Dynamic Bandwidth Management in QoS aware IP Networks

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Dynamic Bandwidth Managementin QoS aware IP
Networks

• Thesis
• Yasir Drabu

Committee Dr. Paul Farrell Dr. Javed Khan Dr.
Hassan Peyravi (Chair)
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Presentation Overview

• INTRODUCTION
• Application, Current Network Problems, QOS,
  Traffic
• Problem Definition and Research Goal.
• BACKGROUND WORK
• Traffic Management - Active Queue Management
  (AQM), Scheduling
• Admission Control Classification, Current
  Implementation and limitations.
• DYNAMIC BANDWIDTH MANAGEMENT (DBM)
• Related Work
• Proposed Model Analysis and Algorithms
• SIMULATION
• Setup - Topology, traffic models, parameters and
  scenarios.
• RESULTS
• CONCULSION and FUTURE WORK

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Applications Changing Needs

• Conventional Apps
• Email, FTP, Telnet, etc
• Loss sensitive, High delay tolerance, jitter
  insensitive.
• The IP Network was designed for these.
• New Applications
• WWW started a new trend.
• Video, VoIP, Interactive/ Streaming Video,
  e-commerce, etc.
• Loss tolerant, delay sensitive, jitter sensitive
  to varying degrees.
• The IP Network was not designed for these.

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Current Networks - Issues

• Inherent Problems
• Different traffic requirement, similar treatment.
• Signaling packets, Real-time packets, data
  packets, individual packets within a flow, all
  treated same.
• Ill behaved traffic hurts well behaved traffic.
• Unresponsive UDP flows dominate TCP flows.
• Congestion Control limited to end hosts.
• TCP is predominant means of congestion control.
• Changing/ Upgrading Difficult.

Bottom line Need Quality of service
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Quality of Service QoS

• User QoS
• Highly perceptional, hard to quantify.
• Application QoS
• Applications change so do requirements.
• Network QoS
• Easy to quantify, well defined.
• All other QoS can be expressed in these terms.
• Metrics well defined.
• Availability
• Delay
• Delay Variation
• Throughput (Bandwidth)
• Packet Loss Rate.

Fig Core Network QoS metrics
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Network QoS Approaches

• Best Effort Enhancements (RED, WRED, ECN)
• Pros Implemented over existing infrastructure.
• Cons No QoS guarantee
• Integrated Services
• Pros High level of QoS
• Cons Not Scalable, consistency issues,
  implementation a big problem.
• Differentiated Services
• Pros Scalable, incremental implementation
• Cons QoS relative, unable to control flow
  misbehavior within aggregate.
• Constrain Based Routing
• Pros Scalable, better network utilization
• Cons Complexity (computational and space), state
  information coherence, routing stability.

Courtesy Cisco
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Todays IP QoS technology
Technology Description Engineering Aspect
RSVP DS Byte Out of Band Signaling In Band Signaling Signaling
CAR (committed access rate) Classification and policing (application, protocol , DS Byte) Policing Classification
RED, WRED Weighted Random Early Detection Service class enforcement Congestion Avoidance
WFQ, CBQ Weighted Fair Queuing Class-Based Queuing Queuing Policies Congestion Management and BW Allocation
MPLS MPLS Diffserv IPATM QoS integration Leverage Layer2
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Todays Internet Traffic

• Conventional Traffic (Exponential, Smooth)
• Exponential like Voice
• Easier to analyze
• Concrete parameters
• arrival rate, queuing delay, etc.
• Can be simulated using Poisson's Distribution
• Actual Traffic (Self Similar, Bursty)
• Heterogeneous mix of data, voice and multimedia
  application
• Difficult to characterize. In some cases not
  possible to characterize.
• Effects of multiplexing
• Makes the aggregate more self similar
• Makes the traffic more exponential
  (contradicting)
• Is there a factor, that can be used to decide
  the actual effect?
• Can be simulated using Multiple Pareto
  distributions.

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QoS Bandwidth Allocation Problem

• Fixed Bandwidth allocated for QoS guarantee
• Admission Control
• Requires a priori information about traffic
  characteristics.
• Traffic model does not accurately describe
  statistical behavior.
• User defined parameters may not accurately
  represent the actual traffic.
• LDR traffic compounds the issue.
• Some work in Call Admission Control MBACs,
  little to no work in Aggregate Flow control.
• Weighted Scheduling Weights associated based on
  admission rates.
• Bottom Line Bandwidth Allocation is inefficient.

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Problem Definition and Goal

• To design and develop a dynamic bandwidth
  management model for efficient utilization of a
  shared link in a QoS aware IP Network.
• Design Guidelines
• Optimize bandwidth Utilization
• Better Delay Vs Utilization trade off.
• Lower Loss rate
• Minimizes bad drop/mark decisions.
• Fairness
• Non responsive UDP type flows can be controlled.
• Coexistence with Best effort
• Prevent starvation of BE traffic
• Scalable and Easily deployable
• The model has to be scalable on a huge network
  and be incrementally deployed.

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Background Work

• Three major components
• Active Queue Management (AQM)
• Random Early Detect
• Exponential Weighted Measured Average (EWMA)
• Admission Control Arrivals
• Scheduling Service Allocation

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Typical QoS Aware Interface

• Usually a QoS Enabled Edge Router has
• Classifier, Admission controller, per class
  queue, scheduler.
• Optional Metering unit.

