P ERFORMANCE E VALUATION : P ARADIGMS , T ECHNIQUES AND T OOLS

APPENDIX A

P ERFORMANCE E ^VALUATION : P ARADIGMS , T ECHNIQUES AND T OOLS

A . 1 . I N T R O D U C T I O N

Performance models of computer and communication systems have been studied for many years with a view to assisting optimization and guiding the design of new generations. The purpose of this appendix is to present briefly the modeling paradigms and the techniques used for performance evaluations, as well as some existing tools.

A . 2 . M O D E L I N G P A R A D I G M S

The central issue in any modeling study is the selection of appropriate abstraction mechanisms for representing the system under study. In the area of communication network modeling, three broad classes of modeling approaches can be identified [52].

The first class of network models are graph-based models such as finite state

machines and Petri nets. These modeling formalisms are typically used for

network protocol specification and verification.

The second class of network models are procedural in nature. In this case, protocols are specified as a collection of interacting procedures written in a programming-like language. This modeling approach is based on the fact that network protocols are, by design, a clear way in which algorithms can be expressed. This class of models has been used extensively in protocol specification and verification.

The final class of network models are the so-called "job shop" models, which include the analytic and extended queueing network models. The queueing network is perhaps the most natural and straightforward model of a computer network.

A . 2 . 1 . Q u e u e i n g N e t w o r k s

Queueing network models have been used extensively as a modeling paradigm for deriving analytical as well as simulation based performance measures [105].

Standard queueing networks are systems which consist of a set of servers (active resources) to serve arriving customers. If there are more arrivals in a time interval than the amount of customers which can be served in that interval, a queue will arise. The server selects one or more customer from the queue according to a certain selection strategy and serves them. Customers either can enter the system at a source (in open systems) or they have been in the system since an initial point of time (in closed systems). A stochastic routing description for each customer in the system determines which sequence of servers is passed with which branching probabilities. The extended version of the queueing modeling paradigm (EQN) includes modeling elements such as traffic sources, shared resources and rules governing the flow of jobs [106]. The EQN is useful for capturing and simulating the asynchronous and queueing aspects of networks.

A . 2 . 2 . P e t r i N e t

A Petri net [107, 108, 109] is a graphic model of a system in which actions are

represented by transitions, drawn as bars or boxes, whereas situations are

represented by the existence of tokens, drawn as block dots, inside places, drawn

as circles. Places and transitions may be connected by arcs, thus forming a

bipartite graph, and establishing input and output relations between places and

transitions. The dynamic behavior of a Petri net is governed by the firing rule

according to which transitions are enabled when all their input places contain an appropriate number of tokens. Enable transitions can fire. The transition firing removes token from the input places and deposits new tokens in the output places. There are several variants of Petri Nets, offering extensions to the basic model, such as : i) timed Petri Net (introducing abstract time in the network), and ii) colored Petri Net (introducing types of places).

Petri net models are excellent for describing and analyzing protocols, they are not well suited for modeling and simulating the flow of packets and messages in large networks. Furthermore, Petri Nets can very quickly lead to unmanageable graphs even for reasonably small systems. This problem has led to the extensions, exemplified above.

A . 3 . P E R F O R M A N C E E V A L U A T I O N T E C H N I Q U E S

This section deals with the performance evaluation techniques used in computer communication networks. Basically, there are three categories of these techniques: measurements on real systems, analytical techniques, and simulation.

A . 3 . 1 . M e a s u r e m e n t s o n R e a l S ys t e m s

Measurements provide the most direct means of network performance evaluation. Their main objective is to gather statistics about various events, interpret them in terms of the network performance, and tune the network parameters to achieve the most optimal performance possible. The main requirements associated with this technique are: i) the network must exist or at least a prototype, ii) the experimentation have to be feasible, namely it is possible to suspend the operation of an ongoing network in order to perform the experiments. In order to obtain meaningful results, enough statistics have to be gathered, during a few days or may be several weeks.

A . 3 . 2 . A n a l yt i c a l M e t h o d s

Analytic models are quick, economical, and easy to work with. They require a

high degree of abstraction, considerable effort and skill may be required on the

part of the network modeler to develop a performance model which accurately

reflects the system under study. A tractable analytic model often restricts the

range of system characteristics that can be explicitly considered in a performance model. As a result, a modeler runs the risk of distorting a model to make it amenable to analytic solution and may end up with a correct solution, but to the wrong problem. Nonetheless, analytic techniques can be effective when carefully applied in practice.

