Towards Adaptive Network Nodes via Service Chain Construction

Cheng, Guangming; Chen, Hongchang; Wang, Zhiming; Peng, Yi; Zhang, Fengyu; Hu, Hongchao

doi:10.1109/tnsm.2015.2432116

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…Innovations resembling to core‐periphery remodeling may overcome the overfitting, slow convergence, “fooling effect” and catastrophic forgetting of several current artificial intelligence techniques. Recent Internet innovations recognize the importance of Internet periphery to adapt to variable challenges and involve the design of an adaptable Internet core . Finally, network‐based drug design has recently emerged as a novel paradigm of drug development .…”

Section: Limitations Potential Proofs and Applications Of The Cormentioning

confidence: 99%

The Wisdom of Networks: A General Adaptation and Learning Mechanism of Complex Systems

Csermely

2017

BioEssays

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I hypothesize that re-occurring prior experience of complex systems mobilizes a fast response, whose attractor is encoded by their strongly connected network core. In contrast, responses to novel stimuli are often slow and require the weakly connected network periphery. Upon repeated stimulus, peripheral network nodes remodel the network core that encodes the attractor of the new response. This "core-periphery learning" theory reviews and generalizes the heretofore fragmented knowledge on attractor formation by neural networks, periphery-driven innovation, and a number of recent reports on the adaptation of protein, neuronal, and social networks. The core-periphery learning theory may increase our understanding of signaling, memory formation, information encoding and decision-making processes. Moreover, the power of network periphery-related "wisdom of crowds" inventing creative, novel responses indicates that deliberative democracy is a slow yet efficient learning strategy developed as the success of a billion-year evolution. Also see the video abstract here: https://youtu.be/IIjP7zWGjVE.

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Section: Limitations Potential Proofs and Applications Of The Cormentioning

confidence: 99%

The Wisdom of Networks: A General Adaptation and Learning Mechanism of Complex Systems

Csermely

2017

BioEssays

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“…[ 16 ] proposed a solution for routing traffic in SDN-enabled dynamic network with consolidated middleboxes implemented using virtual Machines. [17] enhanced the adaptability of network nodes via service function chain construction.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Deployment Mechanism for Virtual Middleboxes in NFV- and SDN-Enabling Network

2016

KSII TIIS

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Network Function Virtualization (NFV) and Software Defined Networking (SDN) are recently considered as very promising drivers of the evolution of existing middlebox services, which play intrinsic and fundamental roles in today's networks. To address the virtual service deployment issues that caused by introducing NFV or SDN to networks, this paper proposes an optimal solution by combining quantum genetic algorithm with cooperative game theory. Specifically, we first state the concrete content of the service deployment problem and describe the system framework based on the architecture of SDN. Second, for the service location placement sub-problem, an integer linear programming model is built, which aims at minimizing the network transport delay by selecting suitable service locations, and then a heuristic solution is designed based on the improved quantum genetic algorithm. Third, for the service amount placement sub-problem, we apply the rigorous cooperative game-theoretic approach to build the mathematical model, and implement a distributed algorithm corresponding to Nash bargaining solution. Finally, experimental results show that our proposed method can calculate automatically the optimized placement locations, which reduces 30% of the average traffic delay compared to that of the random placement scheme. Meanwhile, the service amount placement approach can achieve the performance that the average metric values of satisfaction degree and fairness index reach above 90%. And evaluation results demonstrate that our proposed mechanism has a comprehensive advantage for network application.

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Towards Adaptive Network Nodes via Service Chain Construction

Cited by 2 publications

References 27 publications

The Wisdom of Networks: A General Adaptation and Learning Mechanism of Complex Systems

The Wisdom of Networks: A General Adaptation and Learning Mechanism of Complex Systems

An Optimized Deployment Mechanism for Virtual Middleboxes in NFV- and SDN-Enabling Network

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