网络模拟器NS2简介(2nd,2012版)

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"Introduction to Network Simulator NS2 (2nd,2012) by Teerawat Issariyakul and Ekram Hossain" 《网络模拟器NS2入门（第2版，2012年）》是Teerawat Issariyakul和Ekram Hossain合著的一本专业书籍，主要介绍了网络模拟器NS2的基础知识和应用。这本书由Springer Science+Business Media出版，具有国际标准书号（ISBN）和数字对象标识符（DOI），并且受到版权保护。 NS2（网络模拟器2）是一款广泛使用的开源网络模拟工具，尤其在学术界和研究领域被广泛应用。它允许用户模拟和分析各种网络协议、拓扑结构和通信环境。本书的第二版针对初学者，旨在提供NS2的全面介绍，帮助读者理解和掌握网络模拟的基本概念和实践技巧。书中可能涵盖了以下关键知识点： 1. **NS2简介**：解释NS2的历史、特性、适用范围以及它在计算机网络研究中的重要性。 2. **安装与配置**：指导读者如何在不同的操作系统上安装和配置NS2环境，包括必要的依赖库和工具。 3. **基本网络概念**：阐述网络的基本原理，如TCP/IP协议栈、数据包传输、路由机制等。 4. **NS2工作原理**：深入解析NS2的内部工作机制，包括事件驱动模拟、对象模型和脚本语言（OTcl/Tcl）的使用。 5. **模拟脚本编写**：教授如何使用OTcl编写NS2模拟实验，包括创建网络拓扑、定义协议行为、设置仿真参数和收集统计信息。 6. **案例研究**：通过实例展示NS2在模拟各种网络场景（如无线网络、传感器网络、Ad Hoc网络等）中的应用。 7. **结果分析**：讲解如何解读和分析NS2模拟实验的结果，以及如何利用这些结果进行网络性能评估。 8. **高级主题**：可能涵盖更复杂的NS2功能，如增强的模拟模块、与其他工具（如NAM可视化工具）的集成，以及如何扩展NS2以支持新的网络协议。 9. **故障排查**：给出在使用NS2过程中可能遇到的问题及其解决方案，帮助读者解决实际操作中的困难。 10. **进一步学习资源**：推荐其他相关的文献和在线资源，以便读者进一步提升NS2的使用技能和研究能力。通过阅读本书，读者不仅可以了解NS2的基本操作，还能深入理解网络模拟的原理，并具备使用NS2进行网络研究的能力。对于计算机科学和电气工程专业的学生，以及从事网络研究和开发的专业人士来说，这是一本不可多得的参考资料。

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4 1 Simulation of Computer Networks

1.1.2 OSI and TCP/IP Reference Models

The Open Systems Interconnection (OSI) model was the ﬁrst reference model

developed by International Standards Organization (ISO) to provide a standard

framework to describe the protocol stacks in a computer network. Its consists of

seven layers, where each layer is intended to perform a well-deﬁned function [1].

These are physical layer, data link layer, network layer, transport layer, session layer,

presentation layer, and application layer. The OSI model only speciﬁes what each

layer should do; it does not specify the exact services and protocols to be used in

each layer.

The Transmission Control Protocol (TCP)/Internet Protocol (IP) reference model

[1], which is based on the two primary protocols, namely, TCP and IP, is used in

the current Internet. These protocols have proven very powerful, and as a result

have experienced widespread use and implementation in the existing computer

networks. It was developed for ARPANET, a research network sponsored by the

US Department of Defense, which is considered as the grandparent of all computer

networks. In the TCP/IP model, the protocol stack consists of ﬁve layers – physical,

data link, network, transport, and application – each of which is responsible for

certain services as will be discussed shortly. Note that the application layer in the

TCP/IP model can be considered as the combination of session, presentation, and

application layers of the OSI model.

1.1.2.1 Application Layer

The application layer sits on top of the stack and uses services from the transport

layer (discussed below). This layer supports several higher-level protocols such as

Hypertext Transfer Protocol (HTTP) for World Wide Web applications, Simple Mail

Transfer Protocol (SMTP) for electronic mail, TELNET for remote virtual terminal,

Domain Name Service (DNS) for mapping comprehensible host names to their

network addresses, and File Transfer Protocol (FTP) for ﬁle transfer.

