Analysis and Performance of Discrete Networked Dynamic Systems with Eigen-Spectrum Gap

DEVS is a popular formalism for modeling complex dynamic systems using a discrete-event abstraction. Owing to its popularity, and the simplicity of the simulation kernel, a number of tools have been constructed by academia and industry. However, each of these tools has distinct design goals and a specific programming language implementation. Consequently, each supports a specific set of formalisms, combined with a specific set of features. Performance differs significantly between different tools. We provide an overview of the current state of eight different DEVS simulation tools: ADEVS, CD++, DEVS-Suite, MS4 Me, PowerDEVS, PythonPDEVS, VLE, and X-S-Y. We compare supported formalisms, compliance, features, and performance. This paper aims to help modelers in deciding which tools to use to solve their specific problems. It further aims to help tool builders, by showing the aspects of their tools that could be extended in future tool versions.

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Uncertainty modeling and predicting the probability of stability and performance in the manufacture of dynamic systems

ISA Transactions ◽

10.1016/j.isatra.2010.02.003 ◽

2010 ◽

Vol 49 (4) ◽

pp. 528-534 ◽

Cited By ~ 7

Author(s):

Roger Fales

Keyword(s):

Dynamic Systems ◽

Uncertainty Modeling ◽

And Performance

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A Unifying Framework Design for the Management of Autonomic Network Functions

Advances in Wireless Technologies and Telecommunication - Emerging Automation Techniques for the Future Internet ◽

10.4018/978-1-5225-7146-9.ch003 ◽

2018 ◽

pp. 45-89

Author(s):

Laurent Ciavaglia ◽

Pierre Peloso

Keyword(s):

Dynamic Systems ◽

Relevant Information ◽

Full Potential ◽

Framework Design ◽

Recent Developments ◽

Cognitive Techniques ◽

Network Functions ◽

Knowledge Function ◽

And Performance ◽

Coordination Function

The increased use of software-driven and virtualization techniques enables more versatile network infrastructures. Realizing the full potential of such large and dynamic systems requires advanced automation and adaptation capabilities. In this chapter, the authors review recent development of so-called self-driving networks combining cognitive techniques and autonomic behaviors. In particular, the authors provide insights on a set of core mechanisms for the operation of self-driving networks: (1) a governance function to help operators deploy, pilot, control, and track run-time behaviors and performance of self-driving functions; (2) a coordination function to ensure stability and performance when several self-driving functions are running together; (3) a knowledge function to share relevant information to empowering their actions; and (4) common workflows, lifecycles, and APIs to enable deployment and interoperability of autonomic functions. The analysis connects with reference work in scientific literature and the most recent developments in standards (e.g., IETF/IRTF and ETSI).

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