A FLEXIBLE MOBILE NETWORK MONITORING TOOL

Rodrigo M. S. Gonçalves, Bruno M. G. Miranda

Instituto Superior das Ciências do Trabalho e da Empresa, DCTI

Avenida das Forças Armadas, Edifício ISCTE – 1600 Lisboa

Francisco A. B. Cercas

Instituto de Telecomunicações, Av. Rovisco Pais 1, 1049 - 001 Lisboa

Keywords: Key-Performance Indicators, Quality of Service, Network Monitoring.

Abstract: This article presents a model to implement an information system to monitor mobile networks using Key

Performance Indicators (KPIs). The proposed model encloses not only the KPI calculations but the

translation, importing and presentation of data. It also addresses more advanced topics such as system

configuration, node dependencies and routing. A description of the system’s implementation is also

presented, from two perspectives: high-level components and technology-related issues. The paper finishes

with the presentation of a system application: monitoring a KPI using a data plot generated by the system.

1 INTRODUCTION

Designing an information system to monitor the

performance of a mobile network presents quite

interesting challenges. The inherently complex

network structure faces the implementer with issues

regarding data conversion, storage, processing and

presentation.

Network and node performance measurement is

crucial for any telecommunications network. It

allows network scanning and spotting of anomalous

situations that may contribute to a decrease in the

required Quality of Service (QoS) (Kreher, 2006).

With the help of an appropriate tool to diagnose

these errors, one can apply corrective measures that

eliminate or mitigate the problem’s causes and

effects.

Performance measures are usually achieved by

collecting statistical data at the nodes and then

processing it with appropriate mathematical

formulas to obtain the required performance issues.

This leads to the concept of Key Performance

Indicator (KPI). A KPI is a formula-based

computation executed over collected data. Usually,

the formulas that describe KPIs are simple algebraic

equations. The difficult issues that may involve

KPI’s calculation are related to the information

filtering and data selection that takes place

beforehand (Kreher, 2006).

In mobile networks the data is usually collected

and transmitted to a central repository where it is

stored. For example, in a typical Global System for

Mobile Communications (GSM) network this would

be the Operation and Support Subsystem (OSS).

It is common to find different formats to encode

and transmit the nodes collected data. This

discrepancy is due to the continuous network

evolution and expansion which leads to the

introduction of different technologies at the nodes.

The next step is to process the data and that is

where KPIs are evaluated. Their formulas are

applied on the raw statistical data and the results are

again stored on a different data repository close to

the network operator equipment. Therefore

operators, or subcontracted network managing

service providers, must carefully dimension its

database capacity and structure to implement a

Network Monitoring Information System (NMIS).

Finally the processed data must be presented to the

user in a convenient way.

In this paper we describe a NMIS model that was

completely developed and tested to fulfil an

operator’s needs.

Our goal is to develop a NMIS model that can be

easily customized and with great adaptability

capabilities. Our system must hide all the network

241

M. S. Gonçalves R., M. G. Miranda B. and A. B. Cercas F. (2007).

A FLEXIBLE MOBILE NETWORK MONITORING TOOL.

In Proceedings of the Second International Conference on Wireless Information Networks and Systems, pages 225-229

DOI: 10.5220/0002147102250229

 SciTePress

monitoring complexity, be robust and allow

distributed access.

The remainder of this paper is organized as

follows: section 2 briefly describes the state of the

art on these system tools, section 3 presents the

system model, as well as a description of the

model’s implementation, section 4 presents the

results, based on a network monitoring service

example and it briefly discusses the impact of this

model on the achievement of the service goal.

Finally, section 5 summarizes the model

conclusions.

2 STATE OF THE ART

Since network monitoring is not a common issue on

the software developing circles, there are not many

solutions that can serve as a de facto standard to

base a new system development. Although we were

able to find some similar solutions, these are

commercial products and therefore their

implementation specifications are not in the public

domain.

