2017
Stai, E.; Sotiropoulos, K.; Karyotis, V.; Papavassiliou, S.
Hyperbolic Embedding for Efficient Computation of Path Centralities and Adaptive Routing in Large-Scale Complex Commodity Networks Journal Article
In: IEEE Transactions on Network Science and Engineering, vol. 4, no. 3, pp. 140-153, 2017, ISSN: 23274697, (cited By 10).
Abstract | Links | BibTeX | Tags: Betweenness centrality; Greedy routing; Hyperbolic geometry; Network embedding; rigel embedding; Traffic loads, Complex networks; Computational efficiency; Distributed computer systems; Traffic congestion, Network routing
@article{Stai2017140,
title = {Hyperbolic Embedding for Efficient Computation of Path Centralities and Adaptive Routing in Large-Scale Complex Commodity Networks},
author = {E. Stai and K. Sotiropoulos and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029930433&doi=10.1109%2fTNSE.2017.2690258&partnerID=40&md5=0a5e420781c16b09d6ecee7b614924c3},
doi = {10.1109/TNSE.2017.2690258},
issn = {23274697},
year = {2017},
date = {2017-01-01},
journal = {IEEE Transactions on Network Science and Engineering},
volume = {4},
number = {3},
pages = {140-153},
publisher = {IEEE Computer Society},
abstract = {Computing the most central nodes in large-scale commodity networks is rather important for improving routing and associated applications. In this paper, we introduce a novel framework for the analysis and efficient computation of routing path-based centrality measures, focusing on betweenness and traffic load centrality. The proposed framework enables efficient approximation and in special cases accurate computation of the aforementioned measures in large-scale complex networks, as well as improving/adapting commodity (traffic) routing by identifying and alleviating key congestion points. It capitalizes on network embedding in hyperbolic space and exploits properties of greedy routing over hyperbolic coordinates. We show the computational benefits and approximation precision of our approach by comparing it with state-of-the-art path centrality computation techniques. We demonstrate its applicability on real topologies, characteristic of actual large-scale commodity networks, e.g., data, utility networks. Focusing on two graph embedding types, Rigel and greedy, we compare their impact on the performance of our framework. Then, we exemplify and statistically analyze the dynamic routing adaptation, via the variation of the minimum-depth spanning tree employed for greedy embedding in hyperbolic space. Notably, this allows for efficient routing adaptation according to a simple, distributed computation that can be applied during network operation to alleviate arising bottlenecks. © 2013 IEEE.},
note = {cited By 10},
keywords = {Betweenness centrality; Greedy routing; Hyperbolic geometry; Network embedding; rigel embedding; Traffic loads, Complex networks; Computational efficiency; Distributed computer systems; Traffic congestion, Network routing},
pubstate = {published},
tppubtype = {article}
}
Computing the most central nodes in large-scale commodity networks is rather important for improving routing and associated applications. In this paper, we introduce a novel framework for the analysis and efficient computation of routing path-based centrality measures, focusing on betweenness and traffic load centrality. The proposed framework enables efficient approximation and in special cases accurate computation of the aforementioned measures in large-scale complex networks, as well as improving/adapting commodity (traffic) routing by identifying and alleviating key congestion points. It capitalizes on network embedding in hyperbolic space and exploits properties of greedy routing over hyperbolic coordinates. We show the computational benefits and approximation precision of our approach by comparing it with state-of-the-art path centrality computation techniques. We demonstrate its applicability on real topologies, characteristic of actual large-scale commodity networks, e.g., data, utility networks. Focusing on two graph embedding types, Rigel and greedy, we compare their impact on the performance of our framework. Then, we exemplify and statistically analyze the dynamic routing adaptation, via the variation of the minimum-depth spanning tree employed for greedy embedding in hyperbolic space. Notably, this allows for efficient routing adaptation according to a simple, distributed computation that can be applied during network operation to alleviate arising bottlenecks. © 2013 IEEE.
2016
Stai, E.; Sotiropoulos, K.; Karyotis, V.; Papavassiliou, S.
Hyperbolic Traffic Load Centrality for large-scale complex communications networks Conference
Institute of Electrical and Electronics Engineers Inc., 2016, ISBN: 9781509019908, (cited By 3; Conference of 23rd International Conference on Telecommunications, ICT 2016 ; Conference Date: 16 May 2016 Through 18 May 2016; Conference Code:122441).
