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SINMS A Slow Intelligence Network Manager based on SNMP Protocol Francesco Colace 1 – Shi-Kuo Chang 2 – Massimo De Santo.

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Presentation on theme: "SINMS A Slow Intelligence Network Manager based on SNMP Protocol Francesco Colace 1 – Shi-Kuo Chang 2 – Massimo De Santo."— Presentation transcript:

1 SINMS A Slow Intelligence Network Manager based on SNMP Protocol Francesco Colace 1 – Shi-Kuo Chang 2 – Massimo De Santo 1 – 1 Department of Information and Electrical Engineering, University of Salerno, Italy 2 Department of Computer Science, University of Pittsburgh, USA DMS 2010 – Chicago, IL

2 Outline The Network Management Towards a Slow Intelligence Network Manager The Slow Intelligence System Approach The Ontology The SNMP Protocol SINMS The proposed architecture A first prototype Evaluation Parameters Experimental Results Conclusions

3 DMS 2010 – Chicago, IL The Network Management The Network Management: the process of controlling a network so as to maximise its efficiency and productivity Network Management Tasks Fault management Configuration management Accounting management Performance management Security management

4 DMS 2010 – Chicago, IL The Network Management The are a small number of accessories methods to support network and network device management. Access methods include the SNMP command-line interface (CLIs) custom XML CMIP Windows Management Instrumentation (WMI) Transaction Language 1 CORBA NETCONF Java Management Extensions (JMX).

5 DMS 2010 – Chicago, IL Towards A Slow Intelligence Network Manager The aim of this paper is to design and implement a Network Manager able: To detect automatically faults in a computer network To infer the actions to do in order to recover the faults To share knowledge about faults and actions with other similar networks The proposed results can be reached by the use of Slow Intelligence System Approach Ontology

6 DMS 2010 – Chicago, IL Slow Intelligent System SISs are general-purpose systems characterized by being able to improve performance over time through a process involving an Enumeration Phase

7 DMS 2010 – Chicago, IL Slow Intelligent System SISs are general-purpose systems characterized by being able to improve performance over time through a process involving a Propagation Phase

8 DMS 2010 – Chicago, IL Slow Intelligent System SISs are general-purpose systems characterized by being able to improve performance over time through a process involving an Adaptation Phase

9 DMS 2010 – Chicago, IL Slow Intelligent System SISs are general-purpose systems characterized by being able to improve performance over time through a process involving an Elimination Phase

10 DMS 2010 – Chicago, IL Slow Intelligent System SISs are general-purpose systems characterized by being able to improve performance over time through a process involving a Concentration Phase

11 DMS 2010 – Chicago, IL Slow Intelligent System A SIS continuously learns, searches for new solutions and propagates and shares its experience with other peers A SIS differs from expert systems in that the learning is not always obvious. From the structural point of view, a SIS is a system with multiple decision cycles such that actions of slow decision cycle(s) may override actions of quick decision cycle(s), resulting in poorer performance in the short run but better performance in the long-run

12 DMS 2010 – Chicago, IL Slow Intelligent System and Network Management A network manager has to find a possible solution starting from a fault signal. So it has to enumerate all the possible solutions: Enumeration Phase A network manager can share with other systems or experts knowledge in order to acquire new solution’s approaches: Propagation phase A network manager has to adapt a candidate solution to the context of the managed network: Adaptation Phase A network manager has to select only one solution: Elimination Phase A network manager has to execute at its best the selected solution: Concentration Phase

13 DMS 2010 – Chicago, IL Slow Intelligent System and Ontology Ontology

14 DMS 2010 – Chicago, IL Ontology for Network Management Ontology the definition of ontology is still a challenging task a good practical definition is: “an ontology is a method of representing items of knowledge (ideas, facts, things) in a way that defines the relationships and classification of concepts within a specified domain of knowledge” O = {C, A, R T, R, A X }

15 DMS 2010 – Chicago, IL Ontology for Network Management The development of the ontologies’ system has been obtained By the use of SNMP protocol It is more than just a protocol. In fact it defines an architecture for extracting information from the network regarding the current operational state of the network, using a vendor-independent family of mechanisms By the use of experts

16 DMS 2010 – Chicago, IL Ontology for Network Management In the case of the proposed Network Manager the following ontologies have been developed: O SNMP = {C SNMP, A SNMP, H SNMP, R TSNMP, R SNMP }. This ontology aims to define the entire structure of SNMP protocol by analyzing the various messages and the relations between them O Fault = {C Fault, A Fault, H Fault, R TFault, R Fault }. This ontology describes each kind of possible errors that can occur within a LAN O Cause = {C Cause, A Cause, H Cause, R TCause, R Cause }. This ontology defines the causes of the faults that may occur in a LAN O Solution = {C Solution, A Solution, H Solution, R TSolution, R Solution }. This ontology defines the solutions that can be taken to recover from fault situations which occurred within a LAN O Action = {C Action, A Action, H Action, R TAction, R Action }. This ontology aims to identify the actions to be taken in order to recover from fault situations O Component = {C Component, A Component, H Component, R hComponent, R Action }. This ontology describes the components that may be present within a LAN O Environment = {C Environment, A Environment, H Environment, R hEnvironment, R Environment }. This ontology describes the operative context where the LAN works