Nevertheless, Waadt et al. (Waadt et al, 2005)

presented a framework for a QoS monitoring,

alerting and SMS Center reconfiguration that we can

use as a model for the features that we have

developed. Their system is written in Java and

operates in real-time. It detects when the QoS

parameters drop below, or exceeds previously

defined thresholds, and generates an alert message

that can be used for system reconfiguration. This

system is strongly biased towards the Short Message

System (SMS) of GSM networks and thus it does

not represent a full solution for a more general

performance monitoring system.

Rigallo et al. (Rigallo et al, 2002) have also

proposed an architecture and functional

implementation of a real-time monitoring and

operation assistant system developed for Telecom

Italia Mobile (TIM). This system, named NetDoctor,

is a multi-layer OSS that allows for data collection

and storage, network status monitoring and

automatic failure detection and correction. On top of

this, there is a web interface that allows distributed

access.

Both proposals come short when the goals of

tool design encompass a broad scope solution

covering different areas of mobile networks while

maintaining a good degree of simplicity. These

solutions are based on frameworks, which, as it can

be found on (Fayad et al, 1999), impose a steep

learning curve on their users. We have deviated from

this option by developing a “service-oriented”

solution where the services can be thought of as

components, thus achieving the desired simplicity,

ease of use and also rapid-development ability as a

side effect.

3 SYSTEM MODEL AND

IMPLEMENTATION

In the following description it is useful to separate

the system into two major components: core services

and auxiliary services.

Core services deal with storage, processing and

presentation capabilities. Auxiliary services are

responsible for filling the gap between the physical

network and the data model used in the core

services. This insulating layer must accept the

different data formats used by the network nodes

and convert them to a standard system format. It

must also import data into the storage technology

used by the core services. The auxiliary services act

as an interface between the real world and the core

services.

On the other hand, core services handle the

system’s main concerns: they store the raw and

processed data in a structured way to allow great

system’s flexibility with the chosen database

technology, they supply a mechanism to compute

the KPIs from the defined formulas and they enable

data to be presented in an informative, distributed

and user-friendly way.

The following figure illustrates the system

modelling in terms of components and its relations

with the real world.

The storage service, which is part of the system’s

core services, encompasses the system data model

and the repository where those data is stored. This

repository is traditionally implemented as a

relational database system (Codd, 1970). The

system’s data model can be easily driven by

analysing the proper network hierarchy:

Figure 1: Modelling the system in layers.

WINSYS 2007 - International Conference on Wireless Information Networks and Systems

242

“A network is a set of nodes. A node belongs to

a certain type of network element. This has raw

statistical data objects and KPIs associated to it. A

node can depend on another node, and can have zero

or many nodes that depend on it. A node can be

connected to one or more nodes and this connection

is named a route.”

Using these relations we can represent the data

model structure using a Unified Modelling

Language (UML) (Booch et al, 1970) class diagram.

Figure 2 shows a simplified class diagram that might

be helpful to provide better understanding of the

system’s data model.

Figure 2: Simplified class diagram of the main system.

We present the statistical data (raw and KPIs) on

different tables, with one table per KPI/statistical

data object. We must keep record of the data objects

attributes and values and this leads us to the

concepts of “descriptor” – an entity that is

responsible to describe other entities in terms of

their attributes – and “value holder” – an entity that

keeps the value of another entity defined by a

descriptor. So we should have a table to keep

information about data items such as name, formula

and network element to which the data is associated,

and another one to hold the corresponding data

values. The first table is the “descriptor” and the

second one is the “value holder”. This distinction

makes sense from the database point of view as it

increases its efficiency and provides a clean

structure. The <<metaclass>> UML stereotype is

used to indicate that both raw data types and KPIs

will have a similar structure, but different table

names and column number and names.

Figure 3 illustrates a class diagram showing node

dependency and routing. A node can act as a parent

for other nodes, or a as child of one node. Therefore,

a parent-node can be parent of zero or more nodes,

and a child-node can have, at most, one parent. A

route can also be modelled as an association

between two nodes. The roles of a node in a route

are origin and destination.