A . 3 . 3 . S i m u l a t i o n

Simulation is a powerful tool for evaluating performance of any type of network.

Many times, simulation is the only feasible method to analyze networks where measurements are impracticable and analytical techniques are not tractable. With a simulation techniques, a network can be modeled to any arbitrary level of detail. Since the system may be modeled to any degree of detail, less abstraction is required and the process of model formulation is a more straightforward task.

The solution of a simulation model is generally time consuming.

A . 4 . T O O L S

In the literature, several tools are described. Hereafter, a brief description of BONeS

[10], OPNET

[8], NESSY

[11, 110], TOPNET

[9], and NetMod

[12].

A . 4 . 1 . O P N E T

OPNET is a simulation tool for analyzing communication networks. The model description is done hierarchically. The user of OPNET, graphically specifies the topology of his network which consists of nodes and links. Each node includes processors, queues, and traffic generators. The user also has to describe the data flow between components in a node. Finally, the behavior of each process is

1 Block Oriented Network Simulator

2 Optimized Network Engineering Tool

3 NEtwork Simulation SYstem

4 Tool for the Object oriented, Petri network based Network Evaluation and Test

5 Network Modeling

described using state diagrams. In general, attributes of various building blocks are specified using pop-up menus. When the edition of the model is terminated, OPNET generates a simulation program written in C. OPNET seems to be a powerful tool, and the choice of UNIX/C guarantees its portability although C is not meant for modeling and simulation.

A . 4 . 2 . B O N e S

Concerning BONeS, it is a simulation system for studying communication network models. The modeling principle of this product is the hierarchical decomposition; the network topology is described using blocks or sub-model. A graphical editor is provided for the user to construct his model. The blocks are written in the C language at the lowest level of abstraction and are stored in a library. When the model is edited, it is compiled and a C program is produced for realizing a discrete event simulation. For adding a new block inside the library, the user constructs the new block from other blocks or can write C code. The main drawback of BONeS is the usage of C as a language of simulation.

A . 4 . 3 . N E S S Y

NESSY provides an object oriented methodology in order to represent network components. The assembly of these components is then used for building a network model. NESSY relies on a formal model to describe the components.

They are described with a dedicated language derived from ESTELLE [13], called LASSY (Language for the Simulation System). The final simulation model is made by composition of instances of various object classes. The usage of NESSY is two-phased: the modeling phase where models called atomic models are designed and written, and the building phase where simulation model is constructed and resolved. NESSY is implemented in a UNIX environment, its user’s interface is based upon the X-Window system, and its simulation kernel was developed with the Modula-2 language.

A . 4 . 4 . T O P N E T

TOPNET combines the visual approach with the object-oriented methodology

and with PROT network, a class of Petri net. It provides graphic facilities based

on the X Window System. It uses Ada for the internal programs of the simulation engine, as well as for the description of the procedural aspects of the model.

The main components of TOPNET are : i) Simulation library : it contains models of network components, called blocks, ii) Block editor : it allows to define and modify individual blocks, iii) Network editor : it is a graphic editor for the composition of the topology and architecture of the network to be simulated, iv) Simulator : it supports the execution and the animation of the model constructed with the network and block editors, and v) Translator : It is a software module that builds the simulation program starting from the description of the communication system given by the network editor and from a general purpose software module called the simulation kernel.

A . 4 . 5 . N e t M o d

NetMod uses simple analytical models to provide the designers of large interconnected LANs with an in-depth analysis of the potential performance of these systems. NetMod models popular LAN technologies, such as token ring and Ethernet and some of their variations, as well as special network components such as routers, bridges, and gateways. NetMod can analyze an existing or proposed network in terms of its basic performance characteristics, e.g., component utilization, throughput, and packet delay times. Finally, it provides a graphic interface that corresponds to the world view of the network designer, with icons that represent rings, buses, routers, workstations, etc.

NetMod is implemented on a Macintosh to make use of the existing HyperCard user’s interface environment, together with the Excel macro facility for computation.