An Ontological Approach to Network Management

Transcription

1 An Ontological Approach to Network Management Meltem Gürel Conor Smith Evangelos Stephanou Henry Tah January 28, 2009

2 1 Introduction Several challenges in network and system management, as a result of the lack of interoperability between management domains, models, data and semantics have risen the need for innovative mechanisms to allow the interoperability among all involved management domains/models, by mapping between the information modules that each domain specifies. Ontologies provide a way to overcome some of theses issues and allow interoperability between various domains, by means of formalising data and providing data structures that are machine understandable. Ontologies have been successful in solving similar semantic problems in other areas, such as the semantic web. In this work, we present a review of the state of the art in the area of using ontologies to solve problems in network management, specify the advantages and possible drawbacks of an ontological approach and try a prototype ontology driven implementation as a proof-of-concept. 2 State of the art 2.1 Ontological Approaches Although there is not a universal consensus on the definition of ontology, we can find several definitions in the literature, such as a formal, explicit specification of a shared, machine-readable vocabulary and meanings, in the form of various entities and relationships between them, to describe knowledge about the contents of one of the related subject domains throughout the life cycle of its existence [Wong et al., 2005], set of knowledge terms, including the vocabulary, the semantic interconnections, and some simple rules of inference and logic for some participants [Ben Yahia et al., 2007]. Key applications of the ontologies are collaboration, interoperation, education and modeling [Ben Yahia et al., 2007]. One of the most successful example of the ontologies is in the area of the Semantic Web. Ontologies are used to describe a Web Service as well as provide a way for automatic service composition and extend syntactic interoperability to semantic interoperability. [Medjahed et al., 2003]. The success of semantic web relies heavily on formalised knowledge to structure data for comprehensive and transportable machine understanding [Maedche et al]. The semantic web relies on two basic technologies: 1. Ontologies by wich, the domain concepts, concept hierarchies and concept relationships can be expressed, and 2. Logical reasoning by which new conclusions can be drawn after combining data with ontologies [Haydarlou et al., 2006]. 1

3 Semantic web ontologies are expressed in a description logics language called Ontology Web Language (OWL). Logical reasoning is expressed in rule language called Semantic Web Rule Language (SWRL) (W3C proposal). Ontologies have also been used to describe services in several areas, such as mobile environments [Veijalainen et al., 2006], policy interactions and policy-based management [Davy et al., 2007], New Generation Networks and Services (NGN, NGS) [Ben Yahia et al., 2007]. Also, [Haydarlou et al., 2006] explores the potential of ontological commitments (using semantic web technology) for self-management of distributed object-oriented systems, based on a self management framework and a real life case. The framework in this paper is based on the concept of system use cases which describe the response of system to a given request. The authors also explore the potential of semantic web technology in relation to the requirements that distributed object. In the case of semantic interoperability, the semantic web that incorporates intelligent agents necessitates an interoperability solution requiring agents to communicate unambiguously and reason intelligently to perform corporative management task. An agent needs a formal representation of Knowledge [Wong et al., 2005]. These challenges can be addressed by: 1. The introduction of a new architecture for a network management platform based on ontology-driven multi-agents system (MAS) involving both fixed and mobile agents. [Stephan et al., 2004] 2. Applying ontology base semantics to the development and operation of management systems [Strassner et al., 2008]. Apart from the Semantic Web, significant work has been done in the area of Autonomic and Self-Managed Systems. [Lewis et al., 2006] provide a basis for a common approach on evaluating and benchmarking autonomic communications, setting the key metrics, such as cost, complexity, quality, dependability, resilience etc. Also, [Zhou et al., 2007] propose an architecture called MAC (Middleware for Adaptive Coordination), which support modeling, reasoning about the context knowledge and architecture-based self-management actions. In this proposed architecture, ontologies (specifically OWL) are used to model the context and also provide reasoning over that context, which means inferring implicit information from explicit ones, knowing the unknown from the known. (Figure 1) Also [Lewis et al., 2004] introduce a conceptual architecture for pervasive computing that integrates semantic service composition and policy-based management. [Strassner et al., 2008] actually implement an autonomic architecture, which uses a shared information model in order to harmonize different data models. In order to do that, and since data models are not capable of representing detailed semantics, the information and data models 2

