The Internet has evolved from a service to transport simple text files into a platform for transporting a variety of complex multimedia services. The initial centralized management systems were not designed and are therefore not able to perform efficient management of Quality of Experience (QoE) for these complex services. Deploying an autonomic management system resolves these complexity issues and allows efficient resource allocation based on the service type, end-user requirements and device characteristics. However, existing autonomic management systems only allow limited cooperation between different autonomic elements (AE), which limits their capabilities to provide end-to-end QoE assurance. This research will therefore design cooperative AEs, optimize their organization and provide cooperative allocation algorithms to optimize end-to-end QoE

Bouten, Niels

De Turck, Filip

Latré, Steven

English

Part 4: Ph.D. Workshop — Content Distribution and MultimediaInternational audienceThe Internet has evolved from a service to transport simple text files into a platform for transporting a variety of complex multimedia services. The initial centralized management systems were not designed and are therefore not able to perform efficient management of Quality of Experience (QoE) for these complex services. Deploying an autonomic management system resolves these complexity issues and allows efficient resource allocation based on the service type, end-user requirements and device characteristics. However, existing autonomic management systems only allow limited cooperation between different autonomic elements (AE), which limits their capabilities to provide end-to-end QoE assurance. This research will therefore design cooperative AEs, optimize their organization and provide cooperative allocation algorithms to optimize end-to-end QoE

