Latré, Steven

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Ghent University Academic Bibliography

Towards an Autonomic Architecture forOptimising the QoE in Access NetworksSteven Latre´Supervisor(s): Filip De TurckI. INTRODUCTIONMultimedia services over broadband DSLaccess and aggregation networks such asBroadcast TV and Video on Demand havegained a lot of popularity in the last few years.Operators who want to maximise their revenuetry to manage the service quality as perceivedby the end user, commonly described as theQuality of Experience (QoE). This QoE man-agement is further complicated by the hetero-geneity of today’s access and aggregation net-works, triggering a QoE management on a perservice or per subscriber basis. This introducesthe need for detailed knowledge about usersand services. In our research, we are focusingon an architecture for effectively partitioningthis knowledge in an autonomic managementenvironment.The problem of organising knowledge in anautonomic network has been addressed throughthe Knowledge Based Network paradigm [1].In a Knowledge Based Network, producers ofinformation describe the available informationthrough ontologies. Consumers subscribe tothis information through semantic queries. Thework presented in this paper complements thisapproach: while the KBN work focuses on se-mantic clustering of information [2] and aug-menting the semantic capabilities of existingsolutions [3], we focus on the automatic gen-eration of the semantic queries, which we call—————————————————–S. Latre´ is with the Department of InformationTechnology, Ghent University (UGent), Gent, Bel-gium. He is funded by Ph.D grant of the Fundfor Scientific Research, Flanders (FWO-V). E-mail:Steven.Latre@UGent.be .InformationModelKnowledgePartitionerFilter QuerierCommunicationInterfaceFilter QueryResponderCommunicationInterfacePolicy ModelOther informationmodel architectures Other informationmodel architecturesarchrchitectrchrchHigher layer functionsMonitor probes,Reasoning process, etc.Cognitive modelledgeledgeFigure 1. Architecture for effective knowledge com-munication.filter queries, through a cognitive model.The remainder of this paper is structured asfollows. Section II discusses the integration ofthe cognitive model within the KBN architec-ture. In Section III, the automatic generation offilter queries based on information in the cog-nitive model is discussed for the employed im-plementation. Section IV concludes this paper.II. ARCHITECTURE DESCRIPTIONAn overview of the employed architecture,which is responsible for collecting the neces-sary knowledge for each node by querying in-formation present in other remote nodes, isgiven in Figure 1. At the heart of the archi-tecture lies the information model which repre-sents all knowledge needed for the higher layerfunctions. Conceptually, the architecture splitsthe information model of every node into dif-ferent sub models. Every node X has a dedi-cated information model and several derived in-formation models, containing parts of the infor-mation model of other nodes. The dedicated in-formation model consists of knowledge whichis local to node X . The derived informationmodels contain parts of the information modelsof other nodes and typically contain knowledgeReasoningProcessReasoningTypeRuleBased NeuralNetworkInputClause OutputClauseInformationcontaining volatility and importanceOntologyConceptNodeInformationModelModifierListModifierConstraintTimeConstraintAggregatorSorterhasTypehasParent hasParenthasOutputshasInputshasParent hasParenthasParent hasParenthasFirstisInformationOfisLocatedOnhasModelhasNodehasNexthasParenthasModifiersPolicyModel hasPoliciesc)a)b)hasConstraintsFigure 2. Schematic overview of the concepts andrelationships introduced in the cognitive model.which can aid in the reasoning process of nodeX . The derived models are constructed throughfilter queries, defining which part of the knowl-edge is requested from other nodes.The cognitive model comprises informationabout the capabilities of each node and de-scribes which kind of knowledge is gener-ated by each node. Additionally, the cognitivemodel provides information about the require-ments of the local reasoning process by statingwhat kind of information is needed to reasonupon.The information available in the cognitivemodel is used to select the data that needs to beextracted from other information models. Thisis done in the Knowledge Partitioner, a thirdcomponent in the architecture, which automat-ically generates the filter queries.III. AUTOMATIC GENERATION OF QUERIESTo automatically generate the necessaryqueries, the Knowledge Partitioner relies onknowledge about the reasoning process definedthrough the cognitive model. The cognitivemodel used in our implementation is illustratedin Figure 2. The illustrated concepts can bedivided into three groups. A first group (a),provides information about the reasoning pro-cess itself. The cognitive model describes howreasoning is performed on this node and whatknowledge is needed. In the second group (b),the cognitive model describes how this infor-mation is treated by the reasoning process. Thecognitive model allows for applying policies onthe generation process and supports that infor-mation can be modified or constrained. Thethird part of the cognitive model (c) describeswhich concepts are modelled on remote infor-mation models. This information is needed toknow where the filter queries need to be redi-rected to and if the requested knowledge isavailable or not.The knowledge available in the cognitivemodel can be easily added by investigating thereasoning process of each entity. For example,for a rule based engine, the facts needed to trig-ger rules can be seen as the InputClauses ofthe cognitive model. Depending on how thisinformation occurs in the rules, they may ormay not be aggregated. Based on this cog-nitive model, constructing the filter queries isstraightforward. Each individual of the Input-Clause concept is mapped to a filter query thatand through the is isLocatedOn and hasModelrelationships the Knowledge Partitioner knowswhich information model to contact.IV. CONCLUSIONSWe discussed how filter queries in Knowl-edge Based Networks can be automaticallygenerated through the use of a cognitive model.The cognitive model defines the knowledge re-quirements of each node by identifying the rea-soning processes running on each node and de-scribing the information they need to performtheir task. The structure of the cognitive modelallows to easily map the information onto filterqueries.REFERENCES[1] D. Lewis, D. O’ Sullivan, and J. Keeney, “To-wards the knowledge-driven benchmarking of au-tonomic communications,” in World of Wireless,Mobile and Multimedia Networks, 2006. WoW-MoM 2006. International Symposium on a, 2006,pp. 6 pp.–505.[2] John Keeney, Dominic Jones, Dominik Rob-lek, David Lewis, and Declan O’Sullivan,“Knowledge-based semantic clustering,” in SAC’08: Proceedings of the 2008 ACM symposium onApplied computing, New York, NY, USA, 2008,pp. 460–467, ACM.[3] John Keeney, Dominik Roblek, Dominic Jones,David Lewis, and Declan O’Sullivan, “Extend-ing siena to support more expressive and flexiblesubscriptions,” in DEBS ’08: Proceedings of thesecond international conference on Distributedevent-based systems, New York, NY, USA, 2008,pp. 35–46, ACM.

Towards an autonomic architecture for optimising the QoE in access networks

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Towards an autonomic architecture for optimising the QoE in access networks

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