Routing Control of Packet Flow by using Statistical-Physical Methods
| Project/Area Number |
14084202
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| Research Category |
Grant-in-Aid for Scientific Research on Priority Areas
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| Allocation Type | Single-year Grants |
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| Review Section |
Science and Engineering
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| Research Institution | Tohoku University |
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Principal Investigator |
HORIGUCHI Tsuyoshi Tohoku University, Graduate School of Information Sciences, Professor, 大学院情報科学研究科, 教授 (30005558)
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| Project Period (FY) |
2002 – 2004
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥14,100,000 (Direct Cost: ¥14,100,000)
Fiscal Year 2005: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2004: ¥5,200,000 (Direct Cost: ¥5,200,000)
Fiscal Year 2003: ¥6,300,000 (Direct Cost: ¥6,300,000)
Fiscal Year 2002: ¥1,800,000 (Direct Cost: ¥1,800,000)
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| Keywords | packet routing control / computer network / reinforcement learning / scale-free network / congestion-avoidance / statistical physics / probabilistic mathematical model / mean-field approximation / パケット流 / ルーチング制御 / ネットワークの構造 / 確率情報処理 / ニューラルネットワーク / スモールワールド / 確率的情報処理 |
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| Research Abstract |
1. Study on packet routing control
We have proposed statistical-physical models which are decentralized, autonomous and adaptive for packet flow on a large computer network. An energy function is defined in order to express competition among queue length, distance to destination of a packet. The packet routing control is performed by using the energy function. We have proposed several formulations using neural networks, Ising models, or reinforcement learning systems. It has been shown that these models give good performance for packet routing control.
2. Study on designing of optimal network strictures for packet flow
We have investigated an optimal network structure for packet flow on large networks by defining a cost function which represents efficiency of the packet flow. As a result, we have found that random networks are optimal in the case where a destination is determined randomly, and networks with scale-free property are optimal in the case where the buffer-size distribution has a power-law form and a destination is determined with the probability which is proportional to the buffer size of the node.
3. Study on generating mechanism of scale-free networks
We have studied on possibility of self-organization of nongrowing networks into scale-free networks. By introducing fitness parameters in rewiring processes, we have shown that networks with scale-free property are generated also in the nongrowing case. The same behavior has been observed in the nongrowing model for bipartite graphs.
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Report
(4 results)
Research Products
(23 results)
