Autonomic communication QoS - fixed and/or next generation networks

1. Autonomic communication QoS- fixed and/or next generation networks Kaisa Kettunen Helsinki University of Technology / S-38.4030 Seminar 26.-29.5.2006

2. What is Autonomic Communication (AC)? Current trend in networking: • High amount of new services and applications • Increasing network sizes with increased management costs • Deployment and introduction of new networks (e.g. Next Generation Networks (NGN)) • High complexity of network infrastructures • Growing demands (e.g. security, quality, resilience, reliability etc.) There is a pressure towards converged network architecture (heterogeneity) with open interfaces and standard protocols. Autonomic communication – the vision: ”Towards self-organizing, self-managing and context-aware autonomous network” By moving intelligence to the network, create an adaptive and aware environment to achieve a common purpose by self-organization of network nodes and consequently reduce network management complexity and human intervention. Helsinki University of Technology/S-38.4030

3. AC vs Quality of Service (QoS)? Quality of Service (QoS) is the level of performance that is expected by a user in a network. It is defined by the network performance as well as performance of the used service components and can be characterized by for example the following: • Bandwidth (bps) • Delay (ms) • Loss rate (%) • Jitter • Noise/Loudness level End-to-end (e2) QoS is the overall performance of all involved networks and service components. Assuring wanted e2e QoS with ever increasing network interworking requires dynamic QoS management, which makes use of the existing network synergies and distributes the needed tasks in order to reach the wanted outcome.  Autonomic Communication enables this! Helsinki University of Technology/S-38.4030

4. Example of Autonomic (QoS) Provisioning Framework InterDom Composition Adaptation User Knowledge Domain Discovery Inter-Domain Monitors Cross-Domain Provisioning Cross-Domain Contracting IntraDom IntraDom IntraDom Intra-Domain Provisioning Intra-Domain Provisioning Intra-Domain Provisioning Knowledge Knowledge Knowledge Resource Allocation Intra-Domain Monitors Resource Allocation Resource Allocation Intra-Domain Monitors Intra-Domain Monitors ┤├ ┤├ ┤├ QoS Monitor QoS Monitor QoS Monitor Source: ”QoS-Aware service composition and adaptation in Autonomic Communication”, J. Xiao & R. Boutaba Helsinki University of Technology/S-38.4030

5. Domain Discovery Inter-Domain Monitors Composition Adaptation Cross-Domain Provisioning Cross-Domain Contracting Autonomic component key functions Each domain (IntraDom) has an own QoS provision mechanism and offers a set of service classes. The three functions of an autonomic component are applied to the inter-domain component (InterDom): Sensor Used to observe and to report data about aspects of the system, e.g. reachability of nodes or monitoring aggregate QoS service condition Actuator Used to change behaviour, e.g. establishment of QoS contracts with each domain or obtaining a QoS-assured path segment at specific border points Analyzer/planner Used to ensure required e2e QoS based on cognitive computations Composition: What networks or service providers to involve? Adaptation: Dynamic adjustment of network/service composition based on communication path monitoring and re-evaluation to ensure wanted outcome Helsinki University of Technology/S-38.4030

6. Requirements for adaptation and composition algorithms The performance of an adaptation and composition algorithm complies ideally to the following requirements: • Reasonable processing time enabling fast response • Minimized cost and degree of disruption during processing • High probability of finding a feasible path with near optimal costs • No excessive communication overhead introduced Note that the efficiency of the algorithm also depends on the Sensor and Actuator functions. On the following slides, examples of published methods to improve Quality of Service with the principles of Autonomic Communication in different kind of networks are presented. Helsinki University of Technology/S-38.4030

7. Cognitive Packet Network (CPN) routing CPN is a distributed protocol for packet networks, which provides dynamic routing based on sensing and monitoring and which is driven by a QoS goal defined by the user or by the network itself. Smart / Cognitive Packets (SP)are used to discover routes and collect measurements (no payload). Acknowledgement Packets (ACK)are generated at destination as response to received SPs to carry back the original packet route and the measurement data along a reverse route established by removing any sequences which begin and end in the same node. Example: Original route: <a, b, c, d, a, f, g, h, c, l, m>Reversed route: <m, l, c, b, a> Dumb Packets (DP)carry payload and use the source routing. Mailboxes (MB)in nodes store the QoS information carried by ACKs per QoS class and destination. The information is organized as a Least-Recently-Used (LRU) stack (new info on top). Reinforcement Learning (RL) is a Random Neural Network based algorithm running at each router. It uses the observed outcome (SP success/failure) of a previous decision to ”reward” or ”punish” (weight factor increase/decrease) the previous (link=neuron) choice based on the set goal. Helsinki University of Technology/S-38.4030

8. CPN Node 201 CPN Node 202 CPN Node 203 CPN Node 204 CPN Node 205 CPN Node 206 Source CPN Node 208 CPN Node 209 CPN Node 210 CPN Node 211 CPN Node 212 CPN Node 214 CPN Node 215 CPN Node 216 CPN Node 217 CPN Node 218 CPN Node 219 Destination Experiments on CPN routing Several QoS experiments with a number of nodes have been published with a setup similar to the one below. Here, each pair of nodes is connected by P2P 10Mbit Ethernet links. The system is fed with UDP packets with a constant bit rate and inserted random background traffic on the links. Conclusions: • CPN can approximately find shortest path as well as offer more complex QoS criteria (e.g. delay) for routing. Using more complex criteria than the shortest number of hops can provide better overall quality of service. • A comparetively small fraction of SPs and ACKs of total user traffic is needed to serve a user Goal and a small number of SPs can suffice to initially set up paths. • Usage of Genetic Algorithms for finding new routes improves QoS under light network traffic but not under high traffic conditions (where it seems to slow down the adaptation logic). Helsinki University of Technology/S-38.4030

