Distance-Vector Algorithms for Distributed Shortest Paths Computation in Dynamic Networks

Part of the Emergence, Complexity and Computation book series (ECC, volume 32)


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.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Gran Sasso Science Institute (GSSI)L’AquilaItaly
  2. 2.Department of Information Engineering, Computer Science and MathematicsUniversity of L’AquilaL’AquilaItaly

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