Evaluating the Performance of A4SDN on Various Network Topologies

Load balancing is one of the main goals of Software-Defined Networking (SDN). Distributing the traffic among the possible paths of a network according to specific rules allows SDN systems to increase their global performance index in term of resource usage and scalability. In a previous paper, the authors introduced Adaptive Alienated Ant Algorithm for Software-Defined Networking (A4SDN), a distributed, adaptive, load-balancing algorithm for traffic engineering on SDN showing its ability to optimise the network performance in terms of throughput, communication delay and packet loss rate. In this paper, the authors analyse how the performance of A4SDN are influenced by the underlying topology. For each considered topology, the performance of A4SDN are successfully with those of Extended Dijkstra algorithm. The tests show that the performance of A4SDN are the best when the average number of available shortest paths for each couple of nodes rises.

[1]  Constandinos X. Mavromoustakis,et al.  Ant based probabilistic routing with pheromone and antipheromone mechanisms , 2004, Int. J. Commun. Syst..

[2]  Jehn-Ruey Jiang,et al.  Extending Dijkstra's shortest path algorithm for software defined networking , 2014, The 16th Asia-Pacific Network Operations and Management Symposium.

[3]  Richard Wang,et al.  OpenFlow-Based Server Load Balancing Gone Wild , 2011, Hot-ICE.

[4]  Luca Maria Gambardella,et al.  An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem , 2000, INFORMS J. Comput..

[5]  Antonella Di Stefano,et al.  A4SDN - Adaptive Alienated Ant Algorithm for Software-Defined Networking , 2015, 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC).

[6]  Antonella Di Stefano,et al.  A bio-inspired distributed algorithm to improve scheduling performance of multi-broker grids , 2012, Natural Computing.

[7]  Kwang Mong Sim,et al.  Multiple Ant Colony Optimization for Load Balancing , 2003, IDEAL.

[8]  Andrzej Pacut,et al.  Ant Routing with Distributed Geographical Localization of Knowledge in Ad-Hoc Networks , 2009, EvoWorkshops.

[9]  Min Zhu,et al.  WCMP: weighted cost multipathing for improved fairness in data centers , 2014, EuroSys '14.

[10]  Amin Vahdat,et al.  Hedera: Dynamic Flow Scheduling for Data Center Networks , 2010, NSDI.

[11]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[12]  Edward Balas GlobalNOC,et al.  SciPass : a 100 Gbps capable secure Science DMZ using OpenFlow and Bro , 2014 .

[13]  Lianggui Liu,et al.  A Novel Ant Colony Based QoS-Aware Routing Algorithm for MANETs , 2005, ICNC.

[14]  Kwang Mong Sim,et al.  Ant colony optimization for routing and load-balancing: survey and new directions , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[15]  Antonella Di Stefano,et al.  Evaluating the Robustness of the Alienated Ant Algorithm in Grids , 2010, 2010 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises.

[16]  Muddassar Farooq,et al.  Routing Protocols for Next-Generation Networks Inspired by Collective Behaviors of Insect Societies: An Overview , 2008, Swarm Intelligence.

[17]  Antonella Di Stefano,et al.  Grid jobs scheduling: The Alienated Ant Algorithm solution , 2010, Multiagent Grid Syst..

[18]  J. Rexford,et al.  Efficient Traffic Splitting on SDN Switches , 2015 .

[19]  Li Bai,et al.  Qcolony: a Multi-pheromone Best-fit Qos Routing Algorithm as an Alternative to Shortest-path Routing Algorithms , 2005, Int. J. Comput. Intell. Appl..

[20]  Zdravko Bozakov,et al.  Flow-based load balancing in multipathed layer-2 networks using OpenFlow and multipath-TCP , 2014, HotSDN.

[21]  Janet Bruten,et al.  Ant-like agents for load balancing in telecommunications networks , 1997, AGENTS '97.

[22]  Praveen Yalagandula,et al.  Mahout: Low-overhead datacenter traffic management using end-host-based elephant detection , 2011, 2011 Proceedings IEEE INFOCOM.

[23]  Luca Maria Gambardella,et al.  AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks , 2005, Eur. Trans. Telecommun..

[24]  Stefka Fidanova,et al.  Ant Algorithm for Grid Scheduling Problem , 2005, LSSC.

[25]  Marco Dorigo,et al.  AntNet: Distributed Stigmergetic Control for Communications Networks , 1998, J. Artif. Intell. Res..

[26]  C.-w. Chiang,et al.  Ant colony optimisation for task matching and scheduling , 2006 .

[27]  Richard F. Hartl,et al.  D-Ants: Savings Based Ants divide and conquer the vehicle routing problem , 2004, Comput. Oper. Res..

[28]  Gunjan Tank,et al.  Software-Defined Networking-The New Norm for Networks , 2012 .

[29]  Albert G. Greenberg,et al.  The nature of data center traffic: measurements & analysis , 2009, IMC '09.

[30]  Christian E. Hopps,et al.  Analysis of an Equal-Cost Multi-Path Algorithm , 2000, RFC.