aivika: A multi-method simulation library
Aivika is a discrete event simulation (DES) framework with support of activity-oriented, event-oriented and process-oriented paradigms. It supports resource preemption and other improved simulation techniques. There is also a partial support of system dynamics and agent-based modelling. All the complexity is hidden under easy-to-use computations.
The represented package is a basic simulation library optimized for sequential execution. There are also packages for nested and parallel distributed simulation.
The library has the following features:
allows defining recursive stochastic differential equations of system dynamics (unordered as in maths via the recursive do-notation);
supports the event-driven paradigm of DES as a basis for implementing other paradigms;
supports extensively the process-oriented paradigm of DES with an ability to resume, suspend and cancel the discontinuous processes;
allows working with the resources based on specified queue strategies (FCFS/FIFO, LCFS/LIFO, SIRO, static priorities and so on);
allows customizing the unbounded and bounded queues based on strategies too;
supports the resource preemption;
allows defining a queue network based on streams of data (transacts);
allows using a GPSS-like DSL with help of the additional library;
allows simulating circuits with recursive links and delays;
supports the activity-oriented paradigm of DES;
supports basic constructs for the agent-based modeling such as agents, states, timeout and timer handlers;
allows creating combined discrete-continuous models as all parts of the library are well integrated and this is reflected directly in the type system;
the arrays of simulation variables are inherently supported;
supports the Monte-Carlo simulation;
the simulation model can depend on external parameters;
uses extensively signals for notification;
allows gathering statistics in time points;
hides technical details in high-level simulation computations (monads, streams and arrows).
The simulation engine itself has minimal dependencies. However, there are additional packages [1, 2] that offer the following features:
automating simulation experiments;
saving the results in CSV files;
plotting the deviation chart by rule 3-sigma, histogram, time series, XY chart;
collecting the summary of statistical data;
parallel execution of the Monte-Carlo simulation;
has an extensible architecture.
The charting package has two interchangeable back-ends [3, 4], where one of them uses Cairo and it creates small PNG images, while another creates more detailed SVG files and it can be used on Windows.
There are also additional packages that allow saving the results of simulation in SQL databases. Then the results can be accessed from other software applications. For example, it can be useful when creating flight simulators in other programming languages such as C# or Java.
Moreover, the method was generalized  and applied to nested simulation  and parallel distributed simulation .
The libraries were tested on Linux, Windows and OS X.
The PDF documentation, installation instructions and a more full information about Aivika are available on the the AivikaSoft website .
P.S. Aivika is actually a genuine female Mari name which is pronounced with stress on the last syllable.
|Versions [faq]||0.1, 0.2, 0.3, 0.4, 0.4.1, 0.4.2, 0.4.3, 0.5, 0.5.1, 0.5.4, 0.6, 0.6.1, 0.7, 1.0, 1.1, 1.2, 1.2.1, 1.3, 1.4, 2.0, 2.1, 3.0, 3.1, 4.0, 4.0.1, 4.0.3, 4.1, 4.1.1, 4.2, 4.3, 4.3.1, 4.3.2, 4.3.3, 4.3.4, 4.3.5, 4.5, 4.6, 5.0.1, 5.1, 5.2, 5.3, 5.3.1, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9|
|Dependencies||array (>=0.3.0.0), base (>=18.104.22.168 && <6), binary (>=0.6.4.0), containers (>=0.4.0.0), deepseq (>=22.214.171.124), mtl (>=2.1.1), random (>=126.96.36.199), vector (>=0.10.0.1) [details]|
|Copyright||(c) 2009-2017. David Sorokin <firstname.lastname@example.org>|
|Maintainer||David Sorokin <email@example.com>|
|Source repo||head: git clone https://github.com/dsorokin/aivika|
|Uploaded||by DavidSorokin at 2017-06-10T14:35:55Z|
|Downloads||33681 total (283 in the last 30 days)|
|Rating||2.0 (votes: 1) [estimated by Bayesian average]|
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Last success reported on 2017-06-10 [all 1 reports]
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