More specifically, Pillowslip [8] in a comprehensive survey of problems and solutions suited for steady-state simulation mentioned the relevance of simulation technique for modeling telecommunication networks. In this paper we survey existing network simulators highlighting their strengths and weaknesses. We classify and compare popular simulators based on type and deployment mode along with network impairments and protocol supported. The simulation methodologies, evaluation techniques and credibility of simulation studies are discussed.

Telecommunication network researchers and developers can SE the results of this study in selecting the most appropriate simulator. The rest of the paper is organized as follows. Section II surveys popular network simulators highlighting their strengths and weaknesses. In Section Ill, we describe simulation methodologies and techniques including credibility of simulation studies. Section IV provides recommendations for best practice in network simulation, and a brief conclusion in Section V concludes the paper.

Abstract-?simulation methodology has become popular among computer and telecommunication network researchers and developers worldwide. This popularity is due to the availability of various sophisticated and powerful simulation packages, and also because of the flexibility in model construction and validation offered by simulation. For selecting an appropriate network simulator for a simulation task, it is important to have good knowledge of the simulation tools available, along with their strengths and weaknesses.

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It is also important to ensure that the results generated by the simulators are valid and credible. The objective of this paper is to survey, classify, and compare telecommunication network simulators to aid researchers in electing the most appropriate simulation tool. We compare the network simulators based on type, deployment mode, network impairments and protocol supported. We discuss simulator evaluation methodologies and techniques, and provide guidelines for best practice in network simulation.

Index Terms-? Network simulator, simulation methodology, parallel simulation. L. INTRODUCTION Network simulation methodology is often used to verify analytical models, generalize the measurement results, evaluate the performance of new protocols that are being developed, as well as to compare the existing protocols. However, there may be a potential problem when using simulation in testing protocols because the results generated by a simulator may not be necessarily accurate or representative.

To overcome this problem, it is important for network researchers and developers to use a credible simulation tool which is easy to use; more flexible in model development, modification and validation; and incorporates appropriate analysis of simulation output data, pseudo-random number generators, and statistical accuracy of the simulation results. To select a credible simulator for a simulation task, it is also important to have good knowledge f the available simulation tools, along with their relative strengths and weaknesses. These aspects of credible simulation studies are recommended by leading simulation researchers [1-3].

The use of discrete event simulation packages as an aid to modeling and performance evaluation of computer and II. A SURVEY OF EXISTING NETWORK SIMULATORS While various simulators exist for building a variety of network models, we compare 10 popular network simulators highlighting their strengths and weaknesses. These simulators were selected based on their popularity, published results, and interesting characteristics and features. A. Commercial network simulator I) OPPONENT: Optimized Network Engineering Tool (OPPONENT) is a discrete event, object-oriented, general purpose network simulator.

It provides a comprehensive development environment for the specification, simulation and performance analysis of computer and data communication networks. OPPONENT is a commercial network simulation package which is available for supporting both the teaching and research in educational institutions under the OPPONENT university academic program [9]. OPPONENT has several modules and tools, including 10 OPPONENT modeled, planner, model library, and analysis tools [10]. It is widely used in the network industries for performance modeling and evaluation of local and wide-area networks.

The main strengths of OPPONENT include a comprehensive model library, modular model development, high level of modeling detail, user-friendly GU’, and customizable presentation of simulation results. However, OPPONENT is a very expensive package (license maintenance fees are also high), and its parameter categorization is not very transparent. It) Equaled Developer: Equaled Developer (aqualung’) is a distributed and parallel network simulator that can be used for modeling and simulation of large networks tit heavy traffic [11].

The Equaled consists of Equaled scenario designer, Equaled animator (visualization and analysis tool), Equaled protocol designer (protocol skeleton tool), Equaled analyzer (real time statistical tool), and Equaled packet tracer (visualization and debugging tool). Equaled is a commercial version of the open source simulator called Glooms. The main strength of Equaled is that it supports thousands of nodes and run on a variety of machines and operating systems. It has a comprehensive network relevant parameter sets and allows verification of results through by inspection of code and configuration files.

