NDLI: Information theoretic approach to traffic adaptive WDM networks

Content Provider	ACM Digital Library
Author	Murthy, C. Siva Ram Sinha, Sushant
Abstract	WDM networks adapt to the changes in traffic by reconfiguring the virtual topology. Though reconfiguration is done with the objective of utilizing resources efficiently, the resulting disruption in traffic is a cause for concern. Hence, policies are formed to decide on the time (i.e., when) to trigger reconfiguration and the new virtual topology that is most beneficial to the network. We present a simple, general and flexible framework, based on the two conflicting objectives of efficient resource utilization and minimizing traffic disruption, to evaluate reconfiguration policies. Instead of re-determining the reconfiguration policy whenever the traffic changes, we present Incremental Clustering Algorithm (ICA) to pre-plan the reconfiguration policy for a fully predictable finite sequence of traffic matrices. Since full predictability of such a sequence is not possible in practice, we learn the traffic sequences in order to probabilistically predict the future ones. From an information theoretic point of view, we quantify the predictability of traffic sequences and the number of times the reconfiguration policy is re-determined for any WDM network. To optimally predict the future traffic sequences and to incur optimal cost in the re-determination of the reconfiguration policy, we propose Universal Reconfiguration Management System (URMS). A Prediction-based Incremental Clustering Algorithm (PICA) that extends ICA is used by URMS to predict the reconfiguration policy. Within URMS, the probabilities are assigned to the traffic sequences by the prediction schemes of LZ78. We performed extensive simulations to study the effectiveness and efficiency of URMS when compared to the fully predictable and totally unpredictable models. The performance of URMS improves with learning and nearly achieves the performance of a fully predictable model.
Starting Page	881
Ending Page	894
Page Count	14
File Format	PDF
ISSN	10636692
DOI	10.1109/TNET.2005.852877
Volume Number	13
Issue Number	4
Journal	IEEE/ACM Transactions on Networking (TON)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-08-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	LZ78 algorithm WDM networks Network learning Prediction of traffic sequences Reconfiguration policy Virtual topology design Virtual topology reconfiguration
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Networks and Communications Software Computer Science Applications

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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