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.

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!

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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
Intra-Domain Monitors
Resource Allocation
Intra-Domain Monitors
Resource Allocation



QoS Monitor
QoS Monitor
QoS Monitor
Source QoS-Aware service composition and
adaptation in Autonomic Communication, J. Xiao
R. Boutaba
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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

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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.

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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 lta, b, c, d, a, f, g, h, c, l,
  mgtReversed route ltm, l, c, b, agt
• 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
  (linkneuron) choice based on the set goal.

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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).

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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 trialerror, 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 ()

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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 nodes peer list containing pointers to
  other nodes. In an k-level node, the list should
  contain pointers to all nodes whose nodeIds
  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 - nodes 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

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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 others broadband Wi-Fi users. This is
  done based on an algorithm that detects
  non-simultaneous multi-way P2P exchanges.

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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.

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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

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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, WoWMoM05
• Autonomous Smart Routing for Network QoS,
  E.Gelenbe, M.Gellman, R.Lent, P.Liu and P.Su,
  IEEE, ICAC04
• 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, WoWMoM05
• Adaptive Scheduling in Wireless Sensor Networks,
  A.G.Ruzzelli, M.J.OGrady, G.M.P OHare 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