be harmful to the control operations. To tackle the preceding problem, this article describes a novel, adaptive, and autonomic multicast scheme, the so-called Proportional, Integrative, Derivative plus Neural Network (PIDNN) predictive technique, which consists of two components: the Proportional Integrative plus Derivative (PID) controller and the Back Propagation BP Neural Network (BPNN). In this integrated scheme, the PID controllers are located at the next upstream main branch nodes of the multicast receivers, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

An adaptive and predictive approach for autonomic multirate multicast networks

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References (53)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISSN
1556-4665
DOI
10.1145/2019583.2019589
Publisher site
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Abstract

An Adaptive and Predictive Approach for Autonomic Multirate Multicast Networks NAIXUE XIONG, Georgia State University ATHANASIOS V. VASILAKOS, University of Western Macedonia LAURENCE T. YANG, St. Francis Xavier University EKRAM HOSSAIN, University of Manitoba Autonomic communications aim at easing the burden of managing complex and dynamic networks, and designing adaptive, self-turning and self-stabilizing networks to provide much needed ‚exibility and functional scalability. With the ever-increasing number of multicast applications made recently, considerable efforts have been focused on the design of adaptive ‚ow control schemes for autonomic multicast services. The main dif culties in designing an adaptive ‚ow controller for autonomic multicast service are caused by heterogeneous multicast receivers, especially those with large propagation delays, since the feedback arriving at the source is somewhat outdated and can be harmful to the control operations. To tackle the preceding problem, this article describes a novel, adaptive, and autonomic multicast scheme, the so-called Proportional, Integrative, Derivative plus Neural Network (PIDNN) predictive technique, which consists of two components: the Proportional Integrative plus Derivative (PID) controller and the Back Propagation BP Neural Network (BPNN). In this integrated scheme, the PID controllers are located at the next upstream main branch nodes of the multicast receivers,

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS) – Association for Computing Machinery

Published: Sep 1, 2011

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