Abstract
Computing and updating distributed shortest paths is a core functionality of today’s communication networks. The solutions known in the literature are classified into two categories, namely Distance-Vector and Link-State algorithms. Distance-Vector algorithms usually require each node of the network to store the distance toward every other node in a data structure called routing table, thus requiring linear storage per node. Such a data structure is used to compute the next hop to be used to forward data toward any destination node of interest. This is usually done by solving very simple equations, thus requiring few computational time per node. The main drawback of Distance-Vector algorithms is that, in dynamic scenarios, they can suffer of the looping and count-to-infinity phenomena, though quite efficient countermeasures for such issues are known. Link-State algorithms, instead, require a node of the network to know and store the entire network topology, to compute its distance and next hop toward any destination. This is usually done by means of a centralized shortest-path algorithm, hence requiring quadratic storage and rather high computational effort per node. The main drawback of Link-State algorithms is that, notwithstanding they do not incur in looping and count-to-infinity problems, they perform quite poorly in dynamic scenarios, since nodes need to receive and store up-to-date information on the entire network topology after each change. In the last years, there has been a renewed interest in devising new light-weight distributed shortest-path solutions for large-scale Ethernet networks, where Distance-Vector algorithms are an attractive alternative to Link-State solutions when scalability and reliability are key issues or when the memory resources of the nodes of the network are limited. In this chapter, we hence focus on Distance-Vector solutions by reviewing classic approaches and recent algorithmic developments in this category.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
S. Cicerone, G. D’Angelo, G. Di Stefano, D. Frigioni, Partially dynamic efficient algorithms for distributed shortest paths. Theor. Comput. Sci. 411, 1013–1037 (2010)
S. Cicerone, G.D. Stefano, D. Frigioni, U. Nanni, A fully dynamic algorithm for distributed shortest paths. Theor. Comput. Sci. 297(1–3), 83–102 (2003)
P.A. Humblet, Another adaptive distributed shortest path algorithm. IEEE Trans. Commun. 39(6), 995–1002 (1991)
G.F. Italiano, Distributed algorithms for updating shortest paths, in Lecture notes in computer science on international workshop on distributed algorithms, vol. 579, pp. 200–211 (1991)
A. Orda, R. Rom, Distributed shortest-path and minimum-delay protocols in networks with time-dependent edge-length. Distributed Comput. 10, 49–62 (1996)
K.V.S. Ramarao, S. Venkatesan, On finding and updating shortest paths distributively. J. Algorithms 13, 235–257 (1992)
J. McQuillan, Adaptive routing algorithms for distributed computer networks. Technical Report BBN Report 2831, (Cambridge, MA, 1974)
E.C. Rosen, The updating protocol of arpanet’s new routing algorithm. Comput. Netw. 4, 11–19 (1980)
D. Bertsekas, R. Gallager, Data networks (Prentice Hall International, 1992)
B. Awerbuch, A. Bar-Noy, M. Gopal, Approximate distributed bellman-ford algorithms. IEEE Trans. Commun. 42(8), 2515–2517 (1994)
J.T. Moy, OSPF: anatomy of an internet routing protocol, (Addison-Wesley 1998)
E.W. Dijkstra, A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959)
J. Wu, F. Dai, X. Lin, J. Cao, W. Jia, An extended fault-tolerant link-state routing protocol in the internet. IEEE Trans. Comput. 52(10), 1298–1311 (2003)
F. Bruera, S. Cicerone, G. D’Angelo, G.D. Stefano, D. Frigioni, Dynamic multi-level overlay graphs for shortest paths. Math. Comput. Sci. 1(4), 709–736 (2008)
D. Frigioni, A. Marchetti-Spaccamela, U. Nanni, Fully dynamic algorithms for maintaining shortest paths trees. J. Algorithms 34(2), 251–281 (2000)
P. Narváez, K.-Y. Siu, H.-Y. Tzeng, New dynamic algorithms for shortest path tree computation. IEEE/ACM Trans. Netw. 8(6), 734–746 (2000)
S. Cicerone, G. D’Angelo, G. Di Stefano, D. Frigioni, V. Maurizio, Engineering a new algorithm for distributed shortest paths on dynamic networks. Algorithmica 66(1), 51–86 (2013)
G. D’Angelo, M. D’Emidio, D. Frigioni, Pruning the computation of distributed shortest paths in power-law networks. Informatica 37(3), 253–265 (2013)
K. Elmeleegy, A.L. Cox, T.S.E. Ng On count-to-infinity induced forwarding loops in ethernet networks in Proceedings of 25th IEEE conference on computer communications (INFOCOM2006), pp. 1–13 (2006)
A. Myers, E. Ng, H. Zhang, Rethinking the service model: Scaling ethernet to a million nodes, in Proceedings 3rd workshop on hot topics in networks (ACM HotNets). (ACM Press, 2004)
S. Ray, R. Guérin, K.-W. Kwong, R. Sofia, Always acyclic distributed path computation. IEEE/ACM Trans. Netw. 18(1), 307–319 (2010)
N. Yao, E. Gao, Y. Qin, H. Zhang, Rd: reducing message overhead in DUAL, in Proceedings of1st international conference on network infrastructure and digital content (IC-NIDC2009), (IEEE Press, 2009) pp. 270–274
C. Zhao, Y. Liu, K. Liu, A more efficient diffusing update algorithm for loop-free routing, in Proceedings 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCom2009), (IEEE Press, 2009) pp. 1–4
J.J. Garcia-Lunes-Aceves, Loop-free routing using diffusing computations. IEEE/ACM Trans. Netw. 1(1), 130–141 (1993)
EIGRP, Enhanced interior gateway routing protocol. http://www.cisco.com/c/en/us/support/docs/ip/enhanced-interior-gateway-routing-protocol-eigrp/16406-eigrp-toc.html
G. D’Angelo, M. D’Emidio, D. Frigioni, A loop-free shortest-path routing algorithm for dynamic networks. Theor. Comput. Sci. 516, 1–19 (2014)
G. D’Angelo, M. D’Emidio, D. Frigioni, D. Romano, Enhancing the computation of distributed shortest paths on power-law networks in dynamic scenarios. Theor. Comput. Syst. 57(2), 444–477 (2015)
OMNeT++, Discrete event simulation environment. http://www.omnetpp.org
Y. Hyun, B. Huffaker, D. Andersen, E. Aben, C. Shannon, M. Luckie, K. Claffy, The CAIDA IPv4 routed/24 topology dataset. http://www.caida.org/data/active/ipv4_routed_24_topology_dataset.xml
R. Albert, A.-L. Barabási, Emergence of scaling in random networks. Science 286, 509–512 (1999)
H. Attiya, J. Welch, Distributed Computing Wiley (2004)
E.W. Dijkstra, C.S. Scholten, Termination detection for diffusing computations. Informat. Process. Lett. 11, 1–4 (1980)
N.A. Lynch, Distributed algorithms, Morgan Kaufmann Publishers (1996)
K. Pahlavan, P. Krishnamurthy, Networking fundamentals: Wide (Wiley, Local and Personal Area Communications, 2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
D’Angelo, G., D’Emidio, M., Frigioni, D. (2018). Distance-Vector Algorithms for Distributed Shortest Paths Computation in Dynamic Networks. In: Adamatzky, A. (eds) Shortest Path Solvers. From Software to Wetware. Emergence, Complexity and Computation, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-319-77510-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-77510-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77509-8
Online ISBN: 978-3-319-77510-4
eBook Packages: EngineeringEngineering (R0)