Title :
Self-similar traffic prediction using least mean kurtosis
Author :
Zhao, Hong ; Ansari, Nirwan ; Shi, Yun Q.
Author_Institution :
Dept. of Electr. & Comput. Eng., New Jersey Inst. of Technol., Newark, NJ, USA
Abstract :
Recent studies of high quality; high resolution traffic measurements have revealed that network traffic appears to be statistically self similar. Contrary to the common belief, aggregating self-similar traffic streams can actually intensify rather than diminish burstiness. Thus, traffic prediction plays an important role in network management. In this paper, least mean kurtosis (LMK), which uses the negated kurtosis of the error signal as the cost function, is proposed to predict the self similar traffic. Simulation results show that the prediction performance is improved greatly over the least mean square (LMS) algorithm.
Keywords :
Internet; computer network management; statistical analysis; telecommunication traffic; Internet; LMK; burstiness; cost function; error signal; high resolution traffic measurements; least mean kurtosis; negated kurtosis; network management; network traffic; performance; self-similar traffic prediction; Aggregates; Character generation; Higher order statistics; Internet; Least squares approximation; Mathematical model; Predictive models; Resource management; Telecommunication traffic; Traffic control;
Conference_Titel :
Information Technology: Coding and Computing [Computers and Communications], 2003. Proceedings. ITCC 2003. International Conference on
Print_ISBN :
0-7695-1916-4
DOI :
10.1109/ITCC.2003.1197554