Figure 3: Simplified class diagram showing the routes and

node dependency.

With this static structure we can implement the

storage services of the system. Processing services,

as previously mentioned, are responsible for taking

the raw statistical data, applying the appropriate

formulas and calculating the corresponding KPI

values. Although this task can be accomplished in

several ways, it will always have to follow these

stages: retrieve the formula from the KPI descriptor,

retrieve the data from the raw data value holder,

perform the calculation and store the result on the

KPI value-holder.

Presentation services are responsible for fetching

data (both raw and KPI) and organizing it in a

convenient format for a human reader. They can also

be implemented in several different ways.

Configuration services are used to manage

networks, element types and nodes, as well as to

define KPIs and raw data statistical objects.

Another type of services, called auxiliary, must

be able to translate the incoming data to a format

that is understood by the system and then store it in

the database. This translation process is divided in

two stages: parsing the input data and then

processing it to the output format. The next step is to

import the output data into the database. However,

this depends on the technology used, according to its

database management system. To implement these

tasks, one must know the formats for input data and

structure of the database tables, where parsed data

will be stored.

To validate our model, we made a complete

system’s implementation, fed it with real network

data and then we ran some tests on it. To undertake

this task we have implemented the core part of a

GSM mobile network.

The implementation was done on a Linux

machine with MySQL database system, a PHP 5

interpreter and an Apache http server. This

A FLEXIBLE MOBILE NETWORK MONITORING TOOL

243

configuration is widely known as LAMP-bundle and

was chosen due to two criteria: proven system

stability and zero cost.

We propose a model sufficiently general to

accommodate any kind of network whose nodes may

be associated with statistical measures. Another

feature present in our model is portability, allowing

it to be easily migrated to different platforms with

low production costs. That is why we chose PHP, an

interpreted language, and MySQL. PHP also has the

advantages of strong scalability capacities and a

large set of libraries that allow the programmer to

perform many complex tasks without having to

rewrite code for them. Furthermore it can run both

as a server-side application and as a command-line

interface (CLI) application which allows the

implementation of all systems services in PHP. In

fact, the data parsers, the data import-mechanism,

the calculus engine used by the processing services

and all the web content generation code were coded

using PHP. The fact of having only one

programming language in the system also benefits

code maintenance.

Parsers convert statistical data received from

nodes – via the OSS – in a convenient and

meaningful format for the system. We chose this

data format as Delimiter-Separated Values (DSV)

because it is well suited to be imported in MySQL

database systems. For convenience, these parsers

output files whose name identify the node that

produces the data, the raw data type name and the

date of collection. The parsing process is based on

regular expressions matching,

After the parsing process, the output data is

stored in the database. For this we wrote a script that

reads the processed data files and tries to match its

meta-information (file name and headers, if

applicable) with the raw data descriptors stored in

the database. When a match is found, the data is

retrieved from that file, line by line, and written on

the corresponding database table.

These parsing and import processes were

scheduled as cron jobs. Cron is a time-based

scheduler application found on Unix-like systems.

Since these might be resource-consuming activities

they were scheduled to run on an off-peak hour (3

A.M.).

After storage is completed, another cron job

executes the processing services. These were

implemented as a software engine with the ability to

perform mathematical calculus operations. For this

purpose we chose a very useful PHP function:

eval(). This function receives a string parameter

and evaluates it as if it was code. It is useful for

situations where one wants to store code on a

database and execute it later on, which is exactly the

case we are interested in. The processing service

retrieves the KPI formulas on the descriptor table of

the database and evaluates them, for the different

raw data samples, using the eval() function.

After evaluation the results are again stored on the

appropriate KPI tables on the database.

