Agent-based Distance Vector Routing: A Resource Efficient and Scalable approach to Routing in Large Communication Networks Page: 2
The following text was automatically extracted from the image on this page using optical character recognition software:
is directly related to the size of the network (or autonomous systems). That
is, traditional routing algorithms do not scale well with increasing network
dimensions. In fact, it is the lack of scalability of these mechanisms that
forces hierarchical structuring of a large network into autonomous domains.
It has been observed, that the message overhead due to routing table up-
dates increases drastically as the size of the autonomous system increases
(Malkin and Streenstrup 1995). This increase in message activity is intrinsic
to the implementation of most routing algorithms that are in use today, and is
necessary to ensure that changes in routing cost are propagated throughout the
network. Both, Distance Vector and Link State routing algorithms manifest a
distributed version of shortest path algorithms (Bertsekas and Gallager 1987)
designed for graphs (i.e., Bellman-Ford and Dijkstra).
In existing networks, the importance of fast route discovery and low routing
delays surpasses the requirement of low resource overhead. Hence, aggressive
messaging is deemed essential to quickly propagate local information from
individual routers, thereby enabling other routers to utilize this information
in routing decisions. It is the resource overhead incurred by this massively
concurrent messaging that limits the scalability of these routing algorithms.
Even though we witness an ever-increasing availability of network resources
in conventional networks, the tremendous increase in network traffic makes it
necessary to re-visit the fundamental design of current routing methodologies
to find ways to limit the need for excessive messaging. Particularly in view of
recent developments in mobile ad-hoc networks, which are characterized by
limited bandwidth, memory, and computing power, it is imperative to find new
ways of reducing resource overhead associated with routing algorithms. The
goal is to devise a simple, resource efficient, scalable routing algorithm that
discovers optimal routes expediently yet does so with bounded message activ-
ity. Towards this goal, this paper proposes the formulation of a new routing
strategy that exploits the intelligent mobile agent paradigm. In comparison
to ongoing research efforts that pursue the design of new routing paradigms,
which exploit concepts such as reinforcement learning, this paper addresses
the issue of propagating routing information in the network. Specifically, this
paper focusses on the design and evaluation of an agent-based Distance-Vector
Routing algorithm that facilitates scalability, resource awareness, and fault tol-
erance. The effort is motivated by two conjectures, which have been validated
through a number of carefully crafted experiments.
Conjecture 1 It is possible to bound the degree of message concurrency of
distance-vector routing without significantly affecting the convergence behavior
of the algorithm.
Conjecture 2 It is possible to dynamically control and effectively regulate the
degree of message concurrency without centralized control or global knowledge
of the state of the network.
Here’s what’s next.
This article can be searched. Note: Results may vary based on the legibility of text within the document.
Tools / Downloads
Get a copy of this page or view the extracted text.
Citing and Sharing
Basic information for referencing this web page. We also provide extended guidance on usage rights, references, copying or embedding.
Reference the current page of this Article.
Amin, Kaizar A. & Mikler, Armin R. Agent-based Distance Vector Routing: A Resource Efficient and Scalable approach to Routing in Large Communication Networks, article, March 25, 2002; [New York, New York]. (digital.library.unt.edu/ark:/67531/metadc111275/m1/2/: accessed September 25, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.