A mathematical model for file fragment diffusion and a neural predictor to manage priority queues over BitTorrent

Christian Napoli; Giuseppe Pappalardo; Emiliano Tramontana

International Journal of Applied Mathematics and Computer Science (2016)

  • Volume: 26, Issue: 1, page 147-160
  • ISSN: 1641-876X

Abstract

top
BitTorrent splits the files that are shared on a P2P network into fragments and then spreads these by giving the highest priority to the rarest fragment. We propose a mathematical model that takes into account several factors such as the peer distance, communication delays, and file fragment availability in a future period also by using a neural network module designed to model the behaviour of the peers. The ensemble comprising the proposed mathematical model and a neural network provides a solution for choosing the file fragments that have to be spread first, in order to ensure their continuous availability, taking into account that some peers will disconnect.

How to cite

top

Christian Napoli, Giuseppe Pappalardo, and Emiliano Tramontana. "A mathematical model for file fragment diffusion and a neural predictor to manage priority queues over BitTorrent." International Journal of Applied Mathematics and Computer Science 26.1 (2016): 147-160. <http://eudml.org/doc/276592>.

@article{ChristianNapoli2016,
abstract = {BitTorrent splits the files that are shared on a P2P network into fragments and then spreads these by giving the highest priority to the rarest fragment. We propose a mathematical model that takes into account several factors such as the peer distance, communication delays, and file fragment availability in a future period also by using a neural network module designed to model the behaviour of the peers. The ensemble comprising the proposed mathematical model and a neural network provides a solution for choosing the file fragments that have to be spread first, in order to ensure their continuous availability, taking into account that some peers will disconnect.},
author = {Christian Napoli, Giuseppe Pappalardo, Emiliano Tramontana},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {P2P model; neural network; wavelet; diffusion; file sharing},
language = {eng},
number = {1},
pages = {147-160},
title = {A mathematical model for file fragment diffusion and a neural predictor to manage priority queues over BitTorrent},
url = {http://eudml.org/doc/276592},
volume = {26},
year = {2016},
}

TY - JOUR
AU - Christian Napoli
AU - Giuseppe Pappalardo
AU - Emiliano Tramontana
TI - A mathematical model for file fragment diffusion and a neural predictor to manage priority queues over BitTorrent
JO - International Journal of Applied Mathematics and Computer Science
PY - 2016
VL - 26
IS - 1
SP - 147
EP - 160
AB - BitTorrent splits the files that are shared on a P2P network into fragments and then spreads these by giving the highest priority to the rarest fragment. We propose a mathematical model that takes into account several factors such as the peer distance, communication delays, and file fragment availability in a future period also by using a neural network module designed to model the behaviour of the peers. The ensemble comprising the proposed mathematical model and a neural network provides a solution for choosing the file fragments that have to be spread first, in order to ensure their continuous availability, taking into account that some peers will disconnect.
LA - eng
KW - P2P model; neural network; wavelet; diffusion; file sharing
UR - http://eudml.org/doc/276592
ER -

