The Tensor Model of Ports Buffering for Digital Switch


The objective of the paper is to develop the tensor model of buffer storage allocation for the input-output ports of digital network switch. To this end the method of segregated lists applied where each external physical port of the switch comprises internal transit buffers for all other external ports. Through experiments performed, we introduce the notion of zero port for the switch that connects external ports with the switch itself as the object of addressing object. The buffering matrix and the quaternion of an arbitrary couple of buffers is provided for the switch ports. The reduced port buffering matrix is obtained for the even size case of the one direction interacting input and output buffers. The reduced matrix decomposes in symmetric and anti-symmetric buffering matrix parties. The symmetric buffering matrix is mapped onto the real metric buffering tensor. The anti-symmetric buffering matrix is mapped on two tensors: the real curvature tensor of buffering and complex rotor tensor of buffering. We conclude that it is feasible to use dynamic storage allocation in network switches based on the buffering tensor model.

Publication year: 
С. 34—39. Іл. 5. Бібліогр.: 15 назв.

1. Next Generation Networks Global Standards Initiative [Online]. Available: Pages/default.aspx
2. P.R. Wilson et al. Dynamic Storage allocation: A Survey and Critical Review. Dp. of Computer Sciences Univ.of Texas at Austin [Online]. Available: http://www.arnetminer. org/
3. D. Lea. A Memory Allocator [Online]. Available: http://
4. The BGET Memory Allocator [Online]. Available: http://
5. M. Masmano et al. Dynamic storage allocation for realtime embedded systems. Universidad Polit.ecnica de Valencia, Spain [Online]. Available: ~zaher/rtss-wip/24.pdf
6. D.E. Knuth, The Art of Computer Programming, vol. 1: Fundamental Algorithms. Massachusetts: Addison-Wesley, 1973, 634 p.
7. T. Ogasawara, “An algorithm with constant execution time for dynamic storage allocation”, in 2nd Int. Workshop on Real-Time Computing Systems and Applications. Tokyo, Oct. 25—27, 1995, p. 21.
8. TLSF Memory Allocator [Online]. Available: http://www.ocera. org/download/components/WP5/dynmem-1.4.html
9. Управление трафиком ATM. — Режим доступа: http:// prtrafir-ATM/index.htm
10. L. Kleinrock (July 1961). Information Flow in Large Communication Nets, in RLE Quarterly Progress Report [Online]. Available: REPORT/RLEreport-1961.html
11. D. Kriesel. A Brief Introduction to Neural Networks [Online]. Available: neural_networks
12. Крон Г. Тензорный анализ сетей: Пер. с англ. — М.: Сов. радио, 1978. — 720 с.
13. Петров А.Е. Тензорная методология в теории систем. — М.: Радио и связь, 1985. — 152 с.
14. Тихонов В.И. Построение тензорной модели асимметричных цифровых потоков в комплексном пространстве // Проблеми телекомунікацій. — 2011. — № 2 (4). — С. 42—53.
15. Корн Г., Корн Т. Справочник по математике (для научных работников и инженеров). — М.: Наука, 1973. — 832 с.

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