9. Autonomic distribution in Peer-to-peer (P2P) networks • Peer-to-Peer networking is based on collaboration, intercommunication and resource exchanging among individual nodes. It provides a highly dynamic environment with unpredictable quality of service. • In absence of a centralized resource management, autonomic distribution based on regulation and rules can provide better QoS by self-organization of a peer community in terms of e.g. • availability, capacity and memory • load balancing • reduction of redundant data storing ”Contribute while consuming” • Example: SelfService • Protocol based on trial&error, local memory and broadcast requests for sharing application modules between individual machines based on simple reasoning of every peer. • This again requires improved information collection mechanism • Two common ways: explicit probing, e.g. hearbeat messages (-) or multicasting (+) Helsinki University of Technology/S-38.4030

10. New protocols for improved P2P QoS • PeerWindow is an information collection protocol, with which each node can collect a large amount (N/2level) of information pointers to other nodes at a low cost. • Self-determined level of a node defines its capacity in relation to other nodes and the size of the node’s peer list containing pointers to other nodes. In an k-level node, the list should contain pointers to all nodes whose nodeId’s first k-bits (eigenstring) are the same with local one. • All the nodes, whose peer list contains a pointer to a given node, form a set - node’s audience set, which must be informed at changes. • Autonomic group communication protocol can be used to guarantee a QoS required by applications also if the QoS supported by the underlying network changes • Protocol modules are realized as autonomous agents, which change classes, i.e. ways to implement a protocol function, based on monitored QoS Helsinki University of Technology/S-38.4030

11. QoS improvements for Wireless networks In wireless networks performance is measured on e.g. power, range and data range. The “chaotic” deployment may be improved with Autonomic Communication in terms of • Automated power control and rate adaptation to minimize interference between neighbouring access points (AP) by reducing power to the minimum level which allows reaching receiver at maximum transmission rate • Load management and effective coverage • Adaptive traffic scheduling mechanisms used in case of network changes or according to application needs to save node energy and to avoid overload as well as compulsive behavior • Nodes classified to zones and further grouped as virtual sectors. An intelligent migration agent monitoring the network activity can order a change between interchangeable scheduling tables (e.g. X- or V-scheduling) for an entire sector or zones in a sector. For ad hoc networks formed by passerby peers with no central control, Peer-To-Peer Wireless Network Confederation (P2PWNC) has been developed • Scheme where a set of administrative domains provide wireless service, e.g. Internet access, to each other’s broadband Wi-Fi users. This is done based on an algorithm that detects non-simultaneous multi-way P2P exchanges. Helsinki University of Technology/S-38.4030

12. QoS in Next Generation Networks (NGN) • For next generation networks, a more broader view beyond a specific environment is needed to establish a working reliable co-operation. • The concept of Ambient Networks (AN) aims to improve interworking of different environments for e.g. self-organized establishment of QoS • ANs agree to follow composition agreements, e.g. to maintain QoS for a mobile video conference in a moving train, with means of topic-related control and management tasks provided by a Functional Area (FA). Control functions may be distributed and a procedure is independent from the nature of the entities involved. • In a SIP-based network, autonomic communication can be used for fault recovery/avoidance as well as dynamic load balancing • Through monitoring events over Service Bus, node loads can be supervized and a ”Recovery Agent” logic may allocate or restart services from suitable servers. Helsinki University of Technology/S-38.4030

13. Conclusions • Controlling end-2-end QoS in current static and manually configured network environments is becoming increasingly complex • Principles of autonomic communication provide means to improve QoS in existing networks and enable maintaining it in the next generation solutions • There is a variety of dynamic QoS related protocols, algorithms and schemes which have been published and evaluated successfully so far and more is yet to come… Helsinki University of Technology/S-38.4030

14. References • QoS-Aware service composition and adaptation in Autonomic Communication, J.Xiao and R.Boutaba, IEEE JSAC, Vol. 23, No. 12, Dec 2005 • QoS and Routing in the Cognitive Packet Network, E.Gelenbe and P.Liu, IEEE, WoWMoM’05 • Autonomous Smart Routing for Network QoS, E.Gelenbe, M.Gellman, R.Lent, P.Liu and P.Su, IEEE, ICAC’04 • Self-Aware Networks and QoS, E.Gelenbe, R.Lent and A.Nunez, Proceedings of the IEEE, Vol. 92, No. 9, Sep 2004 • ”Self-Service” – A Theoretical Protocol for Autonomic Distribution of Services in P2P Communities, F. Saffre and H. R. Blok, IEEE, ICAC ’04 • PeerWindow: An Efficient, Heterogeneous, and Autonomic Node Collection Protocol, J.Hu, M.Li, H.Dong and W.Zheng, IEEE, ICPP ’5 • An Autonomic Group Communication, T.Enokido and M.Takizawa • Self-Management in Chaotic Wireless Deployments, A.Akella, G.Judd, S.Seshan and P.Steenkiste, MobiCom ’05 • A Self-Managed Scheme for Free Citywide Wi-Fi, E.C.Efstathiou and G.C.Polyzos, IEEE, WoWMoM’05 • Adaptive Scheduling in Wireless Sensor Networks, A.G.Ruzzelli, M.J.O’Grady, G.M.P O’Hare and R.Tyran, Department of Computer Science, University College Dublin • A Framework for Self-organized Network Composition, C.Kappler, P.Mendes, C.Prehofer, P.Pöyhönen and D.Zhou, • Towards service awareness and autonomic features in a SIP-enabled network, G.Delaire, L.W.Goix and G.Valetto, Telecom Italia lab Helsinki University of Technology/S-38.4030