The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam kertas kerja ini, kami mencadangkan teknik rangkaian P2P evolusi yang secara dinamik dan adaptif mengoptimumkan beberapa topologi rangkaian P2P, di mana semua nod disertakan pada masa yang sama, dalam cara evolusi mengikut kriteria penilaian yang diberikan. Di samping itu, melalui simulasi, kami mengkaji sama ada teknik rangkaian P2P evolusi yang dicadangkan boleh menyediakan keupayaan carian yang boleh dipercayai dalam persekitaran P2P dinamik. Dalam simulasi, kami menganggap persekitaran P2P dinamik di mana setiap nod meninggalkan dan menyertai rangkaian dengan kebarangkalian sendiri dan objek carian berubah mengikut masa. Keputusan simulasi menunjukkan bahawa pembinaan semula topologi oleh teknik rangkaian P2P evolusi adalah lebih baik daripada pembinaan semula topologi rawak apabila hanya beberapa jenis objek carian terdapat dalam rangkaian pada bila-bila masa dan objek carian ini tidak direplikasi. Selain itu, untuk senario di mana teknik rangkaian P2P evolusi adalah lebih berkesan, kami menunjukkan melalui simulasi bahawa apabila setiap nod membuat beberapa pautan dengan nod lain dalam topologi rangkaian tunggal, teknik rangkaian P2P evolusioner meningkatkan keupayaan carian yang boleh dipercayai. Akhir sekali, bilangan pautan yang menghasilkan keupayaan carian yang lebih dipercayai nampaknya bergantung pada kekerapan nod keluar dan menyertai rangkaian.
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Salinan
Kei OHNISHI, Yuji OIE, "Evolutionary P2P Networking That Fuses Evolutionary Computation and P2P Networking Together" in IEICE TRANSACTIONS on Communications,
vol. E93-B, no. 2, pp. 317-327, February 2010, doi: 10.1587/transcom.E93.B.317.
Abstract: In the present paper, we propose an evolutionary P2P networking technique that dynamically and adaptively optimizes several P2P network topologies, in which all of the nodes are included at the same time, in an evolutionary manner according to given evaluation criteria. In addition, through simulations, we examine whether the proposed evolutionary P2P networking technique can provide reliable search capability in dynamic P2P environments. In simulations, we assume dynamic P2P environments in which each node leaves and joins the network with its own probability and in which search objects vary with time. The simulation results show that topology reconstruction by the evolutionary P2P networking technique is better than random topology reconstruction when only a few types of search objects are present in the network at any moment and these search objects are not replicated. Moreover, for the scenario in which the evolutionary P2P networking technique is more effective, we show through simulations that when each node makes several links with other nodes in a single network topology, the evolutionary P2P networking technique improves the reliable search capability. Finally, the number of links that yields more reliable search capability appears to depend on how often nodes leave and join the network.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E93.B.317/_p
Salinan
@ARTICLE{e93-b_2_317,
author={Kei OHNISHI, Yuji OIE, },
journal={IEICE TRANSACTIONS on Communications},
title={Evolutionary P2P Networking That Fuses Evolutionary Computation and P2P Networking Together},
year={2010},
volume={E93-B},
number={2},
pages={317-327},
abstract={In the present paper, we propose an evolutionary P2P networking technique that dynamically and adaptively optimizes several P2P network topologies, in which all of the nodes are included at the same time, in an evolutionary manner according to given evaluation criteria. In addition, through simulations, we examine whether the proposed evolutionary P2P networking technique can provide reliable search capability in dynamic P2P environments. In simulations, we assume dynamic P2P environments in which each node leaves and joins the network with its own probability and in which search objects vary with time. The simulation results show that topology reconstruction by the evolutionary P2P networking technique is better than random topology reconstruction when only a few types of search objects are present in the network at any moment and these search objects are not replicated. Moreover, for the scenario in which the evolutionary P2P networking technique is more effective, we show through simulations that when each node makes several links with other nodes in a single network topology, the evolutionary P2P networking technique improves the reliable search capability. Finally, the number of links that yields more reliable search capability appears to depend on how often nodes leave and join the network.},
keywords={},
doi={10.1587/transcom.E93.B.317},
ISSN={1745-1345},
month={February},}
Salinan
TY - JOUR
TI - Evolutionary P2P Networking That Fuses Evolutionary Computation and P2P Networking Together
T2 - IEICE TRANSACTIONS on Communications
SP - 317
EP - 327
AU - Kei OHNISHI
AU - Yuji OIE
PY - 2010
DO - 10.1587/transcom.E93.B.317
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E93-B
IS - 2
JA - IEICE TRANSACTIONS on Communications
Y1 - February 2010
AB - In the present paper, we propose an evolutionary P2P networking technique that dynamically and adaptively optimizes several P2P network topologies, in which all of the nodes are included at the same time, in an evolutionary manner according to given evaluation criteria. In addition, through simulations, we examine whether the proposed evolutionary P2P networking technique can provide reliable search capability in dynamic P2P environments. In simulations, we assume dynamic P2P environments in which each node leaves and joins the network with its own probability and in which search objects vary with time. The simulation results show that topology reconstruction by the evolutionary P2P networking technique is better than random topology reconstruction when only a few types of search objects are present in the network at any moment and these search objects are not replicated. Moreover, for the scenario in which the evolutionary P2P networking technique is more effective, we show through simulations that when each node makes several links with other nodes in a single network topology, the evolutionary P2P networking technique improves the reliable search capability. Finally, the number of links that yields more reliable search capability appears to depend on how often nodes leave and join the network.
ER -