1.1.2.2 Transport Layer

The objective of a transport layer is to perform ﬂow control and error control for

message transportation. Flow control ensures that the end-to-end transmission speed

is neither too fast to make the network congested nor too slow to underutilize the net-

work. Error control ensures that the packets are delivered to the destination properly.

Generally, when a transport protocol receives a message from the higher layer,

it breaks down the message into smaller pieces. Then it generates a PDU – called a

segment – by attaching necessary error and ﬂow control information, and passes the

segment to the lower layer.

Two well-known transport protocols, namely, TCP and User Datagram Protocol

(UDP), are deﬁned in this layer. While TCP is responsible for a reliable and

connection-orientedcommunication between two hosts, UDP supports an unreliable

1.2 System Modeling 5

connectionless transport. TCP is ideal for applications that prefer accuracy over

prompt delivery and the reverse is true for UDP.

1.1.2.3 Network Layer

This layer determines the route through which a packet is delivered from a source

node to a destination node. A PDU for the network layer is called a packet.

1.1.2.4 Link Layer

A link layer protocol has three main responsibilities. First, ﬂow control regulates

the transmission speed in a communication link. Second, error control ensures the

integrity of data transmission. Third, ﬂow multiplexing/demultiplexing combines

multiple data ﬂows into and extracts data ﬂows from a communication link. Choices

of link layer protocols may vary from host to host and network to network.

Examples of widely used link layer protocols/technologies include Ethernet, Point-

to-Point Protocol (PPP), IEEE 802.11 (i.e., Wi-Fi), and Asynchronous Transfer

Mode (ATM).

Link layer protocols are different from transport layer protocol as follows. The

former deals with a single communication link. On the other hand, the latter does

the same job for an end-to-end ﬂow which may traverse multiple links.

1.1.2.5 Physical Layer

The physical layer deals with the transmission of data bits across a communication

link. Its primary goal is to ensure that the transmission parameters (e.g., transmission

power) are set appropriately to achieve the required transmission performance (e.g.,

to achieve the target bit error rate performance).

Finally, we point out that the ﬁve layers discussed above are common to the

OSI layer. As has been mentioned already, the OSI model contains two other layers

sitting on top of the transport layer, namely, session and presentation layers. The

session layer simply allows users on different computers to create communication

sessions among themselves. The presentation layer basically takes care of different

data presentations existing across the network. For example, a uniﬁed network

management system gathers data with different format from different computers

and converts their format into a uniform format.

1.2 System Modeling

System modeling is an act of formulating a simple representation for an actual

system. It allows investigators to look closely into the system without having to

actually implement it. During the investigation, various parameters can be applied

6 1 Simulation of Computer Networks

to study system behavior. After the system is well understood, investigators can

decide whether the actual system should be implemented.

System modeling often requires simpliﬁcation assumptions. These assumptions

exclude irrelevant details of the actual system, hence making the model cleaner

and easier to implement. However, excessive assumptions may lead to inaccurate

representation of the system. Design engineers need to use their discretion to achieve

the best modeling trade-off.

Traditionally, there are two modeling approaches: Analytical approach and

simulation approach.

1.2.1 Analytical Approach

The general concept of analytical modeling approach is to come up with a way to

describe the system mathematically, and apply numerical methods to gain insight

from the developed mathematical model. Examples of widely used mathematical

tools are queuing and probability theories. Since analytical results derive mainly

from mathematical proofs, they are true as long as the underlying conditions hold.

If properly used, analytical modeling can be a cost-effective way to provide a

general view of the system.

1.2.2 Simulation Approach

Simulation recreates real-world scenarios using computer programs. It is used in

various applications ranging from operations research, business analysis, manufac-

turing planning, and biological experimentation, just to name a few. Compared to

analytical modeling, simulation usually requires fewer simpliﬁcation assumptions,

since almost every possible detail of system speciﬁcations can be incorporated in a

simulation model. When the system is rather large and complex, a straightforward

mathematical formulation may not be feasible. In this case, the simulation approach

is usually preferred to the analytical approach. The essence of simulation is to

perform extensive experiment and make convincing argument for generalization.

