Social-Insect-Inspired Networking for Autonomous Load Optimisation

Rowlings, Matthew; Tyrrell, Andy M.; Trefzer, Martin A.

doi:10.1016/j.procir.2015.07.062

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…In working towards self-repair of systems, research takes examples from various natural systems and seeks to apply them to industrial applications. Rowlings et al [177] adopts the social behavioural aspects of insects as an inspiration for the design of autonomous self-healing systems for electronic systems in their work relating to 'Networks on Chips' (NoC). These seek to connect large numbers of processing elements by way of 'Nodes' which act as 'distributed colonies' with "….adaptive controllers responsible for controlling the behaviour of each node (member of the colony), relying only on a set of sensory inputs local to each node…."…”

Section: Autonomy Within Tes Systemsmentioning

confidence: 99%

“…These seek to connect large numbers of processing elements by way of 'Nodes' which act as 'distributed colonies' with "….adaptive controllers responsible for controlling the behaviour of each node (member of the colony), relying only on a set of sensory inputs local to each node…." [177] to inform an engineering mitigatory response. McWilliam et al [178] [179] [175] continue in their work into resilient electronic system design where the "…design under evaluation relies upon a novel redundant design strategy intended to provide fault discrimination and selective fault masking embedded within…."…”

Section: Autonomy Within Tes Systemsmentioning

confidence: 99%

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State of the art in Through-life Engineering Services

Redding

Tjahjono

2018

Computers in Industry

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Section: Autonomy Within Tes Systemsmentioning

confidence: 99%

Section: Autonomy Within Tes Systemsmentioning

confidence: 99%

State of the art in Through-life Engineering Services

Redding

Tjahjono

2018

Computers in Industry

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“…reallocations are performed based on observed biological phenomenon. They have been well explored to balance communication loads in networks and distributed systems [Nishitha and Reddy 2012;da Silva Rego et al 2012], communication loads in many-core systems [Rowlings et al 2015], and both computation and communication loads in embedded systems [Mendis et al 2015]. These approaches usually employ distributed resource management to overcome the limitations of centralized and clustered (hierarchical distributed) managements for dynamic applications and large scale many-core systems.…”

Section: Bio-inspired Resource Allocationmentioning

confidence: 99%

A Survey and Comparative Study of Hard and Soft Real-Time Dynamic Resource Allocation Strategies for Multi-/Many-Core Systems

et al. 2017

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Multi/Many-core systems are envisioned to satisfy the ever increasing performance requirements of complex applications in various domains such as embedded and high performance computing (HPC). Such systems need to cater for increasingly dynamic workloads, requiring efficient dynamic resource allocation strategies in order to satisfy hard or soft real-time constraints. This article provides an extensive survey of hard and soft real-time dynamic resource allocation strategies proposed over the last two decades and highlights the emerging trends for multi/many-core systems. The survey covers a taxonomy of the resource allocation strategies and considers their various optimization objectives, which have been used to provide comprehensive comparison. The strategies employ various principles such as market and biological concepts to perform the optimizations. The trend followed by the resource allocation strategies, open research challenges, and likely emerging research directions have also been provided.

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“…Thus, we will need to take a decentralised approach to the Network on Chip that utilises information available at each node instead of relying on a global view of the entire network. We have started to address this approach in our previous work, whereby inspiration was taken from behaviours of social insect colonies and applied to the problem of routing in the Network on Chip [12] [13]. It was shown that social insects are a suitable metaphor for many-core networking as their communication structures fit the decentralised model well; simple communications between members result in self-organising behaviours emerging when observed globally at colony level.…”

Section: Introductionmentioning

confidence: 99%

Social-insect-inspired adaptive task allocation for many-core systems

Rowlings

Tyrrell

Trefzer

2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Large social insect colonies require a wide range of important tasks to be undertaken to build and maintain the colony. Fortunately, in most nests there are many thousands of workers available to offer their assistance to ensure the expansion and survival of the colony. However, there is a crucial equilibrium between the number of workers performing each task that must not only be maintained but must also continuously adapt to sudden changes in environment and colony need. What is most fascinating is that social insects can sustain this balance without any centralised control and with colony members that have relatively little intelligence when considered on their own. Due to this simplicity and evident scalability it would seem that social insects have evolved an interesting scalable approach to task allocation that could be applied to very large many-core systems. To investigate this we have explored biological models of task allocation in ant colonies and applied this to a 36-core Network on Chip. This paper not only shows that effective decentralised task allocation is achieved, but also that such a scheme can adapt to faults and alter its behaviour to meet soft real-time constraints. Therefore, it is established that social insect inspired intelligence models offer a suitable metaphor and development direction for tackling the challenges introduced by dark silicon and in-field faults in a decentralised and adaptive fashion.

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Social-Insect-Inspired Networking for Autonomous Load Optimisation

Cited by 6 publications

References 8 publications

State of the art in Through-life Engineering Services

State of the art in Through-life Engineering Services

A Survey and Comparative Study of Hard and Soft Real-Time Dynamic Resource Allocation Strategies for Multi-/Many-Core Systems

Social-insect-inspired adaptive task allocation for many-core systems

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