Presentation services are also considered core

services because this system is user-oriented, so the

interface between the data and the user plays a

crucial role in the system. These services were

implemented as a web-site where the user can

request information on several KPIs. A plot for a

given KPI, a set of nodes and date interval was one

of the implemented services. This service allows a

user to follow the variation of a given KPI. To

generate the plots we have used a PHP library

named ‘jpgraph’, which is also open-source. Other

services were also implemented by operator request.

Configuration services were also implemented as

services running on a web-site. Using Hypertext

Mark-up Language (HTML) forms it is possible to

obtain a clear and intuitive way of configuring all

the system’s parameters regarding its management.

4 RESULTS AND DISCUSSION

For illustrating the results obtained with our system

we present in figures 4 and 5 the results produced by

a KPI named “CP LOAD”. This shows the CPU

load of a MSC node and it is evaluated with the

following formula:

CP_LOAD = ACCLOAD / NSCANS (1)

Where ACCLOAD is the accumulated processor

load in percentage and NSCANS is the number of

accumulations. These fields were implemented as

counters on the raw data object that comes from that

node.

In figure 4 we present the figure obtained for the

evolution of KPI “CP LOAD” on a

MSC located at

Luanda, Angola, between 15th and 28th December

2006. It is possible to see a recurring pattern every

couple of days, as well as to determine the lower and

higher mean value intervals. The x-axis represents

time and the y-axis the KPI’s percentual value.

As we can observe in Figure 4, there was no data

for 18

December and also between 23

and 24

December.

Figure 5 shows a different view for the same KPI

and a given date, Christmas Day on 2006, now

concerning two different MSCs located at Luanda

and Benguela, Angola. This figure clearly shows the

traffic evolution on that day, making it easy to figure

out its busy-hour.

WINSYS 2007 - International Conference on Wireless Information Networks and Systems

244

Figure 4: Evolution of “CP LOAD” between 15

December 2006 and 28

December 2006.

These results, as well as the validation of our

complete system, were only possible thanks to an

operator that has provided us the necessary data.

5 CONCLUSIONS

In this paper we have proposed a system model to

implement a network monitoring tool that can be

used to manage networks from telecommunication

operators or other systems alike. This model is

highly flexible and can be easily extended to

accommodate any particular operator’s needs. The

main characteristics of this system are its low

development costs (due to the use of open source

software), portability (since all code is written in an

interpreted language, and thus independent from the

system), reduced complexity (as opposed to the

large framework-based software referred on the

state-of-the-art), modularity (its services are

independent modules), flexibility (a consequence of

its simple structure and modularity) and easy remote

access (via a web page).

The system has been implemented, validated and

tested in a real environment of a GSM operator.

Figure 5: Evolution of “CP LOAD” on two different

MSCs on 25

December 2006.

REFERENCES

Kreher, R., 2006. UMTS Performance Measures: A

Practical Guide to KPIs for the UTRAN Environment,

Wiley.

Waadt, A., Bruck, G., Jung, P., Kowalzik, M., Trapp, T.,

Begall, C., 2005. QoS Monitoring for Professional

Short-Message-Services in Mobile Networks, Wireless

Communication Systems 2005. 2nd International

Symposium on Wireless Communication Systems,

pp228- 232, 5-7 Sept.

Rigallo, A., Stringa, A., Verroca, F. , 2002. Real-Time

Monitoring and Operational Assistant System for

Mobile Networks, IEEE Network Operations and

Management Symposium, pp 899-901, 15-19 April.

Fayad, M., Schmidt, D., Johnson, R., 1999. Building

Application Frameworks – Object-Oriented

Foundations of Framework Design,pp 11, Wiley.

Codd, E., 1970. A Relational Model of Data for Large

Shared Data Banks, Communications of the ACM,

Vol. 13, No. 6, pp. 377-387.

Booch, G., Rumbaugh, J., Jacobson, I., 1998. The Unified

Modelling Language User Guide, Addison-Wesley

Professional.

A FLEXIBLE MOBILE NETWORK MONITORING TOOL

245