350,169 research outputs found
Comparative Study Of Congestion Control Techniques In High Speed Networks
Congestion in network occurs due to exceed in aggregate demand as compared to
the accessible capacity of the resources. Network congestion will increase as
network speed increases and new effective congestion control methods are
needed, especially to handle bursty traffic of todays very high speed networks.
Since late 90s numerous schemes i.e. [1]...[10] etc. have been proposed. This
paper concentrates on comparative study of the different congestion control
schemes based on some key performance metrics. An effort has been made to judge
the performance of Maximum Entropy (ME) based solution for a steady state
GE/GE/1/N censored queues with partial buffer sharing scheme against these key
performance metrics.Comment: 10 pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS November 2009, ISSN 1947 5500,
http://sites.google.com/site/ijcsis
Fast k-means algorithm clustering
k-means has recently been recognized as one of the best algorithms for
clustering unsupervised data. Since k-means depends mainly on distance
calculation between all data points and the centers, the time cost will be high
when the size of the dataset is large (for example more than 500millions of
points). We propose a two stage algorithm to reduce the time cost of distance
calculation for huge datasets. The first stage is a fast distance calculation
using only a small portion of the data to produce the best possible location of
the centers. The second stage is a slow distance calculation in which the
initial centers used are taken from the first stage. The fast and slow stages
represent the speed of the movement of the centers. In the slow stage, the
whole dataset can be used to get the exact location of the centers. The time
cost of the distance calculation for the fast stage is very low due to the
small size of the training data chosen. The time cost of the distance
calculation for the slow stage is also minimized due to small number of
iterations. Different initial locations of the clusters have been used during
the test of the proposed algorithms. For large datasets, experiments show that
the 2-stage clustering method achieves better speed-up (1-9 times).Comment: 16 pages, Wimo2011; International Journal of Computer Networks &
Communications (IJCNC) Vol.3, No.4, July 201
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