28 research outputs found
An Optimised Shortest Path Algorithm for Network Rotuting & SDN: Improvement on Bellman-Ford Algorithm
Network routing algorithms form the backbone of data transmission in modern network architectures, with implications for efficiency, speed, and reliability. This research aims to critically investigate and compare three prominent routing algorithms: Bellman-Ford, Shortest Path Faster Algorithm (SPFA), and our novel improved variant of Bellman-Ford, the Space-efficient Cost-Balancing Bellman-Ford (SCBF). We evaluate the performance of these algorithms in terms of time and space complexity, memory utilization, and routing efficacy, within a simulated network environment. Our results indicate that while Bellman-Ford provides consistent performance, both SPFA and SCBF present improvements in specific scenarios with the SCBF showing notable enhancements in space efficiency. The innovative SCBF algorithm provides competitive performance and greater space efficiency, potentially making it a valuable contribution to the development of network routing protocols. Further research is encouraged to optimize and evaluate these algorithms in real-world network conditions. This study underscores the continuous need for algorithmic innovation in response to evolving network demands
On Compact Routing for the Internet
While there exist compact routing schemes designed for grids, trees, and
Internet-like topologies that offer routing tables of sizes that scale
logarithmically with the network size, we demonstrate in this paper that in
view of recent results in compact routing research, such logarithmic scaling on
Internet-like topologies is fundamentally impossible in the presence of
topology dynamics or topology-independent (flat) addressing. We use analytic
arguments to show that the number of routing control messages per topology
change cannot scale better than linearly on Internet-like topologies. We also
employ simulations to confirm that logarithmic routing table size scaling gets
broken by topology-independent addressing, a cornerstone of popular
locator-identifier split proposals aiming at improving routing scaling in the
presence of network topology dynamics or host mobility. These pessimistic
findings lead us to the conclusion that a fundamental re-examination of
assumptions behind routing models and abstractions is needed in order to find a
routing architecture that would be able to scale ``indefinitely.''Comment: This is a significantly revised, journal version of cs/050802
A Practical Parallel Algorithm for Diameter Approximation of Massive Weighted Graphs
We present a space and time efficient practical parallel algorithm for
approximating the diameter of massive weighted undirected graphs on distributed
platforms supporting a MapReduce-like abstraction. The core of the algorithm is
a weighted graph decomposition strategy generating disjoint clusters of bounded
weighted radius. Theoretically, our algorithm uses linear space and yields a
polylogarithmic approximation guarantee; moreover, for important practical
classes of graphs, it runs in a number of rounds asymptotically smaller than
those required by the natural approximation provided by the state-of-the-art
-stepping SSSP algorithm, which is its only practical linear-space
competitor in the aforementioned computational scenario. We complement our
theoretical findings with an extensive experimental analysis on large benchmark
graphs, which demonstrates that our algorithm attains substantial improvements
on a number of key performance indicators with respect to the aforementioned
competitor, while featuring a similar approximation ratio (a small constant
less than 1.4, as opposed to the polylogarithmic theoretical bound)
Faster Clustering via Preprocessing
We examine the efficiency of clustering a set of points, when the
encompassing metric space may be preprocessed in advance. In computational
problems of this genre, there is a first stage of preprocessing, whose input is
a collection of points ; the next stage receives as input a query set
, and should report a clustering of according to some
objective, such as 1-median, in which case the answer is a point
minimizing .
We design fast algorithms that approximately solve such problems under
standard clustering objectives like -center and -median, when the metric
has low doubling dimension. By leveraging the preprocessing stage, our
algorithms achieve query time that is near-linear in the query size ,
and is (almost) independent of the total number of points .Comment: 24 page
On space-stretch trade-offs: upper bounds
One of the fundamental trade-offs in compact routing schemes is between the space used to store the routing table on each node and the stretch factor of the routing scheme – the maximum ratio over all pairs between the cost of the route induced by the scheme and the cost of a minimum cost path between the same pair. All previous routing schemes required storage that is dependent on the diameter of the network. We present a new scale-free routing scheme, whose storage and header sizes are independent of the aspect ratio of the network. Our scheme is based on a decomposition into sparse and dense neighborhoods. Given an undirected network with arbitrary weights and n arbitrary node names, for any integer k ≥ 1 we present the first scale-free routing scheme with asymptotically optimal space-stretch tradeoff that does not require edge weights to be polynomially bounded. The scheme uses e O(n 1/k) space routing table at each node, and routes along paths of asymptotically optimal linear stretch O(k)