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Classification Traffic Management
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Classification Admission Control

• Extensive research on Call Admission Control.
• MBAC for more efficient bandwidth allocation.
• Packet Admission Control, limited work as
  compared to CAC.

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Dynamic Bandwidth Management

• To over come the inefficient bandwidth allocation
  we use dynamic approach.
• Measure traffic conditions online and make
  admission and allocation decisions.
• Three approaches closed loop, open loop and
  hybrid.

• Closed loop
• State information used as feedback
• Open Loop
• Prediction based on Past observations.

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DBM Proposed Architecture

• On QoS Enabled Router Interface
• Introduce a controller
• Feedback from Token Bucket - Starvation Rate
  tells us rate of packet arrival
• Feedback from Queue Average Queue Size tells us
  rate of packet departure.
• The Control has two components
• Adaptive Admission Control (AAC)
• Adaptive Class Based Queuing (ACBQ)

r3
r2
r1
r0
w0
w1
S
w2
w3
Per Class Queue
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The Controller Components

• Main Function
• Monitors Arrival Rate
• Decides Packet admission
• Design Parameters
• Bucket Size
• Token Rate
• Decision Parameter
• Bucket Starvation Rate

• Main Function
• Monitor Queue State
• Decides BW allocation
• Design Parameters
• Service Weight
• Queue Thresholds
• Decision Parameter
• Average Queue Size

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Adaptive Admission Control - Analysis

• Bucket Starvation Rate
• This indicates how many tokens a flow needs.
• Fuller buckets mean lesser requirement and vice
  versa.

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AAC Algorithm
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Adaptive Scheduler (ACBQ) - Analysis

• Each Queue has
• TH Upper Threshold
• Average Queue Size
• For Lesser Delay lower TH .
• Average Queue Size indicates w.r.t to TH tells us
  how much service the queue class requires.

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ACBQ Algorithm
The parameters
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Simulation Setup
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Simulation Scenarios
No. Scenario Name Traffic Load AC Scheduling
1 Base Line Exp 0.3-0.95 None Static CBQ
2 Static Allocation Exp 0.3-0.95 CAR Static CBQ
3 Partial Adaptive Exp 0.3-0.95 AAC Static CBQ
4 Fully Adaptive Exp 0.3-0.95 AAC Adaptive CBQ
5 Base Line Pareto 0.3-0.95 None Static CBQ
6 Static Allocation Pareto 0.3-0.95 Car Static CBQ
7 Partial Adaptive Pareto 0.3-0.95 AAC Static CBQ
8 Fully Adaptive Pareto 0.3-0.95 AAC Adaptive CBQ
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Data Collections

• Each simulation scenario was run
• 3 times with different seeds
• For a duration of 70 seconds
• Data was collected between 10-70 seconds,
  assuming the simulator too 10 seconds to reach
  steady state.
• The instantaneous values per simulation scenario
  were averaged over the duration of the
  simulation.
• Then again the averages were averaged for the
  three runs.

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Delay Performance Exponential
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Delay Performance Pareto (LRD)
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Jitter Performance - Exponential
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Jitter Performance - Pareto
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Loss Rate Exp and LRD
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Offered Load Vs Throughput
Average 5 BW saving At 80 Load 14.5 BW
saving
Average 11 BW saving At 80 Load 30 BW saving
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Simulation Results Summary

• Higher Efficiency
• Lower Packet Drop - Prevented bad admission
  decisions.
• Increased Throughput
• Bursty traffic showed better gain.
• Tradeoffs for Higher Throughput
• Increased Delay
• Increased Computation to O(N), Where N is the
  number of QoS classes. N is always small.

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Conclusion

• CONTRIBUTION
• New approach to Bandwidth Management
• Our approach performs better than commercial
  implementing in terms of bandwidth utilization.
• FUTURE WORK
• Define an accurate relation between the QOS
  metrics and Control Parameter.
• What is the best time scale of operation?
• How does it behave with RED and its Variants?
• End to End QOS is still not addressed.

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Thank you.
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View On Fundamental Limitations

• Application Utility as a function of Network
  Performance
• This is undefined, and needs to be well defined.
• QOS tries to provide better than BE
• How about worse than BE, Scavenger service,
  nice in UNIX process resource sharing.
• Elevated services, non-elevated services
  (Internet2).
• Deployment is a bigger challenge that most people
  think.

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Opnet
Router under study
IP Node Model The router implements the complete
networking stack. We are interested in the IP
node. We modify this node to allow us to probe
some of the statistics that we collect in
relation to our proposed model. We also modify
the underlying process models by implementing our
adaptive algorithms.
2 Ethernet and 8 Slip Interface Node Model
Router Node Model
IP Process Mode
ip_dispatch Process Model
IP Child Process Model The child process model,
ip_output_iface implements all the Scheduling and
AQM algorithms like Class Based Queuing, RED,
WRED, etc.
IP Process Model The IP process model has
several child process models like ip_icmp, ip_vpn
etc. Each child process model implements a
specific feature of IP.
Child Process