References

top
  1. Bannò, F., Marletta, D., Pappalardo, G. and Tramontana, E. (2010). Tackling consistency issues for runtime updating distributed systems, Proceedings of the IEEE International Symposium on Parallel & Distributed Processing, Workshops and PhD Forum (IPDPSW), Atlanta, GA, USA, pp. 1-8. 
  2. Bonanno, F., Capizzi, G., Coco, S., Napoli, C., Laudani, A. and Lo Sciuto, G. (2014). Optimal thicknesses determination in a multilayer structure to improve the SPP efficiency for photovoltaic devices by an hybrid FEM-cascade neural network based approach, Proceedings of the IEEE International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), Ischia, Italy, pp. 355-362. 
  3. Bonanno, F., Capizzi, G., Gagliano, A. and Napoli, C. (2012a). Optimal management of various renewable energy sources by a new forecasting method, Proceedings of the IEEE International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), Sorrento, Italy, pp. 934-940. 
  4. Bonanno, F., Capizzi, G. and Napoli, C. (2012b). Some remarks on the application of RNN and PRNN for the charge-discharge simulation of advanced lithium-ions battery energy storage, Proceedings of the IEEE International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), Sorrento, Italy, pp. 941-945. 
  5. Borowik, G., Woźniak, M., Fornaia, A., Giunta, R., Napoli, C., Pappalardo, G. and Tramontana, E. (2015). A software architecture assisting workflow executions on cloud resources, International Journal of Electronics and Telecommunications 61(1): 17-23. 
  6. Calvagna, A. and Tramontana, E. (2013). Delivering dependable reusable components by expressing and enforcing design decisions, Proceedings of the IEEE Computer Software and Applications Conference (COMPSAC) Workshop (QUORS), Kyoto, Japan, pp. 493-498. 
  7. Capizzi, G., Napoli, C. and Paternò, L. (2012). An innovative hybrid neuro-wavelet method for reconstruction of missing data in astronomical photometric surveys, Proceedings of the International Conference on Artificial Intelligence and Soft Computing (ICAISC), Zakopane, Poland, pp. 21-29. 
  8. Chiani, M., Dardari, D. and Simon, M.K. (2003). New exponential bounds and approximations for the computation of error probability in fading channels, IEEE Transactions on Wireless Communications 2(4): 840-845. 
  9. Cohen, B. (2003). Incentives build robustness in BitTorrent, Workshop on Economics of Peer-to-Peer Systems, Berkeley, CA, USA, Vol. 6, pp. 68-72. 
  10. Cohen, B. (2008). The BitTorrent protocol specification, http://jonas.nitro.dk/bittorrent/ bittorrent-rfc.html. 
  11. Connor, J.T., Martin, R.D. and Atlas, L. (1994). Recurrent neural networks and robust time series prediction, Transactions on Neural Networks 5(2): 240-254. 
  12. Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems 2(4): 303-314. Zbl0679.94019
  13. Fornaia, A., Napoli, C., Pappalardo, G. and Tramontana, E. (2015). Using AOP neural networks to infer user behaviours and interests, XVI Workshop “From Object to Agents” (WOA), Napoli, Italy, pp. 46-52. 
  14. Ghit, B., Pop, F. and Cristea, V. (2010). Epidemic-style global load monitoring in large-scale overlay networks, Proceedings of the International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), Fukuoka, Japan, pp. 393-398. 
  15. Giunta, R., Pappalardo, G. and Tramontana, E. (2011). Aspects and annotations for controlling the roles application classes play for design patterns, Proceedings of the IEEE Asia Pacific Software Engineering Conference (APSEC), Ho Chi Minh, Vietnam, pp. 306-314. 
  16. Guo, L., Chen, S., Xiao, Z., Tan, E., Ding, X. and Zhang, X. (2005). Measurements, analysis, and modeling of BitTorrent-like systems, Proceedings of the ACM SIGCOMM Conference on Internet Measurement, Berkeley, CA, USA, pp. 35-48. 
  17. Gupta, M.M., Jin, L. and Homma, N. (2004). Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory, Wiley-IEEE Press, New York, NY. 
  18. Haykin, S. (2009). Neural Networks and Learning Machines, Vol. 3, Prentice Hall, New York, NY. 
  19. Kaune, S., Rumin, R.C., Tyson, G., Mauthe, A., Guerrero, C. and Steinmetz, R. (2010). Unraveling BitTorrent's file unavailability: Measurements and analysis, IEEE International Conference on Peer to Peer Computing (IEEE P2P), Delft, The Netherlands, pp. 1-9. 