However, Equaled does not have any predefined model constructs. Iii) Mentis: Mentis is available both commercial and academic versions, and can be used for modeling and simulation of various network protocols, including Walls, Ethernet, TCP/IP, and asynchronous transfer mode (ATM) switches [12]. Mentis allows a detailed performance study of Ethernet networks, including wireless Ethernet. The effect of relative positioning of stations on network performance, a realistic signal propagation modeling, the transmission of deferral mechanisms, and the collision handling and detection processes can also be investigated [10].

The main strength of Mentis is that the package can be run on a variety of operating systems. However, the use of Mentis is limited to academic environments only. Iv) Shunts Virtual Enterprise (Shunts EVE) 5. 0: Shunts EVE is a hardware-based simulation environment having an advantage of high speed than the software-based simulation [13]. The network impairments supported are the latency, bandwidth, Jitter, packet loss, bandwidth congestion and utilization [14]. Stomacher enables the replay and capture of network activities. Storminess used as the interface to

Streamlining, creates the network model [13]. The main strength of Shunts EVE include hardware-based system, good support, empirical model and uses real-life appliances. However, it is a very expensive package and requires a good network infrastructure for up and running. The Virtual Intervention Tested (VENT) project [15]. It was primarily designed for network research community for simulating routing algorithms, multicast, and TCP/IP protocols. The Monarch project at Carnegie Mellon University has extended the NSA-2 with support for node mobility [16].

NSA-2 is written in C++ and sees TCL as a command and configuration interface. The main strength of NSA-2 is its availability for download on a variety of operating systems at no costs. Authors of research papers often publish NSA-2 code that they used, allowing other researchers to build upon their work using the original code. This is particularly useful to academia, specifically Master’s and Doctoral students who are looking for a tool for network modeling and performance evaluation. The main weakness of NSA-2 is the lack of graphical presentations of simulation output data.

The raw data must be processed using scripting languages such as ‘gawk or ‘Perl’ to produce data in a suitable format for tools like Graph or Nonplus [15]. Another disadvantage of NSA-2 is that it is not a user-friendly package because of its text-based interface, and many student researchers point out that NSA-2 has a steep learning curve. A tutorial contributed by Marc Geris [17] and the continuing evolution of NSA documentation have improved the situation, but NSA-g’s split-programming model remains a barrier to many developers. I) Glooms: It is a library-based parallel simulator, developed at the University of California, Los Angels, for bile wireless networks [18]. It is written in PARSEC (Parallel Simulation Environment for Complex System), which is an extension of C for parallel programming. Glooms is a scalable simulator that can be used to support research involving simulation and modeling of large-scale networks with thousands of nodes. The main strength of Glooms is its scalability to support thousands of nodes and executing simulation on multiple machines.

Although Glooms was designed for both wired and wireless networks, currently it supports wireless networks only. Iii) Moment++: It is a modular component-based discrete event simulator [19]. It uses building blocks called modules in the simulator. There are two types of modules used in Moment++, namely, simple and compound. Simple modules are used to define algorithms and are active components of Moment++ in which events occur and the behavior of the model is defined (generation of events, reaction on events).

Compound modules are a collection of simple modules interacting with one another. The main strengths of Moment++ include GU’, object inspectors for zooming into component level and to display the state of each component during simulation, modular architecture and abstraction, configurable, and detailed implementation of modules and protocols. However, Moment++ is a bit slow due to its long simulation run and high memory consumption. Moment++ is also a bit difficult to use. B. Open source network simulator I) NSA-2: NSA-2 is one of the most widely used network simulators in use today.

It is an object-oriented discrete-event network simulator originally developed at Lawrence Berkeley Laboratory at the University of California, Berkeley, as part of 11 C. Comparison Table I compares 10 popular network simulators based on selected criteria such as simulator type (I. E. Commercial or open source), deployment ode (enterprise, small and large scale), network impairments and protocol supported. The simulator and the corresponding type are listed in column 1 and 2, respectively.

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