17 DMS 2010 – Chicago, IL Ontology for Network Management: Faults

18 DMS 2010 – Chicago, IL Ontology for Network Management: Actions

19 DMS 2010 – Chicago, IL SINMS – The Proposed Architecture Local_Server_k Central_Server Device_k_1Device_k_2Device_k_n … … … Local_Server_m Device_m_1Device_m_2Device_m_n … … … Local_Server_i Device_i_1Device_i_2Device_i_n … … … Local_Server_j Device_j_1Device_j_2Device_j_n … … … O k-SNMP O k_Fault O k_Cause O k_Solution O k_Action O k_Component O k_Environment O m-SNMP O m_Fault O m_Cause O m_Solution O m_Action O m_Component O m_Environment O i-SNMP O i_Fault O i_Cause O i_Solution O i_Action O i_Component O i_Environment O j-SNMP O j_Fault O j_Cause O j_Solution O j_Action O j_Component O j_Environment O central-SNMP O central_Fault O central_Cause O central_Solution O central_Action O central_Component O cemtral_Environment

20 DMS 2010 – Chicago, IL SINMS – The Proposed Architecture Device_1 Zabbix_Agent Device_2 Zabbix_Agent Device_N Zabbix_Agent … … … Zabbix_Server SNMP-Message Reader SINMS Local_Server_i SINMS Central_Server SNMP_ Events Ontologies Local_Server_i Actions Ontologies Central_Server Other_Local_servers

21 DMS 2010 – Chicago, IL SINMS – The Operative Workflow Action Builder Action Builder Action Builder Action Builder Ontology Updating Ontology Updating Comparator Empty Set Comparator Empty Set Comparator Empty Set Action Selector Ontology Selector Actuator Report Generator Ontologies Local Server Local_Server_Actions Actions Local_Server_Actions Local_Servers Central Server_Actions Ontology_Nodes

22 DMS 2010 – Chicago, IL SINMS – The Prototype Adopted Technologies for the framework development Java MySql SNMP Zabbix OWL Protegè

23 DMS 2010 – Chicago, IL SINMS – The Prototype

24 DMS 2010 – Chicago, IL Experimental Scenario 1 The network manager has to manage two different LANs. The first one is composed by a Cisco switch and 30 personal computers The second LAN is composed by a Nortel switch, 30 personal computers equipped with various operative systems and a HP network printer. Each local server has SNMP ontology able to cover the 80% of the SNMP messages that the hosts in the LAN can launch

25 DMS 2010 – Chicago, IL Experimental Scenario 1 The experimental phase aimed to evaluate the following parameters: The system’s ability to identify the correct management actions to apply in the LAN after a SNMP signal. This parameter, named CA, is so defined: The system’s ability to select in a LAN a viable solution that was previously adopted in a similar case in another LAN. This parameter, named IS, is so defined: The system’s ability to manage the introduction of a new component in a LAN. In particular the system has to recognize components that were previously managed in other LANs. This parameter, named KC, is so defined:

26 DMS 2010 – Chicago, IL Experimental Scenario 1 The previous indexes were calculated in the following way: The CA index: this index was calculated after 10, 20, 30, 40 and 50 SNMP signals. In this case there was not variations in the LANs The IS index: this index was calculated forcing some SNMP events in the LAN not expected in its SNMP reference ontology. This index was evaluated after 10, 20, 30, 40, 50 SNMP signal not expected. The KC index was estimated after the introduction of new components in a LAN. In particular for five times a component belonging to a LAN has been shifted in the other LAN and the index was evaluated after 10, 20, 30, 40, 50 SNMP signal launched from the host. Index CA 90,00%95,00%93,33%92,50%94,00% IS 50,00%60,00%66,67%70,00%74,00% KC 60,00%70,00%76,67%80,00%82,00%

27 DMS 2010 – Chicago, IL Experimental Scenario 2 The Network Manager has been tested for 72 hours monitoring the following LANS Lab_1: 1 Switch Cisco Catalyst 1 HP Network Printer 40 Personal Computer Lab_2 1 Switch Nortel 1 Canon Network Printer 35 Personal Computer Lab_3 1 Switch Cisco Catalyst 50 Personal Computer

28 DMS 2010 – Chicago, IL Experimental Scenario 2 The network manager can recognize 237 OID Each local server can recognize and manage 80 OID (selected in a randomatic way) The overlapping among the systems is the following: S_L_1 and S_L_2 = 45 S_L_1 and S_L_3 = 39 S_L_2 and S_L_1 = 37

29 DMS 2010 – Chicago, IL Experimental Scenario 2 SNMP_SignalsManaged_SignalsManaged_Signals_After_Central_Server_RequestNot_Managed_Signals Local_Server_ Local_Server_ Local_Server_ hours M/MAR/NM 48 hours M/MAR/NM 72 hours M/MAR/NMLocal_Server_ – – – Local_Server_ – – – Local_Server_ – 12 – – –

30 DMS 2010 – Chicago, IL Conclusions In this paper a novel method for network management has been introduced This method is based on SNMP Ontology Slow Intelligence System approach The approach has been tested in various operative scenario with good results The future works aim to improve the system by the introduction of some modules based on Artificial Intelligence for the automatic inference of actions when the network manager does not find any solutions


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