4 are augmented with ontologies. Finally, another architecture for ontologybased context & policy interaction is proposed by [Davy et al., 2007], called PRIMO (Policy Relations and Interaction for Management using Ontologies). Again, ontologies are used to provide the context for the policies and rules, as well as different vocabularies, grammatical rules and semantic integration techniques. This allows, for example, relating one vocabulary and set of grammatical rules to another vocabulary and set of rules, without that relationship having been explicitly identified, but instead is inferred by the automated reasoning capabilities of the ontology that is used. 2.2 Ontologies in Network Management In the past couple of decades the evolution of technology and the increased needs for big and complex networks have led to the parallel increase in the complexity of the management of those networks. On one hand, users expect environments and networks that are easy to use and on the other hand new ways of managing these complex networks are needed. Under these circumstances, and taking into consideration the dynamic nature of evolving networks, future network management solutions need to be flexible, adaptable and intelligent, without increasing the burden on network resources [Stephan et al., 2004]. Also, the multiplicity of network management models may imply the use of multiple languages to define the managed objects. However, these languages have different levels of semantic expressiveness which may difficult to translate across different management information models [de Vergara et al., 2003a]. We will see how ontologies and semantic reasoning are proposed to help define new frameworks and architectures in the area of network management. Even before ontologies were introduced spread, there has been certain effort to achieve managing network resources, permitting the coexistance of Figure 1: The MAC architecture example 3

5 different management models and allow interoperability between them but also with legacy systems. [Dimopoulou et al., 2003] introduced an XMLbased management platform for QoS-aware IP networks, called QMTool. This tool adopted XML to provide an abstract definition model for configuration and monitoring of network elements, and Java/CORBA environment to enhance this definition model with with strong interoperability capabilities, portability, scalability and easy integration with both new and existing technologies. Using XML can present some advantages, since many tools and libraries can handle this type of information and used to develop network management applications, and at the same time present this information in different ways with the use of XSLT sheets. Furthermore, Document Type Definitions (DTD) or XML Schemas can specify various formats the information may have. On the other hand, however useful this technologies maybe in general, they do not provide semantics and reasoning over the information they deliver. Parsing those files can give us plenty of information but no inferrence can be made on the state of the network [de Vergara et al., 2004]. The next step, that leads to ontologies and reasoning over the information available, is the use of the Resource Description Framework (RDF) and RDF Schema (RDFS), which are XML-based languages but with the added feature of providing a way of defining structured sets and hierarchies, as well as domain-range constraints. Furthermore, some ontologies languages, such as OWL, are based on RDFS, and actually go beyond it, allowing machines to perform useful reasoning tasks [de Vergara et al., 2004]. Having defined the languages and tools needed, is now possible to see how these have been used to solve the network management problems we have discussed. There have been several attempts to compare and contrast several of the information models that are currently used in the field (CMIP, SMI-SNMP, WBEM, etc). Such a comparison is carried out in [de Vergara et al., 2003a], where several information model languages are compared against various characteristics. After the comparison, MOF/CIM seems to have the best semantic expressiveness, where SMIng and GDMO are near. Having done that, they next describe the process of obtaining a common management model. The basic steps in this process are: a. Merging, where all the models are merged in a global ontology, with overlapping concepts redefined to a common and unique concept and not-overlapping concepts included in the ontology with the additional relationships so that the ontology is able to reason with them. b. Mapping, where mapping rules are defined to translate instances from one model to the global ontology. [de Vergara et al., 2004] present an introduction of the ontologies concept, as well as the OWL and other languages, compairing their semantic 4

