The Discrete Event Simulation Framework DESMO-J: Review, Comparison To Other Frameworks And Latest Development

Göbel, Johannes; Joschko, Philip; Koors, Arne; Page, Bernd

doi:10.7148/2013-0100

Cited by 13 publications

(7 citation statements)

References 2 publications

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“…DESMO-J (Göbel et al, 2013) is an OS tool that supports both the process-oriented and the event-oriented modelling styles. It is being developed by the department of computer science of the University of Hamburg.…”

Section: Second Phase: Analysis Of Most Competent Os Des Toolsmentioning

confidence: 99%

A review of open source discrete event simulation software for operations research

Dagkakis

Heavey

2016

Journal of Simulation

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This article reviews the state of the art in Open Source (OS) Discrete Event Simulation (DES) software used for decision support in Operations Research (OR) focusing on the modelling of manufacturing, services, supply chain management and logistics. DES is arguably one of the most popular OR techniques. Commercial-Off-The-Shelf (COTS) DES software has been around for decades and it has considerably helped in the wider adoption and use of DES, providing the user with tools for modelling, debugging and experimentation. However, such tools often have limitations regarding their aspects such as cost, flexibility and reusability and OS DES seems to be a strong alternative in order to fill those gaps. The scope of our review is to present the state of the art in OS DES and identify future areas of research. The paper concludes with the desired features that an OS DES project should have in order to become a notable competitor against COTS equivalents in the OR domain.

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Section: Second Phase: Analysis Of Most Competent Os Des Toolsmentioning

confidence: 99%

A review of open source discrete event simulation software for operations research

Dagkakis

Heavey

2016

Journal of Simulation

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“…Finally, if the ANN is an MDN then the mixture components referenced in the data structure will be types of parametric distribution (e.g., Weibull) but the parameter values will not be set since these are predicted by the MDN rather than fixed. The authors recommend that the DES model is constructed as a software code to ease this integration step and suggest the use of open-source DES modelling libraries, such as DESMO-J (Göbel et al, 2013).…”

Section: Integration Of Anns Within a Des To Predict Random Variable Distributionsmentioning

confidence: 99%

Modelling stochastic behaviour in simulation digital twins through neural nets

Reed

Löfstrand

Andrews

2021

Journal of Simulation

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In discrete event simulation (DES) models, stochastic behaviour is modelled by sampling random variates from probability distributions to determine event outcomes. However, the distribution of outcomes for an event from a real system is often dynamic and dependent on the current system state. This paper proposes the use of artificial neural networks (ANN) in DES models to determine the current distribution of each event outcome, conditional on the current model state or input data, from which random variates can then be sampled. This enables more realistic and accurate modelling of stochastic behaviour. An application is in digital twin models that aim to closely mimic a real system by learning from its past behaviour and utilising current data to predict its future. The benefits of the approach introduced in this paper are demonstrated through a realistic DES model of load-haul-dump vehicle operations in a production area of a sublevel caving mine.

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“…More details about the various independent components 2 and their function are provided in Sections 5.3, 5.4 and 5.5. In order to model computer systems we have used a discrete-event simulation approach using the Java library DESMO-j (Page, Kreutzer, and Gehlsen 2005;Göbel et al 2013; Tim Lechler 2014) 3 . The library has been extended with a framework wrapping the APIs, and implementing specific features of such computing systems, replicating queuing models for server, process, disk controller, action requests, and action types.…”

Section: Testbed Designmentioning

confidence: 99%

Neural adaptive admission control framework: SLA-driven action termination for real-time application service management

Sikora

Magoulas

2019

Enterprise Information Systems

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Although most modern cloud-based enterprise systems, or operating systems, do not commonly allow configurable/automatic termination of processes, tasks or actions, it is common practice for systems administrators to manually terminate, or stop, tasks or actions at any level of the system. The paper investigates the potential of automatic adaptive control with action termination as a method for adapting the system to more appropriate conditions in environments with established goals for both system's performance and economics. A machine-learning driven control mechanism, employing neural networks, is derived and applied within data-intensive systems. Control policies that have been designed following this approach are evaluated under different load patterns and service level requirements. The experimental results demonstrate performance characteristics and benefits as well as implications of termination control when applied to different action types with distinct run-time characteristics. An automatic termination approach may be eminently suitable for systems with harsh execution time Service Level Agreements, or systems running under conditions of hard pressure on power supply or other constraints. The proposed control mechanisms can be combined with other available toolkits to support deployment of autonomous controllers in high-dimensional enterprise information systems.

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The Discrete Event Simulation Framework DESMO-J: Review, Comparison To Other Frameworks And Latest Development

Cited by 13 publications

References 2 publications

A review of open source discrete event simulation software for operations research

A review of open source discrete event simulation software for operations research

Modelling stochastic behaviour in simulation digital twins through neural nets

Neural adaptive admission control framework: SLA-driven action termination for real-time application service management

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