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model and simulate discrete-event systems

simevents^® provides a discrete-event simulation engine and component library for analyzing event-driven system models and optimizing performance characteristics such as latency, throughput, and packet loss. queues, servers, switches, and other predefined blocks enable you to model routing, processing delays, and prioritization for scheduling and communication.

with simevents, you can study the effects of task timing and resource usage on the performance of distributed control systems, software and hardware architectures, and communication networks. you can also conduct operational research for decisions related to forecasting, capacity planning, and supply-chain management.

tutorials

simevents software incorporates discrete-event system modeling into the simulink^® time-based framework.
build an event-based model using the simevents block library.
explore statistics and visualize simulation results

access statistics through simevents blocks, examine, and experiment with behavior of the d/d/1 queuing example model, visualize, and animate simulations.
specify random intergeneration times for entities, manage entities with event actions in a queuing system.
this example shows how to use simulink function blocks to timestamp entities, pass entity attributes to simulink® components, and create notification events for routing.
simulate a hybrid system that has simevents and simulink parts.

about discrete-event simulation

learn how entities fit into the modeling process.
learn the basics of events and event actions in simevents.

featured examples

m/m/1 queuing system

model a single-queue single-server system with a single traffic source and an infinite storage capacity. in the notation, the m stands for markovian; m/m/1 means that the system has a poisson arrival process, an exponential service time distribution, and one server. queuing theory provides exact theoretical results for some performance measures of an m/m/1 queuing system and this model makes it easy to compare empirical results with the corresponding theoretical results.

modeling hybrid systems - tank filling

a hybrid system with both continuous time and discrete event sections. the discrete event part models tanks, represented by entities, which are being queued and need to be filled up. each tank has a "capacity" attribute. the continuous time part models the process of filling up a tank, modeled by an integrator. when a tank is filled to capacity, this event can be detected by a hit crossing block, which will generate a message corresponding to this event. the generated message will trigger the server to release the tank.

videos

learn the basics of discrete-event simulation, and explore how you can use it to build a process model in this matlab^® tech talk by will campbell.

add system simulation or environment modeling to your algorithm modeling and graphical programming using simevents.

model, analyze, and optimize various operational processes using simevents.