Abstract | Links | BibTeX | Tags: Analysis and simulation; Communications networks; Greedy routing; Hyperbolic geometry; Network embedding; Scale-free properties; Shortest path routing; Traffic loads, Complex networks; Graph theory; Telecommunication networks, Network routing
@conference{Stai2016,
title = {Hyperbolic Traffic Load Centrality for large-scale complex communications networks},
author = {E. Stai and K. Sotiropoulos and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979205129&doi=10.1109%2fICT.2016.7500371&partnerID=40&md5=f46ed087a57f7adf10247c593797f09b},
doi = {10.1109/ICT.2016.7500371},
isbn = {9781509019908},
year = {2016},
date = {2016-01-01},
journal = {2016 23rd International Conference on Telecommunications, ICT 2016},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Estimating accurately important nodes for routing in modern and future networks is a key process with numerous benefits. Towards this goal, in this paper we propose Hyperbolic Traffic Load Centrality (HTLC), as a novel alternative to the Traffic Load Centrality (TLC) metric, used for ranking nodes with respect to their importance in the routing operation. HTLC is based on network embedding in hyperbolic space, while assuming paths paved by greedy routing over hyperbolic coordinates, which requires less computational effort than shortest path routing (in terms of hop distances) used for TLC. Greedy routing in hyperbolic space also yields paths with lengths very close to the shortest ones for the social networks of interest bearing the scale-free property. Through analysis and simulation, we demonstrate that HTLC requires significantly lower computational time than TLC, and despite being more suitable for greedy routing constraints over hyperbolic space, it nevertheless achieves a close approximation of TLC for networks with scale-free properties when assuming shortest path routing. Thus, it can substitute TLC when analyzing very large network topologies. © 2016 IEEE.},
note = {cited By 3; Conference of 23rd International Conference on Telecommunications, ICT 2016 ; Conference Date: 16 May 2016 Through 18 May 2016; Conference Code:122441},
keywords = {Analysis and simulation; Communications networks; Greedy routing; Hyperbolic geometry; Network embedding; Scale-free properties; Shortest path routing; Traffic loads, Complex networks; Graph theory; Telecommunication networks, Network routing},
pubstate = {published},
tppubtype = {conference}
}
Estimating accurately important nodes for routing in modern and future networks is a key process with numerous benefits. Towards this goal, in this paper we propose Hyperbolic Traffic Load Centrality (HTLC), as a novel alternative to the Traffic Load Centrality (TLC) metric, used for ranking nodes with respect to their importance in the routing operation. HTLC is based on network embedding in hyperbolic space, while assuming paths paved by greedy routing over hyperbolic coordinates, which requires less computational effort than shortest path routing (in terms of hop distances) used for TLC. Greedy routing in hyperbolic space also yields paths with lengths very close to the shortest ones for the social networks of interest bearing the scale-free property. Through analysis and simulation, we demonstrate that HTLC requires significantly lower computational time than TLC, and despite being more suitable for greedy routing constraints over hyperbolic space, it nevertheless achieves a close approximation of TLC for networks with scale-free properties when assuming shortest path routing. Thus, it can substitute TLC when analyzing very large network topologies. © 2016 IEEE.
2013
Arkoulis, S.; Anifantis, E.; Karyotis, V.; Papavassiliou, S.; Mitrou, N.
On the optimal, fair and channel-aware cognitive radio network reconfiguration Journal Article
In: Computer Networks, vol. 57, no. 8, pp. 1739-1757, 2013, ISSN: 13891286, (cited By 16).
Abstract | Links | BibTeX | Tags: Channel assignment and routing; Channel switching; Cognitive radio network; Fairness; MILP; Network re-configuration; problem, Cognitive radio; Heuristic methods; Integer programming; Optimization; Radio systems, Network routing
@article{Arkoulis20131739,
title = {On the optimal, fair and channel-aware cognitive radio network reconfiguration},
author = {S. Arkoulis and E. Anifantis and V. Karyotis and S. Papavassiliou and N. Mitrou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878115702&doi=10.1016%2fj.comnet.2013.03.004&partnerID=40&md5=f0b5125bfb88b55dda37a775e106f7ff},
doi = {10.1016/j.comnet.2013.03.004},
issn = {13891286},
year = {2013},
date = {2013-01-01},
journal = {Computer Networks},
volume = {57},
number = {8},
pages = {1739-1757},
abstract = {In this work, we focus on the Joint Channel Assignment and Routing (JCAR) problem in Cognitive Radio Networks (CRNs) and especially on the optimal reconfiguration of secondary networks under the presence of primary users. Secondary CRN users need to adapt their transmission channels promptly, while effectively limit additional or escalating system modifications triggered by the intertweaved primary user activity. Our approach takes into consideration the underlying spectrum switching dynamics and concurrently aims at a fair resource allocation among the active network flows. We take an optimization perspective and formulate the JCAR and network reconfiguration problems as mixed integer linear programs, addressing fairness concerns as well. We propose a heuristic approach which is based on a sequential reduced search space methodology, in order to obtain efficiently solutions of otherwise tough and demanding reconfiguration problems. The operation, effectiveness and performance of the proposed mechanisms are evaluated through analysis and simulations under various working conditions. The obtained numerical results indicate the benefits of the proposed schemes in terms of overhead performance and their scaling properties with respect to more realistic and thus demanding topologies. © 2013 Elsevier B.V. All rights reserved.},
note = {cited By 16},
keywords = {Channel assignment and routing; Channel switching; Cognitive radio network; Fairness; MILP; Network re-configuration; problem, Cognitive radio; Heuristic methods; Integer programming; Optimization; Radio systems, Network routing},
pubstate = {published},
tppubtype = {article}
}
In this work, we focus on the Joint Channel Assignment and Routing (JCAR) problem in Cognitive Radio Networks (CRNs) and especially on the optimal reconfiguration of secondary networks under the presence of primary users. Secondary CRN users need to adapt their transmission channels promptly, while effectively limit additional or escalating system modifications triggered by the intertweaved primary user activity. Our approach takes into consideration the underlying spectrum switching dynamics and concurrently aims at a fair resource allocation among the active network flows. We take an optimization perspective and formulate the JCAR and network reconfiguration problems as mixed integer linear programs, addressing fairness concerns as well. We propose a heuristic approach which is based on a sequential reduced search space methodology, in order to obtain efficiently solutions of otherwise tough and demanding reconfiguration problems. The operation, effectiveness and performance of the proposed mechanisms are evaluated through analysis and simulations under various working conditions. The obtained numerical results indicate the benefits of the proposed schemes in terms of overhead performance and their scaling properties with respect to more realistic and thus demanding topologies. © 2013 Elsevier B.V. All rights reserved.