Probabilistic models of computer systems

Gelenbe, Erol; Pujolle, Guy

doi:10.1145/800200.806188

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…It was also recently tested for Software Defined Networks (François and Gelenbe [34]) and for intercontinental overlay networks over the Internet (Brun,Wang,and Gelenbe [16], François and Gelenbe [33]). While traditional network research has relied on mathematical modeling and discrete event simulation (Gelenbe [42,43]), CPN is based on an autonomic or selforganized approach (Gelenbe, Seref, and Xu [136]) that suggests that decisions based on "naturally inspired heuristics" (Gelenbe [54]) can be effective even in environments where adversarial agents may operate (Gelenbe and Cao [74]).…”

Section: Autonomic Systems and Cognitive Packet Network (Cpns)mentioning

confidence: 99%

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

Caglayan¹

2017

Prob. Eng. Inf. Sci.

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This paper introduces a special issue of this journal (Probability in the Engineering and Informational Sciences) that is devoted to G(elenbe)-Networks and their Applications. The special issue is based on revised versions of some of the papers that were presented at a workshop held in early January 2017 at the Séminaire Saint-Paul in Nice (France). It includes contributions in several research directions that followed from the introduction of the G-Network in the late 1980s. The papers present original theoretical developments, as well as applications of G-Networks to Machine Learning, to the performance optimization of energy systems via the novel Energy Packet Networks formalism for systems that operate with renewable and intermittent energy sources, and to packet network routing and Cloud management over the Internet. We introduce these contributions from the perspective of an overview of recent work based on G-Networks.

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Section: Autonomic Systems and Cognitive Packet Network (Cpns)mentioning

confidence: 99%

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

Caglayan¹

2017

Prob. Eng. Inf. Sci.

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“…CPN related was a clear break with Erol's traditional research which has relied essentially on mathematical modeling and simulation [40][41][42]137,155].…”

Section: Autonomic Communications and Cpnmentioning

confidence: 99%

Erol Gelenbe: A Career in Multi-Disciplinary Probability Models

Caglayan¹

2016

Prob. Eng. Inf. Sci.

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We focus on Erol Gelenbe's scientific and technical contributions to probability models in the computer and information sciences, but limit our survey to the last fifteen years. We start with a brief overview of his work as a single author, as well as his work in collaboration with over 200 co-authors. We discuss some of his recent and innovative work regarding a new probability model that represents Intermittent Energy Sources for Computing and Communications, introducing Energy Packet Networks which are a probabilistic representation of the flow, storage and consumption of electrical energy at the microscopic level (in electronic chips), and at the macroscopic level (e.g. in buildings or data centers) and for its routing and dynamic usage by consuming units (such as computer elements, chips or machines). We next discuss his work on designing computer and communication systems that parsimoniously use energy in order to achieve a satisfactory level of quality of service (QoS). Trade-offs between system QoS and energy consumption are also considered. Then we turn to Prof. Gelenbe's pioneering work on Autonomic Communications and the design and implementation of CPN, the Cognitive Packet Network, and we also briefly review his spiking random neural network that was used in CPN. This is followed by a brief review of work that he conducted since 1999 on human evacuation from dangerous or catastrophic environments, and the design of technology driven Emergency Management Systems. His research since the late 2000s on Gene Regulatory Networks is then covered together with its application to the detecting possible disease from microarray data. Finally, we briefly discuss some novel analytical models that he developed in this period with publications appearing in journals of physics and applied mathematics.

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“…The natural choice for this approach is multi-class queueing and diffusion models [10,17] and related methods. Such models provide estimates of both averages and variances of the times that it would take to undertake signaling or call processing functions, as well as of access times to web sites and other services, in the presence of a large population of mobile users in the network, once the average service times and task sequences are known.…”

Section: Modellingmentioning

confidence: 99%

Mobile Network Anomaly Detection and Mitigation: The NEMESYS Approach

Abdelrahman

Gelenbe

Görbil

et al. 2013

Information Sciences and Systems 2013

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Mobile malware and mobile network attacks are becoming a significant threat that accompanies the increasing popularity of smart phones and tablets. Thus in this paper we present our research vision that aims to develop a network-based security solution combining analytical modelling, simulation and learning, together with billing and control-plane data, to detect anomalies and attacks, and eliminate or mitigate their effects, as part of the EU FP7 NEMESYS project. These ideas are supplemented with a careful review of the state-of-the-art regarding anomaly detection techniques that mobile network operators may use to protect their infrastructure and secure users against malware.

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Probabilistic models of computer systems

Abstract: We develop a method based on diffusion approximations in order to compute, under some general conditions, the queue length distribution for a queue in a network. Applications to computer networks and to time-sharing systems are presented.

Cited by 8 publications

References 19 publications

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

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

Erol Gelenbe: A Career in Multi-Disciplinary Probability Models

Mobile Network Anomaly Detection and Mitigation: The NEMESYS Approach

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