G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

Caglayan, M.U.

doi:10.1017/s0269964817000171

Cited by 11 publications

(9 citation statements)

References 182 publications

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“…G-networks [22,23] have had numerous applications to Gene Regulatory Networks [24], Neural Networks [25] as tools to develop complex Pattern Analysis algorithms [6] and to control routing in packet networks [26,27]. Their transient behaviour [28] has been recently examined and other applications are discussed in [7].…”

Section: Discussionmentioning

confidence: 99%

“…Queueing models with "product form" solutions and their efficient computational algorithms [1,2] are useful in many engineering fields including computer systems and networks [3,4], machine learning [5][6][7], transportation systems [8,9], job-shop and manufacturing systems [10], and emergency evacuation [11,12]. G-networks [13][14][15] are a significant extension of earlier queueing models [16].…”

Section: Introductionmentioning

confidence: 99%

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G-Networks with Adders

Fourneau

Gelenbe

2017

Future Internet

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Queueing networks are used to model the performance of the Internet, of manufacturing and job-shop systems, supply chains, and other networked systems in transportation or emergency management. Composed of service stations where customers receive service, and then move to another service station till they leave the network, queueing networks are based on probabilistic assumptions concerning service times and customer movement that represent the variability of system workloads. Subject to restrictive assumptions regarding external arrivals, Markovian movement of customers, and service time distributions, such networks can be solved efficiently with "product form solutions" that reduce the need for software simulators requiring lengthy computations. G-networks generalise these models to include the effect of "signals" that reroute customer traffic, or negative customers that reject service requests, and also have a convenient product form solution. This paper extends G-networks by including a new type of signal, that we call an "Adder", which probabilistically changes the queue length at the service center that it visits, acting as a load regulator. We show that this generalisation of G-networks has a product form solution.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

G-Networks with Adders

Fourneau

Gelenbe

2017

Future Internet

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“…. If an unique non-negative solution of the traffic equations given in (4) exists such that the conditions 0 < q 1,i < 1 and 0 < q 2,i+N < 1 for i = 1, . .…”

Section: Product-form Solution Of the Epn Modelmentioning

confidence: 99%

Optimal Energy Distribution With Energy Packet Networks

Zhang¹

2019

Prob. Eng. Inf. Sci.

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We use Energy Packet Network paradigms to investigate energy distribution problems in a computer system with energy harvesting and storages units. Our goal is to minimize both the overall average response time of jobs at workstations and the total rate of energy lost in the network. Energy is lost when it arrives at idle workstations which are empty. Energy is also lost in storage leakages. We assume that the total rate of energy harvesting and the rate of jobs arriving at workstations are known. We also consider a special case in which the total rate of energy harvesting is sufficiently large so that workstations are less busy. In this case, energy is more likely to be sent to an idle workstation. Optimal solutions are obtained which minimize both the overall response time and energy loss under the constraint of a fixed energy harvesting rate.

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“…Since their introduction, G-networks have been extensively studied covering several extensions such as triggered movement, which redirect customers among the queues [15]; catastrophes or batch service [18], adders [19]; multiple classes of positive customers and signals [20], state-dependent service disciplines [21,22,23], tandem networks [24,25], deletion of a random amount of work [26,27], retrials [28,29] (not exhaustive list). For a complete bibliography see [30,31,32]. G-networks have been shown to be a diverse application tool to analyse and optimise the effects of dynamic load balancing in large scale networks [33] as well as in Gene Regulatory Networks [34,35].…”

Section: Related Workmentioning

confidence: 99%

A Random Access G-Network: Stability, Stable Throughput, and Queueing Analysis

Dimitriou¹,

Παππάς²

2019

Prob. Eng. Inf. Sci.

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The effect of signals on stability, throughput region, and delay in a two-user slotted ALOHA based random-access system with collisions is considered. This work gives rise to the development of random access G-networks, which can model virus attacks or other malfunctions and introduce load balancing in highly interacting networks. The users are equipped with infinite capacity buffers accepting external bursty arrivals. We consider both negative and triggering signals. Negative signals delete a packet from a user queue, while triggering signals cause the instantaneous transfer of packets among user queues. We obtain the exact stability region, and show that the stable throughput region is a subset of it. Moreover, we perform a compact mathematical analysis to obtain exact expressions for the queueing delay by solving a Riemann boundary value problem. A computationally efficient way to obtain explicit bounds for the queueing delay is also presented. The theoretical findings are numerically evaluated and insights regarding the system performance are derived.

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G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

Cited by 11 publications

References 182 publications

G-Networks with Adders

G-Networks with Adders

Optimal Energy Distribution With Energy Packet Networks

A Random Access G-Network: Stability, Stable Throughput, and Queueing Analysis

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