ASMTA and EPEW are flagship conferences for academic and industrial researchers from diverse fields with common interest on the performance modelling, analysis and design of stochastic systems. This year, the two conference join again (and also join, again for ASMTA, with the International ECMS conference on Modelling and Simulation, ECMS 2023), and solicit papers on the development of broadly applicable analytic, simulation and measurement-based methods on all aspects of performance engineering.

ASMTA 2023 aims to bring together researchers interested in understanding and improving the performance of systems where the flow of information is random by means of proper modeling and solution methods. It seeks papers to promote research results in the development and analysis of stochastic models arising among others in communication systems, manufacturing, production, service operations, supply chain/inventory management, and biological systems.

Its scope includes both methodological and computational advances. In particular, it aims to bring together researchers working on the theoretical, algorithmic and computational aspects of the methods applied in stochastic models, and on the applications of such methods across a broad spectrum of research fields. The conference is particularly interested in contributions that present new performance evaluation methods/algorithms, or that creatively apply existing methods to predict and efficiently design stochastic systems.

Topics of interest

The topics include but are not limited to the following:

Performance-Oriented Methodologies:

    Markov Processes
    Queueing Systems and Networks
    Capacity Planning, Resource Allocation, Routing, Scheduling and Quality of Service
    Branching Processes and Epidemic Models
    Game Theory and Stochastic Games
    Reliability Theory
    (Computationally Intensive) Statistical Methods
    Stochastic Control, Decision Processes and Optimization
    Stochastic Process Algebras and Petri Nets
    Rare Events and Stochastic Simulation
    Approximations, Bounds and Limits of Stochastic Models
    Reinforcement Learning Techniques for Dynamic Resource Allocation
    Fluid and Diffusion Limits
    Risk Processes
    Random Graphs

Envisaged Application Areas:

    Complex Systems
    Computer and Information Systems
    Workflow Management Systems
    Communication Systems and Networks
    Wireless and Mobile Systems and Networks
    Peer-to-Peer Applications and Services
    Embedded Systems and Sensor Networks
    Storage Systems, Data Centers, Content Delivery, Cloud/Fog/Edge/. . . Computing and Virtualisation
    Workload Modelling and Characterization
    Road Traffic and Transportation
    Social Networks
    Modelling of Virtualisation
    Energy-Aware Optimisation
    Stochastic Modelling for Systems Biology
    Biologically Inspired Network Design
    Artificial Intelligence and Machine Learning Platforms
    Blockchains and Crypto-Currency
    Network Economics and Platform Designs
    Matching Systems and Sharing Economy