  20. Lapedes, A. and Farber, R. (1986). A self-optimizing, nonsymmetrical neural net for content addressable memory and pattern recognition, Physica D: Nonlinear Phenomena 22(1): 247-259. 
  21. Mallat, S. (2009). A Wavelet Tour of Signal Processing: The Sparse Way, Academic Press, Cambridge. Zbl1170.94003
  22. Mandic, D.P. and Chambers, J. (2001). Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability, John Wiley & Sons, Inc., New York, NY. 
  23. Menasche, D.S., Rocha, A.A., Li, B., Towsley, D. and Venkataramani, A. (2009). Content availability and bundling in swarming systems, Proceedings of the ACM Conference Co-NEXT, Rome, Italy, pp. 121-132. 
  24. Napoli, C., Bonanno, F. and Capizzi, G. (2010). Exploiting solar wind time series correlation with magnetospheric response by using an hybrid neuro-wavelet approach, Advances in Plasma Astrophysics: Proceedings of the International Astronomical Union, Giardini Naxos, Italy, pp. 156-158. 
  25. Napoli, C., Pappalardo, G. and Tramontana, E. (2013). A hybrid neuro-wavelet predictor for QoS control and stability, in M. Baldoni et al. (Eds.), Proceedings of Artificial Intelligence (AIxIA), Lecture Notes in Computer Science, Vol. 8249, Springer, Berlin pp. 527-538. 
  26. Napoli, C., Pappalardo, G. and Tramontana, E. (2014a). An agent-driven semantical identifier using radial basis neural networks and reinforcement learning, XV Workshop “From Objects to Agents” (WOA), Catania, Italy, Vol. 1260. 
  27. Napoli, C., Pappalardo, G. and Tramontana, E. (2014b). Improving files availability for BitTorrent using a diffusion model, Proceedings of the IEEE International WETICE Conference, Parma, Italy, pp. 191-196. 
  28. Napoli, C., Pappalardo, G., Tramontana, E., Nowicki, R., Starczewski, J. and Woźniak, M. (2015). Toward work groups classification based on probabilistic neural network approach, in L. Rutkowski et al. (Eds.), Proceedings of the International Conference on Artificial Intelligence and Soft Computing (ICAISC), Lecture Notes in Computer Science, Vol. 9119, Springer, Berlin, pp. 79-89. 
  29. Napoli, C., Pappalardo, G., Tramontana, E. and Zappalà, G. (2016). A cloud-distributed GPU architecture for pattern identification in segmented detectors big-data surveys, Computer Journal 59(3): 338-352, DOI: 10.1093/comjnl/bxu147. 
  30. Nowak, B., Nowicki, R., Woźniak, M. and Napoli, C. (2015). Multi-class nearest neighbour classifier for incomplete data handling, in L. Rutkowski et al. (Eds.), Proceedings of the International Conference on Artificial Intelligence and Soft Computing (ICAISC), Lecture Notes in Computer Science, Vol. 9119, Springer, Berlin, pp. 469-480. 
  31. Qiu, D. and Srikant, R. (2004). Modeling and performance analysis of BitTorrent-like peer-to-peer networks, SIGCOMM Computer Communication Review 34(4): 367-378. 
  32. Rabiner, L.R. and Gold, B. (1975). Theory and Application of Digital Signal Processing, Prentice-Hall, Inc., Englewood Cliffs, NJ. 
  33. Tramontana, E. (2013). Automatically characterising components with concerns and reducing tangling, Proceedings of the IEEE Computer Software and Applications Conference (COMPSAC), Workshop QUORS, Kyoto, Japan, pp. 499-504. 
  34. Visan, A., Pop, F. and Cristea, V. (2011). Decentralized trust management in peer-to-peer systems, Proceedings of the International Symposium on Parallel and Distributed Computing (ISPDC), Cluj-Napoca, Romania, pp. 232-239. 
  35. Williams, R.J. (1989). A learning algorithm for continually running fully recurrent neural networks, Neural Computation 1: 270-280. Williams, R.J. and Zipser, D. (1989). Experimental analysis of the real-time recurrent learning algorithm, Connection Science 1(1): 87-111. 
  36. Woźniak, M., Połap, D., Gabryel, M., Nowicki, R., Napoli, C. and Tramontana, E. (2015). Can we process 2D images using artificial bee colony?, in L. Rutkowski et al. (Eds.), Proceedings of the International Conference on Artificial Intelligence and Soft Computing (ICAISC), Lecture Notes in Computer Science, Vol. 9119, Springer, Berlin, pp. 660-671. 

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.