IEICE TRANSACTIONS on Communications
Autonomous Decentralized Control for Indirectly Controlling System Performance Variable of Large-Scale and Wide-Area Networks
Summary :
In this paper, we propose a novel Autonomous Decentralized Control (ADC) scheme for indirectly controlling a system performance variable of large-scale and wide-area networks. In a large-scale and wide-area network, since it is impractical for any one node to gather full information of the entire network, network control must be realized by inter-node collaboration using information local to each node. Several critical network problems (e.g., resource allocation) are often formulated by a system performance variable that is an amount to quantify system state. We solve such problems by designing an autonomous node action that indirectly controls, via the Markov Chain Monte Carlo method, the probability distribution of a system performance variable by using only local information. Analyses based on statistical mechanics confirm the effectiveness of the proposed node action. Moreover, the proposal is used to implement traffic-aware virtual machine placement control with load balancing in a data center network. Simulations confirm that it can control the system performance variable and is robust against system fluctuations. A comparison against a centralized control scheme verifies the superiority of the proposal.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E98-B No.11 pp.2248-2258
- Publication Date
- 2015/11/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E98.B.2248
- Type of Manuscript
- PAPER
- Category
- Network
Authors
Yusuke SAKUMOTO
Tokyo Metropolitan University
Masaki AIDA
Tokyo Metropolitan University
Hideyuki SHIMONISHI
NEC Corporation
Keyword
Latest Issue
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Yusuke SAKUMOTO, Masaki AIDA, Hideyuki SHIMONISHI, "Autonomous Decentralized Control for Indirectly Controlling System Performance Variable of Large-Scale and Wide-Area Networks" in IEICE TRANSACTIONS on Communications,
vol. E98-B, no. 11, pp. 2248-2258, November 2015, doi: 10.1587/transcom.E98.B.2248.
Abstract: In this paper, we propose a novel Autonomous Decentralized Control (ADC) scheme for indirectly controlling a system performance variable of large-scale and wide-area networks. In a large-scale and wide-area network, since it is impractical for any one node to gather full information of the entire network, network control must be realized by inter-node collaboration using information local to each node. Several critical network problems (e.g., resource allocation) are often formulated by a system performance variable that is an amount to quantify system state. We solve such problems by designing an autonomous node action that indirectly controls, via the Markov Chain Monte Carlo method, the probability distribution of a system performance variable by using only local information. Analyses based on statistical mechanics confirm the effectiveness of the proposed node action. Moreover, the proposal is used to implement traffic-aware virtual machine placement control with load balancing in a data center network. Simulations confirm that it can control the system performance variable and is robust against system fluctuations. A comparison against a centralized control scheme verifies the superiority of the proposal.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E98.B.2248/_p
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@ARTICLE{e98-b_11_2248,
author={Yusuke SAKUMOTO, Masaki AIDA, Hideyuki SHIMONISHI, },
journal={IEICE TRANSACTIONS on Communications},
title={Autonomous Decentralized Control for Indirectly Controlling System Performance Variable of Large-Scale and Wide-Area Networks},
year={2015},
volume={E98-B},
number={11},
pages={2248-2258},
abstract={In this paper, we propose a novel Autonomous Decentralized Control (ADC) scheme for indirectly controlling a system performance variable of large-scale and wide-area networks. In a large-scale and wide-area network, since it is impractical for any one node to gather full information of the entire network, network control must be realized by inter-node collaboration using information local to each node. Several critical network problems (e.g., resource allocation) are often formulated by a system performance variable that is an amount to quantify system state. We solve such problems by designing an autonomous node action that indirectly controls, via the Markov Chain Monte Carlo method, the probability distribution of a system performance variable by using only local information. Analyses based on statistical mechanics confirm the effectiveness of the proposed node action. Moreover, the proposal is used to implement traffic-aware virtual machine placement control with load balancing in a data center network. Simulations confirm that it can control the system performance variable and is robust against system fluctuations. A comparison against a centralized control scheme verifies the superiority of the proposal.},
keywords={},
doi={10.1587/transcom.E98.B.2248},
ISSN={1745-1345},
month={November},}
Copy
TY - JOUR
TI - Autonomous Decentralized Control for Indirectly Controlling System Performance Variable of Large-Scale and Wide-Area Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 2248
EP - 2258
AU - Yusuke SAKUMOTO
AU - Masaki AIDA
AU - Hideyuki SHIMONISHI
PY - 2015
DO - 10.1587/transcom.E98.B.2248
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E98-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2015
AB - In this paper, we propose a novel Autonomous Decentralized Control (ADC) scheme for indirectly controlling a system performance variable of large-scale and wide-area networks. In a large-scale and wide-area network, since it is impractical for any one node to gather full information of the entire network, network control must be realized by inter-node collaboration using information local to each node. Several critical network problems (e.g., resource allocation) are often formulated by a system performance variable that is an amount to quantify system state. We solve such problems by designing an autonomous node action that indirectly controls, via the Markov Chain Monte Carlo method, the probability distribution of a system performance variable by using only local information. Analyses based on statistical mechanics confirm the effectiveness of the proposed node action. Moreover, the proposal is used to implement traffic-aware virtual machine placement control with load balancing in a data center network. Simulations confirm that it can control the system performance variable and is robust against system fluctuations. A comparison against a centralized control scheme verifies the superiority of the proposal.
ER -
English
