Multi agent quality of service routing based on scheme ant colony optimization algorithm

The current Internet needs to support a wide variety of applications with different demands in terms of Quality of Service (QoS) requirements including constraints on throughput, delay, jitter, loss rate. Differentiated Service (DiffServ) has been proposed by the Internet Engineering Task Force (...

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Main Author:	Baygi, Maassoumeh Javadi
Format:	Thesis
Language:	English
Published:	2014
Subjects:	Routing (Computer network management) - Mathematical models Computer network protocols
Online Access:	http://psasir.upm.edu.my/id/eprint/76128/1/ITMA%202014%2014%20IR.pdf
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spelling	my-upm-ir.761282019-11-27T06:16:19Z Multi agent quality of service routing based on scheme ant colony optimization algorithm 2014-01 Baygi, Maassoumeh Javadi The current Internet needs to support a wide variety of applications with different demands in terms of Quality of Service (QoS) requirements including constraints on throughput, delay, jitter, loss rate. Differentiated Service (DiffServ) has been proposed by the Internet Engineering Task Force (IETF), motivated by the desire to serve different types of traffic in IP networks. While much of the existing work on DiffServ focus on scheduling policies, in order to provide QoS for high-quality Internet applications, QoSaware path selection is important. Known as QoS Routing (QoSR), this multiple constraints path selection is considered NP-Complete. Some algorithms provide QoS in term of routing, but they usually focus on improving the performance of the highest priority traffic, at the expense of the lower priority classes. In order to fully exploit the network resources, and to meet QoS requirements of various classes concurrently, it is important to establish class dependent paths. Therefore, an algorithm which can adaptively assign network resources to meet the QoS requirements of different classes simultaneously is still needed. This research presents a per class QoS routing approach based on Ant Colony Optimization (ACO) called ACR-QoS to provide QoS for different Class of Services (CoSs). Traffic of different classes can be distributed in accordance with network states and QoS requirements. The important new aspect of ACR-QoS is the combination of constraint-based routing and DiffServ architecture, in Swarm Intelligence (SI) structure where a set of artificial ants is used to determine the optimal path for each class to construct class-based routing tables. This approach allows easy and efficient design of service classes, while no changes are needed at lower layers. This study also introduces a new probe-based procedure for discovery and setup QoS path for real-time traffic. The proposed scheme has been simulated by OMNET++ and compared with standard AntNet and two well-known standard QoS routings; Widest Shortest Path (WSP) algorithm and Shortest Widest Path (SWP) algorithm. Experiment results show that the desired service class differentiation is obtained and ACR-QoS satisfies the QoS requirements of each CoS without discriminating against the best-effort traffic. In comparison with standard AntNet, results confirm that the modifications and extensions applied to AntNet can significantly reduce the average packet delay and jitter by about 11.8% and 46% respectively in transient regime. It can also improve the throughput in link failure state by about 25% and decrease the packet loss rate by 27% and 39% before and during link failure respectively. Moreover, the proposed approach outperforms the WSP and SWP algorithms, where network efficiency in saturated load gains an 8.39% improvement while the satisfaction of delay and jitter constraints for time critical applications are also achieved. In saturated load, efficiency is a very important parameter and a few changes in it can result in high performance of network to delivery of data. Routing (Computer network management) - Mathematical models Computer network protocols 2014-01 Thesis http://psasir.upm.edu.my/id/eprint/76128/ http://psasir.upm.edu.my/id/eprint/76128/1/ITMA%202014%2014%20IR.pdf text en public doctoral Universiti Putra Malaysia Routing (Computer network management) - Mathematical models Computer network protocols
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Routing (Computer network management) - Mathematical models Computer network protocols
spellingShingle	Routing (Computer network management) - Mathematical models Computer network protocols Baygi, Maassoumeh Javadi Multi agent quality of service routing based on scheme ant colony optimization algorithm
description	The current Internet needs to support a wide variety of applications with different demands in terms of Quality of Service (QoS) requirements including constraints on throughput, delay, jitter, loss rate. Differentiated Service (DiffServ) has been proposed by the Internet Engineering Task Force (IETF), motivated by the desire to serve different types of traffic in IP networks. While much of the existing work on DiffServ focus on scheduling policies, in order to provide QoS for high-quality Internet applications, QoSaware path selection is important. Known as QoS Routing (QoSR), this multiple constraints path selection is considered NP-Complete. Some algorithms provide QoS in term of routing, but they usually focus on improving the performance of the highest priority traffic, at the expense of the lower priority classes. In order to fully exploit the network resources, and to meet QoS requirements of various classes concurrently, it is important to establish class dependent paths. Therefore, an algorithm which can adaptively assign network resources to meet the QoS requirements of different classes simultaneously is still needed. This research presents a per class QoS routing approach based on Ant Colony Optimization (ACO) called ACR-QoS to provide QoS for different Class of Services (CoSs). Traffic of different classes can be distributed in accordance with network states and QoS requirements. The important new aspect of ACR-QoS is the combination of constraint-based routing and DiffServ architecture, in Swarm Intelligence (SI) structure where a set of artificial ants is used to determine the optimal path for each class to construct class-based routing tables. This approach allows easy and efficient design of service classes, while no changes are needed at lower layers. This study also introduces a new probe-based procedure for discovery and setup QoS path for real-time traffic. The proposed scheme has been simulated by OMNET++ and compared with standard AntNet and two well-known standard QoS routings; Widest Shortest Path (WSP) algorithm and Shortest Widest Path (SWP) algorithm. Experiment results show that the desired service class differentiation is obtained and ACR-QoS satisfies the QoS requirements of each CoS without discriminating against the best-effort traffic. In comparison with standard AntNet, results confirm that the modifications and extensions applied to AntNet can significantly reduce the average packet delay and jitter by about 11.8% and 46% respectively in transient regime. It can also improve the throughput in link failure state by about 25% and decrease the packet loss rate by 27% and 39% before and during link failure respectively. Moreover, the proposed approach outperforms the WSP and SWP algorithms, where network efficiency in saturated load gains an 8.39% improvement while the satisfaction of delay and jitter constraints for time critical applications are also achieved. In saturated load, efficiency is a very important parameter and a few changes in it can result in high performance of network to delivery of data.
format	Thesis
qualification_level	Doctorate
author	Baygi, Maassoumeh Javadi
author_facet	Baygi, Maassoumeh Javadi
author_sort	Baygi, Maassoumeh Javadi
title	Multi agent quality of service routing based on scheme ant colony optimization algorithm
title_short	Multi agent quality of service routing based on scheme ant colony optimization algorithm
title_full	Multi agent quality of service routing based on scheme ant colony optimization algorithm
title_fullStr	Multi agent quality of service routing based on scheme ant colony optimization algorithm
title_full_unstemmed	Multi agent quality of service routing based on scheme ant colony optimization algorithm
title_sort	multi agent quality of service routing based on scheme ant colony optimization algorithm
granting_institution	Universiti Putra Malaysia
publishDate	2014
url	http://psasir.upm.edu.my/id/eprint/76128/1/ITMA%202014%2014%20IR.pdf
_version_	1747813124518445056

Multi agent quality of service routing based on scheme ant colony optimization algorithm

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