Connection Admission Control Schemes for Self-Similar Traffic

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Connection Admission Control Schemes for
Self-Similar Traffic

• Yanping Wang
• Carey Williamson
• University of Saskatchewan

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Connection Admission Control

• Features
• important traffic management mechanism
• improve network utilization (statistical
  multiplexing)
• meet QOS requirements of all existing connections
• Difficulties
• diverse traffic characteristics
• various QOS requirements
• bursty, self-similar traffic (i.e., LRD)

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Self-SimilarityPrevalence of Bursts Over Many
Time Scales
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Properties of Self-Similar Traffic

• Autocorrelation Function

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(continued)

• Variance-Time Plot

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(continued)

• R/S Pox Diagram

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Research Objectives

• Self-similarity
• affects queuing behavior of aggregate traffic
• has impact on network-engineering problems
• admission control, rate control
• Research objectives
• CAC performance when presented with self-similar
  traffic
• identify the impact of different parameters

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CAC Algorithms

• PCR CAC
• QOS guaranteed
• network resource wasted
• SCR CAC
• high network utilization
• poor QOS performance
• AVG CAC
• allocates extra bandwidth to handle the
  burstiness in the traffic

• GCAC
• specified in P-NNI for efficient path selection
• exploit multiplexing gains
• Norros CAC
• based on FBM model
• traffic characteristics captured by m, a, and H
• exploit multiplexing gains

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Experimental Methodology (1)

• Network Topology
• Fractional-ARIMA Based Model
• Hoskings model 3 transformations
• LRD and SRD features adjustable
• marginal distribution adjustable

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Experimental Methodology (2)

• Simulation Configuration
• ATM-TN simulator
• factors (m, a, H, b, C and ?)
• metrics (CA, LU and CLR)
• baseline configuration and the structured
  simulations
• Simulation Validation
• warmup phase
• accuracy of the results

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Simulation Results (1)Baseline Configuration

• Call Acceptance Performance

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Simulation Results (2)Baseline Configuration

• Link Utilization

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Simulation Results (3)Baseline Configuration

• CLR Performance

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Simulation Results (4)Baseline Configuration

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Simulation Results (5)Parameter Effects

• Source Granularity

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Simulation Results (6)Parameter Effects

• Source Variability

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Conclusions (1) CAC Performance

• Impact of the Parameters
• source granularity, source variability
• long-range correlation structure
• buffer size, target CLR
• link capacity
• mixing traffic sources
• Norros CAC and AVG CAC are promising
• None of the CAC algorithms provides satisfying
  overall performance in all the scenarios

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Conclusions (2) Impact of Self-Similarity

• Strong impact on network performance
• especially when link capacity is small
• statistical multiplexing gains should be
  exploited
• achievable link utilization increases as link
  capacity increases
• ineffectiveness of buffering

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Future Work

• Multifractal property
• multifractal vs. monofractal traffic
• Estimated traffic parameters
• accurate vs. poor traffic parameters