The quality and representativeness of simulation results heavily depend on the sequence of numbers used in the experiments. When dealing with simulations of critical systems, it might be necessary to increase the representativeness of such results by running experiments with different sequences and to investigate the system behaviour in specific situations. jRand increases the flexibility in the configuration of the input sequences and that raises the simulation system developers from their details while designing and coding simulation software. Using jRand, simulation system developers are raised from the concerns of the choice of sequences, which can in turn be designed and applied with less effort for the specific scenarios.


jRand is a Java component-based framework for sequences of objects, which particularly addresses numbers, that allows the implementation of sources of number with generic properties (e.g. deterministic, stochastic with random or pseudo-random predictability).
jRand structure bases on the concept of object producer and object consumers, which decouples the components providing objects from the ones using them. Object producers are identified by the base interface ObjectStream. They can be of various types, such as hand-typed numbers from file, hard-coded in the software, or internally generated by functions. The object consumers, which use the interface ObjectStream, are not concerned with the details of the sequence generation, and therefore sequences can be transparently interchanged without modifying their code.

Structure of the framework

The structure of the framework is illustrated in Figure 1 with the use of UML class diagram.

Figure 1: Class diagram of jRand

Its definition bases on the interface ObjectStream. The interface defines service specifications for object producers, which have to provide two services: getNextObject() and goToObject(n). Where the former returns the next object in the stream and the latter shifts the stream of n positions.

On top of the general object producer interface, jRand defines several groups of sequences according to the properties they can have. Currently, jRand categorizes the sequences according to three criterions, which might overlap though. The first one (a) is related to the dependences between values in a sequence; the second one (b) concerns the type of values; and the third (c) considers the structural details of the objects producers, and therefore a selected object stream can be identified as holding one or more of the above properties.

According to the property (a), sequences of objects can be classified in three types

  1. Random
  2. Pseudo-Random
  3. Deterministic

Each of which classifies the sequences according to the statistical relationship between adjacent objects in the sequence in terms of bit-wise comparison.

Random tags all those streams that present a purely random sequence, which is thus not reproducible. Typically, streams of this group rely on the number of nanoseconds measured by the system clock or the unpredictable values contained in sets of address in the local physical memory. For their properties, such type of stream is to be avoided in systematic simulation experiments since the reproducibility of the runs is not guaranteed.

Pseudo-Random stands for the stream for which it is expected that the objects present statistical properties such as uniformity and serial correlation, even though they are deterministically generated. This type of sequence might have input parameters that can vary the sequence and can be statistically tested to assess their goodness in terms of the above statistical properties.

Deterministic represents the streams for which the sequence is known completely and explicitly since of the producer definition. By definition, a deterministic stream presents always the same sequence of objects and has no input parameters. 

Although the above classification might lead to an ambiguous categorization of object streams, such uncertainty can be easily resolved by considering the expected use of the sequence. For example if we consider a hand-typed object stream residing on a file, it might belong to either the deterministic or the pseudo-random group. The stream creator will determine which the scope of the stream is, that could be deterministic for highly-correlated objects in the stream or could be pseudo-random if it statically satisfies some statistical properties. Certainly, it cannot be classified as Random since the object order is known.

The second property (b) characterizing a stream of objects is the type. Object is an abstract type, which can be subjected to general properties and general transformation. Grouping the streams of objects according to their type contributes to the definition of specific properties and transformations. For example, considering Numbers as a special type of Objects, several properties can be more formally quantified through statistical tests and more transformations can be defined, such as algebraic transformations. We will discuss the Numbers in the following section.

The structural properties (c) of the sequence of objects are captured by the classification according to the transformation.  Transformations group object streams that are obtained through composition, interleaving or functional transformation of one or more object streams by acting as decoration or composition of existing object stream. For the abstract type Object, this category defines a base interface, which plays a marker role, and a set of classes. Possible simple stream transformation classes are (Figure 1)
  • Round Robin, which takes n object streams and uses the next object for each stream until all the stream are used, and then starts over
  • Repeated, which repeats the elements of a specified stream

Other transformations might consider the use of stream of type Number to perform more complex operations on a set of streams. For example:

  • Shuffled Stream, which takes one object stream o and a numeric stream n. The shuffle maintains an array of a certain number of objects generated by o and uses n to select one of the objects. When returned, each object is replaced with a new one from the stream o
  • Stream Splitter, which takes an object stream o and a numeric stream n, and uses n to shift the object stream between each request of a new object.

