Quality of Service

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• Quality of Service
• in IN-home digital networks
• Alina Albu
• 23 October 2003

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Contents

• Presentation
• Prediction-based policy adaptation for QoS
  management in wireless networks
• Proceedings of the 4th International Workshop on
  Policies for Distributed Systems and Networks
  (POLICY 03)

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Current situation, demands

• mobile end users having Anywhere/ Anytime access
  to a wide range of computing services, with an
  emphasis on multimedia applications (video
  tele-conferencing and news-on-demand)

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Current situation,challenges

• Delivering seamless QoS based on individual users
  needs (the network must provide different levels
  of service to different categories of customers).

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Challenges

• Caused by
• wireless network component characterized by lower
  bandwidth and greater packet loss rate.
• The quality of service of the wireless component
  may change abruptly over time due to geographic
  impairments, weather conditions.
• Mobile users can move between cells characterized
  by different number of served users and hence,
  with different available bandwidth.

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Challenges

• Even if users remain static the bandwidth
  available to them may vary due to the mobility of
  the other users.
• Due to users movements, the path between the
  sender and the receiver may change gt cause a
  rerouting of data packets gt a possible variation
  of resources even in the wired part of the
  connection.

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The current solution

• The selection of a strategy for QoS management is
  usually statically fixed

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The need for a new solution

• Dynamic QoS management approaches are needed that
  can support the dynamic variation of network
  strategies.

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New proposed solution

• Framework for policy-based management that
  defines a set of components to enable policy
  rules definition, saving and enforcing.

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Policies

• Set of predefined rules that govern network
  resources, including conditions and actions with
  parameters that determine when the policies are
  to be implemented in the network

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Proposed Framework

• Multi-Agent System in which intelligent agents
  cooperate to predict future changes in the
  delivered QoS and adapt the network behavior
  according to these changes.
• Architecture composed of 3 layers of agents
• QoS prediction agents (QPA)
• QoS adaptation agents (QAA)
• Monitoring agents (MA)

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Proposed Framework
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QoS Prediction Agents (QPA)

• Utilize users information such as
• User profile
• Location
• Terminal characteristics
• to predict possible future changes that would
  affect the delivery of the required QoS level.

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QoS Prediction Agents (QPA)

• Prediction performed by 3 different agents
• User Agent (UA)
• Location Agent (LA)
• Application Agent (AA)

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User Agents (UA)

• Users actions and preferences play a role in the
  process of future predictions. Users can specify
  their QoS requirements through the use of User
  Policies (UP).
• The UA can access the UP along with information
  about the users preferences and terminal
  characteristics.
• UA is responsible for analyzing this info and
  predicting any events that would trigger a UP.
  Policy - triggered, -gt UA responsible for
  reporting the necessary actions to be taken by
  the QPA.

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Location Agent (LA)

• The LA is responsible for predicting the future
  location of the user, assessing its effects on
  the QoS, and delivering the info to the QPA.

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Application Agent (AA)

• Similar to the UA, the AA is responsible for
  analyzing and specifying the continuously
  changing QoS requirements for each running
  application.

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QoS Adaptation Agent (QAA)

• The goal of the QAA is to provide the required
  QoS based on specifications from the QPAs, taking
  into consideration current and predicted
  users/network statuses.
• The specifications are given in the form of sets
  of network level adapted policies to be applied
  to the network resources.
• The QAA assembles sets of policies at run-time,
  dependent on the sets of imposed constraints and
  goals that need to be satisfied.

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Monitoring Agents (MA)

• MA provides a real-time feed-back for the QAA. It
  is responsible for the measurement of the QoS
  exhibited by the network.
• An MA may interact with other Mas in order to
  gather the required measurements of QoS.
• Obtained measurements are then reported back to
  the QAA.
• The QAA is responsible for instructing the MA
  with the necessary parameters to be measured.

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Policy Adaptation

• Types of policy adaptation
• Adaptation carried out by dynamically changing
  the parameters of a QoS policy to specify new
  attribute values
• enabling/disabling a policy from a set of
  predefined QoS policies at run-time.
• Learn from the current system behavior and create
  new policies at run-time

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Policy Adaptation

• Policy adaptation performes by QAA -gt belongs
  into the 3rd category
• QAA architecture, scenario
• Stage setting
• Candidate actions selection
• Policy assembly
• Reassessment

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Stage setting

• QAA specifies its objectives, constraints,
  actions set, definition of success
• The objectives - determined based on info
  obtained from the QPA representing values for the
  required QoS parameters.
• The constraints set represents constraints
  imposed by the device features (memory size, CPU
  speed, available software).
• Actions set possible actions that can be used
  in the policies action part
• Each action associated an utility function
  -gtexpresses the degree of usefulness of the
  action.
• Utility function -gtfunction of QoS parameters
  (delay, jitter, throughput)

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Candidate Actions selection

• QAA selects one or more actions set which would
  best attain the specified objectives.
• QAA may negotiate with other neighboring QAAs for
  the selection of the most suitable actions to be
  taken
• Through the negotiation, actions with the highest
  utility values are selected.

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Policy assembly

• Assembly for one or more network policies given
  the actions selected in the previous step.
• Each assembled policy triggering event, a set
  of conditions, actions
• Each policy associated with a life time after
  which it should expire and be deleted.
• Once a policy is assembled it is dispatched to be
  applied at the network level.

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Reassessment

• Evaluate the degree of success of the previously
  dispatched policies.
• Based on the comparison between the QoS
  measurements (provided by MA), and the current
  definition of success, the reassessment module
  decide to modify the utility function associated
  with the actions already applied -gt either
  increase or decrease their value.