6 expressiveness. They also give an example of using OWL to merge and map several management information languages (SMIv2, GDMO, MOF/CIM) into one common ontology. [Wong et al., 2005] extensively investigate the semantic interoperability problem at different levels of network management of enterprise services using ITU-U Telecommunication Management Network (TMN), and proposed an ontology-driven approach to address this problem. The approach emphasises the similarity function and ontology mapping components with an illustration of Cisco and Nortel router configuration management application. The evaluation consists of a Cisco and Nortel ontology constructed using Protégé, and an ontology mapping strategy implemented in Java. The evaluation result based on the analysis, modelling and classification of approximately 250 Cisco commands and 200 Nortel commands into two different ontologies, shows that, the Cisco commands were more general than the Nortel commands, with no match in the semantics. The authors found that each Cisco command archives less than a Nortel command, and Cisco s programming model differs from Nortels (due to the different Cisco administration Modes). [de Vergara et al., 2003b] shows the advantages of using formal ontology techniques to improve the integration of current network management model. An illustration was done using the Web-Based Enterprise Managements (WBEMs) Common Information Model (CIM) which is a kind of ontology that defines the information of the management domain in a formal way. But they do not include the heavyweight ontology; hence interoperability is not completely achieved in CIM. The authors then proposed a method to create a network management information model based on formal ontology by extending the CIM, and adding the necessary axioms and constraints to obtain heavyweight ontology. The authors postulate that by defining a set of rules to enable the merging of CIM with all other models (e.g. the merging of CIM and SNMP Mobs), will result in CIM based ontology containing all information defined in other management models. With this approach, a manager can use the merged ontology to have unified view of all the information it manages and translate it, by applying the mapping rules by applying it to each domain information model taking into account the semantics of the concepts. [Wong et al., 2005] extensively investigate the semantic interoperability problem at different levels of network management of enterprise services using ITU-U Telecommunication Management Network (TMN), and proposed an ontology-driven approach to address this problem. The approach emphasises the similarity function and ontology mapping components with an illustration of Cisco and Nortel router configuration management application. The evaluation consists of a Cisco and Nortel ontology constructed using Protégé, and an ontology mapping strategy implemented in Java. The evaluation result based on the analysis, modelling and classification 5

7 of approximately 250 Cisco commands and 200 Nortel commands into two different ontologies, shows that, the Cisco commands were more general than the Nortel commands, with no match in the semantics. The authors found that each Cisco command archives less than a Nortel command, and Cisco s programming model differs from Nortels (due to the different Cisco administration Modes). [Xu & Xiao, 2006, Xu & Xiao, 2007] combine ontologies, web services with network management. In [Xu & Xiao, 2007] they also try introduce a common model for the integration of Ontology-based and Policy-based automatic network management. This process consists of several steps: Step 1: An ontology tool (such as Protégé-OWL editor) is used to convert PIB and MIB to a OWL+SWRL network management ontology. Step 2: According to the behaviors defined in SWRL for both management information and policies, corresponding actions are defined in the form of network management services by OWL-S. Step 3: All the policies created in the ontology form a common policy repository which can be used as usual by a policy-based network management architecture. Finally, [Keeney et al., 2006] introduce a framework for the delivery of network knowledge as context for decision making in various forms of management solutions. In the prototype that is implemented attempts are made to match the semantics of the SMI and CIM network management models. This information is then made available by a Knowledge Delivery Service to the Knowledge Delivery Network. Furthermore, they examine extent to which existing mapping techniques can be employed for runtime semantic interoperability for network knowledge and offer a more decentralized knowledge exchange mechanism. 3 Prototype: An Application for Intelligent Query of MIB Objects 3.1 Description Here our aim was to produce a solution to overcome the issue which arises when using SNMP tools such as net-snmp that the user needs to know the object names. We propose that it would be better if the tool could accept an argument such as tcpsegmentsreceived instead of tcpinsegs. We have built a semantic web application using Java. The application accepts an argument about managed objects that may not be MIB object names. Once submitted, the application finds the intended object and sends an snmpget command to the SNMP agent. 6

8 When building the application we used the Protégé-OWL Editor (v4) to create the ontology for network management and Jena to build the semantic web application. Below we give brief descriptions of these tools. 3.2 Tools Used Protégé is a free, open source ontology editor and a knowledge acquisition system. It is being developed at Stanford University in collaboration with the University of Manchester. Here we have used the Protégé-OWL (4.0) editor which enables users to build ontologies for the Semantic Web, in the W3C s Web Ontology Language (OWL). OWL ontologies can be categorized into three sub-languages: OWL-Lite, OWL-DL and OWL-Full. The defining feature of each sublanguage can be specified as its expressiveness. OWL-Lite is the least expressive and OWL-Full is the most expressive sub-language. The expressiveness of OWL-DL falls between that of OWL-Lite and OWL-Full. In this project we used the OWL-DL sub-language which is based on Description Logics (i.e. computations/algorithms based on the logic will terminate in a finite time). Description Logics are open to automated reasoning. It is therefore possible to automatically compute the classification hierarchy and check for inconsistencies in an ontology that conforms to OWL-DL. The reason we chose OWL-DL was to benefit from the maximum expressiveness possible while retaining computational completeness (all conclusions are guaranteed to be computed), decidability (all computations will finish in finite time), and the availability of practical reasoning algorithms. Ontologies that are described using OWL-DL can be processed by a reasoner (also known as classifiers). One of the main services offered by a reasoner is to determine if a superclass/subclass relationship exists between two classes. By doing so, a reasoner computes the inferred ontology class hierarchy. Another standard service that is offered by reasoners is consistency checking. Based on the description (conditions) of a class the reasoner can check whether or not it is possible for the class to have any instances. A class is deemed to be inconsistent if it cannot possibly have any instances. Pellet which is an open source OWL-DL reasoner written in Java was used to infer relationships between classes. To build the prototype application we used Jena, which is an open source framework for Java. Jena allows OWL ontologies to be read and manipulated in Java applications. 3.3 Implementation The OWL ontology Using the Protégé editor we first created an OWL ontology of the following MIB objects: ipsystemstatsindelivers, hrsystemuptime, ipdefaultttl, 7