Sequence of Numbers: Numeric Stream

Numeric Streams represent the streams of Numbers. They conform to the ObjectStream interface, which is then specialized to NumericStream and redefined in the return type of the method getNextNumber(). NumericStream can be classified according to the same properties of the general object stream. In addition, however, they present the inherent properties of numbers. Such properties enrich the semantic of the objects and widen the set of possible transformations.

Concerning property (a), Numeric Streams can be classified in random, pseudo-random and deterministic. Differently from the general Object stream, however, NumericStream can be accurately assessed in their random and pseudo-random properties with statistical tests, such as chi-square or serial test.

In jRand, pseudo-random numeric streams are further divided into core generators and distribution generators. The former group consists of uniform generators over the available set of integers. In such group, all the linear-congruent and multiplicative congruent generators are defined. The latter group, differently, includes the generators that produce real numbers with specified statistical properties, such as Exponential, Gaussian, K-Erlang, Coxian, Poisson, Truncated Pareto, Weibull, Geometric or LogNormal.
distinguish pseudo-random stream in two groups because of the component-based design of jRand. In fact, the functional transformations from which the exponential, Gaussian, etc. generators are derived is indeed independent from how the original integer sequences are produced. They rely on the hypothesis that the original sequence is uniformly distributed, property which is in turn to be assessed with statistical tests.

In the current release, jRand defines a core generator that has already been designed and developed in The JavaSim’s  User Guide, University of New Castel, http:// javasim.ncl.ac.uk. Nevertheless, such contribution is complementary to jRand, and therefore a combination of both approaches brings the advantages of such generator with the jRand flexible structure. As suggested in "D. Knuth, The Art of Computer Programming Vol. 2 - Seminumerical Algorithms, Addison-Wesley, US, 1969", the generator consists of a shuffled combination of linear and multiplicative generators. The multiplicative generator is defined through the following expression:

Y[i+1] = Y[i] * (55 mod 226);

which presents a period of 224 when the initial seed is odd. The linear congruent generator, which is based on the algorithm in "R. Sedgewick, Algorithms, Addison-Wesley, 1983", produces numbers that are eventually shuffled by the above multiplicative generator. In the specific context, the initial seed is set at 772531 to meet the conditions that guarantee good statistical uniformity and longest period.
The jRand implementation of this generator is done through the object transformations. Specifically, the shuffled stream transformation is used with type Number and the two numeric streams of the two generators, the multiplicative and the linear congruent, which are respectively the objects shuffler and the object stream to shuffle. Concerning the transformation (c), Numbers can be subjected to a wider set of possible types. Besides shuffling or interleaving transformation, numeric stream can also be manipulated with standard operations on numbers. For example, a single numeric stream can be shifted, can be multiplied, can be truncated or bounded to a range. Other operations that combine multiple numeric streams are also possible, such as sum or subtraction of streams. In its current version jRand provides for this group of NumericStream, the following classes: Integerizer, LinearTransformation, and BoundedStream, nevertheless other transformation can be immediately coded in the framework. Integerizer converts a general NumericStream in an integer stream; LinearTransformation multiplies for and adds constant numbers to the given NumericStream; and finally the number BoundedStream limits the range of numbers of a specified NumericStream.

jRand example of use

The instantiation of a jRand sequence inside a simulation system requires first that a NumericStream interface be defined. This can be obtained with the following statement:

                                                #1 NumericStream numericStream;

Then, the proper sequence has to be instantiated. For example, in the case of a pseudo-random exponential generator, this can be done with the following statements:

#2 CoreGenerator cg = new JavasimGen ();

#3 numericStream = new Exponential(cg,lambda);

Where statement #2 allocates a core generator that produces a uniform sequence of numbers in the range [-MAX_LONG - 1; MAX_LONG], such as the JavasimGenerator. Finally, statement #3 allocates an exponential transformation of such stream, with parameter lambda to be specified.