Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/613337
Title: Quantum-Assisted Joint Multi-Objective Routing and Load Balancing for Socially-Aware Networks
Authors: Dimitrios Alanis;Jie Hu;Panagiotis Botsinis;Zunaira Babar;Soon Xin Ng;Lajos Hanzo
subject: BBHT-QSA|NSGA-II|quantum computing|NDQIO|DHA|DTNs|routing|load balancing|Grover’s QSA
Year: 2016
Publisher: IEEE
Abstract: The widespread use of mobile networking devices, such as smart phones and tablets, has substantially increased the number of nodes in the operational networks. These devices often suffer from the lack of power and bandwidth. Hence, we have to optimize their message routing for the sake of maximizing their capabilities. However, the optimal routing typically relies on a delicate balance of diverse and often conflicting objectives, such as the route&#x2019;s delay and power consumption. The network design also has to consider the nodes&#x2019; user-centric social behavior. Hence, the employment of socially aware load balancing becomes imperative for avoiding the potential formation of bottlenecks in the network&#x2019;s packet-flow. In this paper, we propose a novel algorithm, referred to as the multi-objective decomposition quantum optimization</italic> (MODQO) algorithm, which exploits the quantum parallelism to its full potential by reducing the database correlations for performing multi-objective routing optimization, while at the same time balancing the teletraffic load among the nodes without imposing a substantial degradation on the network&#x2019;s delay and power consumption. Furthermore, we introduce a novel socially aware load balancing metric, namely, the normalized entropy of the normalized composite betweenness of the associated socially aware network, for striking a better tradeoff between the network&#x2019;s delay and power consumption. We analytically prove that the MODQO algorithm achieves the full-search based accuracy at a significantly reduced complexity, which is several orders of magnitude lower than that of the full search. Finally, we compare the MODQO algorithm to the classic non-dominated sort genetic <xref ref-type="algorithm" rid="alg2">algorithm II</xref> evolutionary algorithm and demonstrate that the MODQO succeeds in halving the network&#x2019;s average delay, while simultaneously reducing the network&#x2019;s average power consumption by 6 dB without increasing the computational complexity.
Description: 
URI: http://localhost/handle/Hannan/159020
http://localhost/handle/Hannan/613337
ISSN: 2169-3536
volume: 4
Appears in Collections:2016

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Title: Quantum-Assisted Joint Multi-Objective Routing and Load Balancing for Socially-Aware Networks
Authors: Dimitrios Alanis;Jie Hu;Panagiotis Botsinis;Zunaira Babar;Soon Xin Ng;Lajos Hanzo
subject: BBHT-QSA|NSGA-II|quantum computing|NDQIO|DHA|DTNs|routing|load balancing|Grover’s QSA
Year: 2016
Publisher: IEEE
Abstract: The widespread use of mobile networking devices, such as smart phones and tablets, has substantially increased the number of nodes in the operational networks. These devices often suffer from the lack of power and bandwidth. Hence, we have to optimize their message routing for the sake of maximizing their capabilities. However, the optimal routing typically relies on a delicate balance of diverse and often conflicting objectives, such as the route&#x2019;s delay and power consumption. The network design also has to consider the nodes&#x2019; user-centric social behavior. Hence, the employment of socially aware load balancing becomes imperative for avoiding the potential formation of bottlenecks in the network&#x2019;s packet-flow. In this paper, we propose a novel algorithm, referred to as the multi-objective decomposition quantum optimization</italic> (MODQO) algorithm, which exploits the quantum parallelism to its full potential by reducing the database correlations for performing multi-objective routing optimization, while at the same time balancing the teletraffic load among the nodes without imposing a substantial degradation on the network&#x2019;s delay and power consumption. Furthermore, we introduce a novel socially aware load balancing metric, namely, the normalized entropy of the normalized composite betweenness of the associated socially aware network, for striking a better tradeoff between the network&#x2019;s delay and power consumption. We analytically prove that the MODQO algorithm achieves the full-search based accuracy at a significantly reduced complexity, which is several orders of magnitude lower than that of the full search. Finally, we compare the MODQO algorithm to the classic non-dominated sort genetic <xref ref-type="algorithm" rid="alg2">algorithm II</xref> evolutionary algorithm and demonstrate that the MODQO succeeds in halving the network&#x2019;s average delay, while simultaneously reducing the network&#x2019;s average power consumption by 6 dB without increasing the computational complexity.
Description: 
URI: http://localhost/handle/Hannan/159020
http://localhost/handle/Hannan/613337
ISSN: 2169-3536
volume: 4
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7745885.pdf9.56 MBAdobe PDFThumbnail
Preview File
Title: Quantum-Assisted Joint Multi-Objective Routing and Load Balancing for Socially-Aware Networks
Authors: Dimitrios Alanis;Jie Hu;Panagiotis Botsinis;Zunaira Babar;Soon Xin Ng;Lajos Hanzo
subject: BBHT-QSA|NSGA-II|quantum computing|NDQIO|DHA|DTNs|routing|load balancing|Grover’s QSA
Year: 2016
Publisher: IEEE
Abstract: The widespread use of mobile networking devices, such as smart phones and tablets, has substantially increased the number of nodes in the operational networks. These devices often suffer from the lack of power and bandwidth. Hence, we have to optimize their message routing for the sake of maximizing their capabilities. However, the optimal routing typically relies on a delicate balance of diverse and often conflicting objectives, such as the route&#x2019;s delay and power consumption. The network design also has to consider the nodes&#x2019; user-centric social behavior. Hence, the employment of socially aware load balancing becomes imperative for avoiding the potential formation of bottlenecks in the network&#x2019;s packet-flow. In this paper, we propose a novel algorithm, referred to as the multi-objective decomposition quantum optimization</italic> (MODQO) algorithm, which exploits the quantum parallelism to its full potential by reducing the database correlations for performing multi-objective routing optimization, while at the same time balancing the teletraffic load among the nodes without imposing a substantial degradation on the network&#x2019;s delay and power consumption. Furthermore, we introduce a novel socially aware load balancing metric, namely, the normalized entropy of the normalized composite betweenness of the associated socially aware network, for striking a better tradeoff between the network&#x2019;s delay and power consumption. We analytically prove that the MODQO algorithm achieves the full-search based accuracy at a significantly reduced complexity, which is several orders of magnitude lower than that of the full search. Finally, we compare the MODQO algorithm to the classic non-dominated sort genetic <xref ref-type="algorithm" rid="alg2">algorithm II</xref> evolutionary algorithm and demonstrate that the MODQO succeeds in halving the network&#x2019;s average delay, while simultaneously reducing the network&#x2019;s average power consumption by 6 dB without increasing the computational complexity.
Description: 
URI: http://localhost/handle/Hannan/159020
http://localhost/handle/Hannan/613337
ISSN: 2169-3536
volume: 4
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7745885.pdf9.56 MBAdobe PDFThumbnail
Preview File