9 sysuptime, tcpinsegs, tcprtomin and udpindatagrams. These objects are represented as classes which are subclasses of the mibobjects superclass. In order to define these MIB objects, we used two more classes, namely SYNTAX and mibdefs. Here SYNTAX represents the set of type definitions used in the syntax field in the SMI definition for objects (e.g. TimeTicks) while mibdefs represents the set of modules in which the MIB objects are defined (e.g. TCP-MIB modules). We then defined the object property hassyntax which relates the MIB objects to their corresponding syntax definition. To identify the relationship between the MIB objects and their MIB modules we used the object property specified as ispartofmib- Def. In figure 2 relations of some classes in the OWL ontology are shown. Figure 2: Some classes in the ontology 8

10 The two object properties are defined with set logic as follows: Object properties hassyntax 1 hassyntax Thing hassyntax Thing mibobjects hassyntax SYNTAX ispartofmibdef 1 ispartofmibdef Thing ispartofmibdef Thing mibobjects ispartofmibdef mibdefs We then add another class called aliases. This class has several subclasses which represent alias names of the MIB objects we had previously defined. For example the MIB object tcpinsegs is represented with the subclasses tcpinputsegments, tcpreceivedsegments and tcptotalsegmentsreceived. The complete asserted class hierarchy can be seen in Figure 3. In OWL a restriction describes a class of individuals based on the relationships that members of the class participate in. In other words a restriction is a kind of class, in the same way that a named class is a kind of class. Universal restrictions describe classes of individuals that for a given property only have relationships along this property to individuals that are members of a specified class. For example, the class of individuals that only have hassyntax relationships to members of Counter32. In Protege 4 the keyword only is used. In order to relate MIB objects and their aliases, we set restrictions in such a way that a reasoner places the alias name classes as subclasses of their corresponding MIB objects. For example, a reasoner will put class tcpinputsegments as a subclass of tcpinsegs. To do this we set the two restrictions below: If something is a member of class tcpinsegs, then it is necessary that something hassyntax Counter32 and ispartofmibdef TCP-MIB If something hassyntax Counter32 and ispartofmibdef TCP-MIB, then it must be a member of class tcpinseg. 9

11 Figure 3: Complete asserted class hierarchy 10

12 For each of the mibobjects subclasses we defined the relationship between them and the classes stated above as equivalent, i.e. as an necessary and sufficient criteria. The subclasses of the alias class have the above classes as superclasses, i.e. necessary criteria. The logic is shown below for tcpinsegs and tcpinputsegments: tcpinsegs tcpinsegs hassyntax Counter32 ispartofmibdef TCP-MIB tcpinsegs mibobjects tcpinputsegments tcpinputsegments hassyntax Counter32 ispartofmibdef TCP-MIB tcpinputsegments aliases Here tcpinputsegment also has syntax Counter32 and is part of MIB definition TCP-MIB. Moreover, tcpinsegs also has a stronger restriction such that another class which has the same properties will be classified as its subclass. This is shown as a screenshot from the the Protégé 4 environment in Figure 4. Figure 4: The Protégé environment Having completed the OWL ontology we now invoke the reasoner. Protégé 4 allows different reasoners to be plugged in, we use the Pellet reasoner. The ontology can be sent to the reasoner to automatically compute the classification hierarchy, and also to check the logical consistency of the ontology. The class hierarchy that is automatically computed by the reasoner is called the inferred hierarchy. Here as expected the reasoner specified the aliases as subclasses of the relevant MIB objects, i.e. it automatically computed the subclasses of each MIB object. The inferred class hierarchy can be seen in Figure 5. 11

