Rules for Biologically Inspired Adaptive Network Design

Tero, Atsushi; Takagi, Seiji; Saigusa, Toyohei; Ito, Kentaro; Bebber, Dan; Fricker, Mark D.; Yumiki, Kenji; Kobayashi, Ryo; Nakagaki, Toshiyuki

doi:10.1126/science.1177894

Cited by 747 publications

(656 citation statements)

References 21 publications

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“…Mycelial fungi and acellular slime molds grow as selforganized networks that explore new territory for food sources, whilst maintaining an effective internal transport system to resist continuous attacks or random damage (Fessel et al, 2012). Honed by evolution, these biological networks are examples of adaptive transportation networks, balancing real-world compromises between search strategy and transport efficiency (Tero et al, 2010).…”

Section: B Controlling Adaptive Networkmentioning

confidence: 99%

Control principles of complex systems

Liu¹,

Barabási²

2016

Rev. Mod. Phys.

511

338

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A reflection of our ultimate understanding of a complex system is our ability to control its behavior. Typically, control has multiple prerequisites: it requires an accurate map of the network that governs the interactions between the system's components, a quantitative description of the dynamical laws that govern the temporal behavior of each component, and an ability to influence the state and temporal behavior of a selected subset of the components. With deep roots in nonlinear dynamics and control theory, notions of control and controllability have taken a new life recently in the study of complex networks, inspiring several fundamental questions: What are the control principles of complex systems? How do networks organize themselves to balance control with functionality? To address these here we review recent advances on the controllability and the control of complex networks, exploring the intricate interplay between a system's structure, captured by its network topology, and the dynamical laws that govern the interactions between the components. We match the pertinent mathematical results with empirical findings and applications. We show that uncovering the control principles of complex systems can help us explore and ultimately understand the fundamental laws that govern their behavior.

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Section: B Controlling Adaptive Networkmentioning

confidence: 99%

Control principles of complex systems

Liu¹,

Barabási²

2016

Rev. Mod. Phys.

511

338

View full text Add to dashboard Cite

show abstract

“…Cellular slime moulds [1] are single cell amoebiod organisms that have the ability to aggregate together upon release of a chemical signal in order to form an aggregate that can move as if it were a larger organism. This behaviour has been harnessed to solve a range of optimisation problems, for example planning an urban rail network [36]. In our analogy individual IPTV content items are the single cell slime moulds, which upon the release of a signal (higher request rejection rates) aggregate together and replicate themselves on a remote storage server.…”

Section: Illustration Of Slime Mould Content Replicationmentioning

confidence: 99%

“…For example Tero et al [36] used slime moulds to emulate the design of the shortest path topology of the Greater Tokyo railway network.…”

Section: Related Workmentioning

confidence: 99%

Federation Lifecycle Management Incorporating Coordination of Bio-inspired Self-management Processes

et al. 2013

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As it has evolved, the Internet has had to support a broadening range of networking technologies, business models and user interaction modes. Researchers and industry practitioners have realised that this trend necessitates a fundamental rethinking of approaches to network and service management. This has spurred significant research efforts towards developing autonomic network management solutions incorporating distributed self-management processes inspired by biological systems. Whilst significant advances have been made, most solutions focus on management of single network domains and the optimisation of specific management or control processes therein. In this paper we argue that a networking infrastructure providing a myriad of loosely coupled services must inherently support federation of network domains and facilitate coordination of the operation of various management processes for mutual benefit. To this end, we outline a framework for federated management that facilitates the coordination of the behaviour of bio-inspired management processes. Using a case study relating to distribution of IPTV content, we describe how Federal Relationship Managers realising our layered model of management federations can communicate to manage service provision across multiple application/storage/network providers. We outline an illustrative example in which storage providers are dynamically added to a federation to accommodate demand spikes, with appropriate content being migrated to those providers servers under control of a bio-inspired replication process.

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“…Laboratory experiments and theoretical studies have shown that the slime mould can solve many graph theoretical problems, such as finding the shortest path [26][27][28][29][30][31][32][33], shortest path tree problem [34], network formulation and simulation [25,26,37], influential nodes identification [38], connecting different arrays of food sources in an efficient manner [39][40][41][42], network design [43][44][45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

A Physarum-inspired approach to supply chain network design

Adamatzky

Yang

et al. 2016

Sci. China Inf. Sci.

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A supply chain is a system which moves products from a supplier to customers, which plays a very important role in all economic activities. This paper proposes a novel algorithm for a supply chain network design inspired by biological principles of nutrients' distribution in protoplasmic networks of slime mould Physarum polycephalum. The algorithm handles supply networks where capacity investments and product flows are decision variables, and the networks are required to satisfy product demands. Two features of the slime mould are adopted in our algorithm. The first is the continuity of flux during the iterative process, which is used in realtime updating of the costs associated with the supply links. The second feature is adaptivity. The supply chain can converge to an equilibrium state when costs are changed. Numerical examples are provided to illustrate the practicality and flexibility of the proposed method algorithm.

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Rules for Biologically Inspired Adaptive Network Design

Cited by 747 publications

References 21 publications

Control principles of complex systems

Control principles of complex systems

Federation Lifecycle Management Incorporating Coordination of Bio-inspired Self-management Processes

A Physarum-inspired approach to supply chain network design

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