Turck, Filip,

Archive Ouverte en Sciences de l'Information et de la Communication

QoE-Centric Management of Multimedia Networks through Cooperative Control Loops

Niels Bouten

Steven Latré

Filip De Turck

Crossref

Ghent University Academic Bibliography

QoE-centric Management of MultimediaNetworks through Cooperative Control LoopsNiels Bouten, Steven Latre´, and Filip De TurckDepartment of Information Technology, Ghent University - iMinds{niels.bouten,steven.latre,filip.deturck}@intec.ugent.behttp://ibcn.intec.ugent.beAbstract. The Internet has evolved from a service to transport simpletext files into a platform for transporting a variety of complex multi-media services. The initial centralized management systems were notdesigned and are therefore not able to perform efficient managementof Quality of Experience (QoE) for these complex services. Deployingan autonomic management system resolves these complexity issues andallows efficient resource allocation based on the service type, end-userrequirements and device characteristics. However, existing autonomicmanagement systems only allow limited cooperation between differentautonomic elements (AE), which limits their capabilities to provide end-to-end QoE assurance. This research will therefore design cooperativeAEs, optimize their organization and provide cooperative allocation al-gorithms to optimize end-to-end QoE.Keywords: Autonomic Network Management, Multimedia Services1 IntroductionThe consumption of multimedia services over the Internet has increased tremen-dously over the past decade and this growth is forecasted to continue over thenext years [1]. Not only the variety of services has expanded, also the end-userdevices used to access those services have diversified. These heterogeneous ser-vices and devices have more stringent demands in terms of bitrate, maximumpacket loss and maximum latency, than the services for which the Internet wasinitially designed. To be able to cope with this increased demands, providerstend to over-dimension the available resources to be able to meet the Qualityof Service (QoS) levels at all times. These are however expensive upgrades forwhich end-users pay for in the end. The problems however are not caused by alack of infrastructure, but are induced by an inefficient allocation of the availableresources. The overall quality as perceived by the end user needs to be expressedin terms of Quality of Experience (QoE) and can not be achieved by assigningequal bitrates to each data flow [2]. Although QoE is related to QoS, there isno direct mapping between both concepts: a small percentage of packet loss willyield acceptable QoE for a data transmission service, but the same QoS-levelswill yield artifacts when applied to a video streaming, deteriorating QoE. The al-location of resources to data flows therefore needs to include flow characteristics(e.g., the content of the flow, user subscription level, end-user device properties)during the decision process.These requirements induce a steep increase in complexity for the centralizednetwork management software, leading to complicated systems for the operatorsand towering maintenance costs. Therefore, the autonomic networking paradigmproposes to shift the workload to the system itself by using distributed autonomicelements (AEs) which are responsible for configuring the system and react tonovel situations without requiring time-consuming manual interventions by hu-man operators. Existing autonomic systems however only allow limited cooper-ation between the individual control loops, potentially leading to counteractingdecisions. To be able to provide end-to-end quality assurance, the autonomiccontrol loops need to cooperate in order to provide an optimal solution.2 QoE-centric managementTo be able to cope with the increasing complexity of network management soft-ware, a network of distributed AEs needs to be deployed. These AEs each man-age one or more QoE-optimizers (e.g., rate adaptation system, access controlsystem, caching infrastructure). Existing autonomic systems use isolated ele-ments which take their decisions independently from other elements. Figure 1shows how the lack of cooperation between elements prevents the managementsystem to provide optimal end-to-end quality assurance. When congestion ariseson the delivery path between the end-users and the video source (1), this will besignaled to both AEs along this path (2). Since the AEs do not cooperate, bothof them will decide to take action: sessions will be declined (3) and the videobitrate will be lowered to eliminate congestion (4). In this way, less users willbe admitted and they will receive a degraded version of the video, deterioratingoverall QoE due to the lack of cooperation.Bitrate  Adaptation Admission  Control1.  Congestion2.  Signalling4.  Adapt        rate 3.  Refuse        sessionsVideo  server Edge  routerCore  routerAccess  router DeviceAE AEFig. 1. Example of conflicting decisions between AEs when no cooperation is present,leading to inefficient use of resources and a drop of QoE.Enabling cooperation between AEs does not only require extension of exist-ing control loops such as the FOCALE control loops [3] to support communi-cation and knowledge exchange, they also need to be structured to form a scal-able and reliable network management overlay. The impact of a registry-based,multi-agent [4] and hierarchical organization [5] of AEs in terms of scalability,efficiency, reliability and convergence needs to be quantified. One of the draw-backs of a registry-based organization is the use of a centralized repository, whichundermines the scalability and reliability of the system. Multi-agent based or-ganization allows flexible interaction between groups of agents via a shared bus.Although providing solutions for scalability and reliability, the lack of global in-formation can lead to divergent decisions between AEs. A hierarchical approachovercomes these issues, since the root has access to aggregate information on theentire system, but at the same time this introduces trust issues when orches-trating cooperation beyond the borders of management domains since controlneeds to be transferred to a third party entity. Taking into account the previousremarks, a hybrid solution proposes itself where resources are managed hierar-chically within a certain domain with a multi-agent based organization acrossmanagement domains.In order to provide end-to-end quality management, distributed decision al-gorithms need to be deployed in the autonomic management overlay. The goalof these algorithms is to keep track of the available resources, identify whichactions are required in the network and which components are able to completethese actions. A first key step to attain QoE-centric management is to identifythe impact of the managed components on QoE and model the different states,actions and transitions using finite state machines. This allows the autonomicsystem to analyze the network condition, determine the corresponding state andidentify the possible QoE-improving actions that are available. A second step isto cooperatively decide which QoE-optimizers will be deployed and whom shouldmanage them. The third step in achieving autonomic QoE-centric managementis to dynamically adapt the constructed state models to be able to cope withunknown situations.3 Obtained resultsDuring the first year of the PhD, the scalability issues arising from replicated uni-cast HTTP Adaptive Streaming (HAS) sessions were addressed [6]. A hierarchi-cal approach was taken, where the autonomic distribution management collectssession data from the delivery servers, analyzes this and decides which sessionswill be grouped. The autonomic delivery management then decides which multi-cast channels it wants to receive. This approach led to a considerable decrease inbandwidth consumption (i.e. 23% when applying autonomic distribution man-agement and an additional 25% when applying autonomic channel selection atthe delivery server) while increasing the QoE. The disadvantages of the absenceof cooperation were also discussed in previous work, showing the difficulty ofcompeting autonomic HAS clients to converge to a stable solution [7]. By de-ploying additional in-network management, taking into account the subscriptionand device parameters of the clients, the stability and performance of autonomicHAS clients was improved, leading to a higher overall QoE.4 ConclusionTo overcome QoE-management issues in centralized network management sys-tems, this paper proposes to apply autonomic management for end-to-end QoEoptimization. This is obtained using an overlay of cooperative AEs which pre-vents contradictory decisions, deteriorating end-to-end QoE. Using a hybrid ap-proach between a hierarchical and agent-based organization overcomes issueswith a trusted third party entity and lack of global information that arise inrespectively hierarchical and multi-agent organization. In order to support end-to-end QoE, distributed allocation algorithms need to be deployed to managethe available resources efficiently. Initial results show the benefits of applyingan autonomic management overlay to HTTP Adaptive Streaming services. Infuture work, the impact of the organization of AEs on efficiency and reliabilitywill be investigated together with how inter-domain cooperation is affected bythe organization scheme.Acknowledgements. Niels Bouten is funded by a Ph.D. grant of the Agencyfor Innovation by Science and Technology (IWT). This work was partly fundedby Flamingo, a Network of Excellence project (ICT-318488) supported by theEuropean Commission under its Seventh Framework Programme.References1. CISCO. Cisco Visual Networking Index: 2011-2016. Technical Report, 2012.2. B. Briscoe. Flow rate fairness: Dismantling a religion. ACM SIGCOMM ComputerCommunications Review, 37(2):63-74, 2007.3. N. Agoulmine. Autonomic Network Management Principles: From Concepts to Ap-plications. Academic Press - Elsevier, 2011.4. G. Tesaureo, D. M. Chess, W. E. Walsh, R. Das, A. Segal, I. Whalley, J. O. Kephartand S. R. White. A Multi-Agent Systems Approach to Autonomic Computing. InProceedings of the Third International Joint Conference on Autonomous Agentsand Multiagent Systems - Volume 1, IEEE Computer Society, pp. 464-471, 2004.5. J. Famaey, S. Latre´ and J. Strassner. A Hierarchical Approach to Autonomic Net-work Management. Proceedings of the 2nd IFIP/IEEE International Workshop onManagement of the Future Internet (ManFI), pp. 225-232, 2010.6. N. Bouten, S. Latre´, W. Van De Meerssche, B. De Vleeschauwer, K. De Schepper,W. Van Leekwijck and F. De Turck. A multicast-enabled delivery framework forQoE assurance of over-the-top services in multimedia access networks, Journal ofNetwork and Systems Management, pp. 1-30, 2013.7. N. Bouten, J. Famaey, S. Latre´, R. Huysegems, B. De Vleeschauwer, W. Van Leekwi-jck and F. De Turck. QoE Optimization Through In-Network Quality Adaptationfor HTTP Adaptive Streaming, In 8th International Conference on Network andService Management (CNSM), IEEE, pp. 336-342, 2012.

QoE-centric management of multimedia networks through cooperative control loops

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QoE-centric management of multimedia networks through cooperative control loops

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