13 Figure 5: Inferred class hierarchy 12

14 The reasoner has determined that tcpinputsegments, tcpreceivedsegments and tcptotalsegmentsreceived are subclasses of tcpinsegs. This is because we have defined tcpinsegs using necessary and sufficient conditions. Any individual that has syntax Counter32 and is part of MIB definition TCP-MIB is a member of the class tcpinsegs. Due to the fact that all of the individuals that are described by the classes tcpinputsegments, tcpreceivedsegments and tcptotalsegmentsreceived have syntax Counter32 and are part of MIB definition TCP-MIB the reasoner has determined that these classes must be subclasses of tcpinsegs The prototype application A prototype application was built in Java using the Jena framework and the Pellet reasoner. The application reads in our ontology. The Pellet reasoner is used to classify the ontology and infer a class hierarchy from this. In this inferred hierarchy our subclasses of aliases are subclasses of the respective MIB objects they are aliasing. The user is able to enter a string to see if it is an alias of any known MIB object. Because space characters are not part of the ontology class names, but are obviously a part of natural language the application trims all whitespace from the user-entered string before querying the reasoner. The application uses the Jena InfModel class s function listobjectsofproperty, which takes in the trimmed user-entered string, prepending the namespace of our ontology to the string. The function also takes in an ontology property, which in our application is subclassof. The function returns the objects in our ontology that the user-entered alias is a sub class of. There are multiple results, including Thing, mibobjects and, of course, aliases. The one result we are looking for is the name of the MIB object relating to the alias. This name is taken from the results of the function and presented to the user. If the alias entered by the user is not an alias of any of the MIB objects in the ontology then the user is informed so. When developing our prototype application we have used the localhost as an snmp server. The MIB object identifier (OID) is determined based on the actual MIB object s name. Here we used the Java SNMP Package which is an open-source implementation of the SNMP protocol as a Java package. It provides support for basic SNMP client and agent operations as defined in SNMP versions 1 and 2. The package provides a mechanism for getting and setting SNMP object identifier (OID) values. Much of the top-level user functionality is provided by the class SNMPv1CommunicationInterface. In the code below we create a communications interface to the localhost, by providing its InetAddress and the community name and supplying the version number for the SNMP messages to be sent (in our case it is 1 which represents SNMPv2): 13

15 InetAddress hostaddress = InetAddress.getByName(" "); String community = "rocommunity"; int version = 1; SNMPv1CommunicationInterface cominterface = new SNMPv1CommunicationInterface(version, hostaddress, community); We then send an SNMP GET request to retrieve the value of the SNMP variable corresponding to the retrieved OID. The type and value of the relevant object is displayed on the screen Testing We run the application and enter the alias host run time to express the MIB object hrsystemuptime. Once submitted the application displays the retrieved value and type information of this MIB object. (Figure 6) Figure 6: Application screenshot The same object can also be called with another expression, amount of time host initialized as shown below. (Figure 7) Evaluation Overall our prototype performs well. One of the downsides is that currently the application is case sensitive, simply because the alias names in the ontology are not uniformly cased. The listobjectsofproperty function from 14

16 Figure 7: Application screenshot Jena needs the exact class names to work. The application can also enable users to configure the parameters that are set within (InetAddress, community name, etc.). The network management ontology we have provided is open to further improvement; once the current restrictions are eliminated our application can be enhanced in order to enable the final application to be presented as a semantic layer for a MIB browser. 4 Conclusions Ontologies look promising in tackling the challenges that are presented in managing modern networks, in providing new generations of services and can help to integrate several technologies to achieve that goal. In this work we tried to present an overview of the state of the art in this field, which is still relatively new and has plenty of aspects that have not yet been fully discovered. We also implemented a simple prototype application to demostrate the capabilities and usage of the ontologies in network management as a proof of concept. Future work in the implementation would include further integration and mapping of human language to elements of the ontology (and in extend, to the managed objects) and also making the application more technology independent, including several information models under one shared ontology. Finally, this application could also be deployed as a web service, instead of a local stand-alone application. 15

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