Due to the generalization, simulation results are usually considered not as strong as

the analytical results.

1.3 Basics of Computer Network Simulation

A simulation is, more or less, a combination of art and science. That is, while the

expertise in computer programming and the applied mathematics accounts for the

science part, the very skills in analysis and conceptual model formulation usually

represent the art portion. A long list of steps in executing a simulation process, as

given in [2], seems to reﬂect this popular claim.

1.3 Basics of Computer Network Simulation 7

A simulation of general computer networks consists of three main parts:

• Part 1 – Planning: This part includes deﬁning the problem, designing the

corresponding model, and devising a set of experiments for the formulated

simulation model. It is recommended that 40% of time and effort be spent on

planning.

• Part 2 – Implementing: Implementation of simulation programs consists of three

steps:

– Step 1 – Initialization: This step establishes initial conditions (e.g., resetting

simulation clocks and variables) so that the simulation always starts from a

known state.

– Step 2 – Result generation: The simulation creates and executes events, and

collects necessary data generated by the created events.

– Step 3 – Postsimulation processing: The raw data collected from simulation

are processed and translated into performance measures of interest.

It is recommended that 20% of time should be used for implementation.

• Part 3 – Testing: This part includes verifying/validating the simulation model,

experimenting on the scenarios deﬁned in Part 1 and possibly ﬁne-tuning the

experiments themselves, and analyzing the results. The remaining 40% of time

should be used in this part.

This formula is in no way a strict one. Any actual simulation may require more

or less time and effort, depending on the context of interest, and deﬁnitely on the

modeler himself/herself.

1.3.1 Simulation Components

A computer network simulation can be thought of as a ﬂow of interaction among

network entities (e.g., nodes, packets). These entities move through the system,

interact with other entities, join activities, trigger events, cause some changes to state

of the system, and terminate themselves. From time to time, they contend or wait for

some type of resources. This implies that there must be a logical execution sequence

to cause all these actions to happen in a comprehensible and manageable way.

According to Ingalls [4], the key components of a simulation include the

following:

1.3.1.1 Entities

Entities are objects that interact with one another in a simulation program. They

cause some changes to the states of the system. In the context of a computer network,

entities may include computer nodes, packets, ﬂows of packets, or nonphysical

objects such as simulation clocks.

8 1 Simulation of Computer Networks

1.3.1.2 Resources

Resources are limited virtual assets shared by entities such as bandwidth or power

budget.

1.3.1.3 Activities and Events

From time to time, entities engage in some activities. The engagement creates events

and triggers changes in the system state. Common examples of events are packet

reception and route update events.

1.3.1.4 Scheduler

A scheduler maintains a list of events and their execution time. During a simulation,

it moves along a simulation clock and executes events in the list chronologically.

1.3.1.5 State and Global Variables

State variables keep track of the system state. They can be classiﬁed as local

variables and global variables based on their scope of operation. Local variables

are valid under a limited range, while global variables are understandable globally

by all program entities.

In general, global state variables hold general information shared by several

entities such as the total number of nodes, the geographical area information, the

reference to the scheduler, and so on.

1.3.1.6 Random Number Generator

A Random Number Generator (RNG) introduces randomness in a simulation model.

It generates random numbers by sequentially picking numbers from a deterministic

sequence of pseudo-random numbers [5], yet the numbers picked from this sequence

appear to be random.

Without randomness, the results for every run would be exactly the same. To

generate a set of different results, we may initialize the RNG for different runs with

different seeds. A seed identiﬁes the ﬁrst location where the RNG starts picking

random numbers. Two simulations whose RNG picks different initial positions

would generate different simulation results.

In a computer network simulation, for example, a packet arrival process and a

service process are usually modeled as random processes. These random processes

are usually implemented with the aid of an RNG. The readers are referred to [6, 7]

for a comprehensive treatment on random process implementation.

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