Adaptation Framework for Wireless Thin-client Computing

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Adaptation Framework for Wireless Thin-client
Computing
Mohammad Al-Turkistany
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Presentation Outline

• Problem Definition
• Wireless Thin-client Computing Constraints
• Related Work
• VNC Thin-client system
• Thin-client Performance Model
• Proposed Approach Adaptive Thin-Clients
• Experimental Evaluation
• Conclusion
• Publications

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Problem Definition

• Thin-client computing is attractive model for
  mobile computing
• Outsource processing and storage to network
  servers
• Off-device management maintenance of
  applications
• Constraints
• Thin-clients may generate excessive traffic when
  sending screen updates over a wireless network
• Sensitive to applications screen hyper-activity
• Resources variability of the wireless network and
  the mobile device

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Wireless Network Variability

• Service parameters bandwidth, latency and error
  rate are location dependent
• Causes of resource variability
• Wireless noise and interference multi-path
  fading, impulse noise, etc.
• Surge in the number of users at airport terminal
  leads to lower bandwidth per user
• Vertical horizontal handoff between different
  wireless technologies

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Client Resources Variability

• Processing speed, battery energy, transmission
  power
• Causes of variability
• OS decides to decrease processors frequency when
  battery energy reaches some threshold.
• Decrease in processors frequency due to
  overheating
• Switching the network card to low power mode

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Proposed Approach

• Dynamic adaptation of thin-client system
  operation to optimize performance
• Adaptive system needs to discover thin-client's
  context (processors frequency, wireless
  bandwidth ) and use it to make tradeoff decisions
  that affect system performance

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Thin-client Computing Model
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Wireless Thin-client Computing Constraints

• Major thin-clients systems
• Citrix's Winframe and Microsoft's Windows
  Terminal Server and ATTs VNC
• Performance limiting factors
• Latency in wireless networks
• Limited processing power of mobile devices
• Low bandwidth wireless networks
• Mobility and resources variability (bandwidth etc)

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Related Work

• NCL of Columbia U Optimizing Bandwidth usage by
  compressing screen updates may degrade the
  overall performance in high-latency networks
• Server-push eager screen update policy has best
  performance for multimedia (video) applications
• Wireless thin-client web browsing is superior to
  local fat-client browsing (under high packet loss
  rates)
• TCP protocol overheads and latencies for setting
  up and maintaining connections under packet loss
  conditions

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Related Work

• Mobile Computing Lab at UF Thin-Clients
  optimization for wireless active-media
  applications
• Introduced the concept of scalable application
  localization at the thin-client
• Transfer some of the application processing tasks
  based on the quality of network connectivity
• Localization of keyboard and mouse events
• Localization of active-web objects (animated gif
  image)

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ATTs VNC Thin-client

• Encoding requirements for active and media-rich
  applications (with frequent display updates)
• Low complexity decoding
• High compression level to conserve bandwidth
• Performance bottleneck
• VNC performance depends on the quality of
  underlying wireless connection (i.e. bandwidth
  latency) and clients processing power

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VNC Thin-client Limitations

• Excessive use of the wireless bandwidth
• Poor compression of complex-graphic screen
  updates (variation of RLE encoding)
• Variability of wireless connection quality that
  causes variable available bandwidth
• Noise (S/N ratio)
• Multi-path fading
• of users in cell area
• Power level position relative to access point

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Adaptive Thin-client Computing

• It is critical to dynamically adapt (at
  application level) thin-client performance to the
  variability of available resources
• Adapt by changing the encoding type or
  compression level of screen updates
• Employ scalable compression level control by
  using lossy Wavelet-based encoding

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Proposed Performance Model

• VNC performance parameters
• bandwidth, client processing speed, and server
  processing speed
• We model these using three cascading queues using
  M/M/1 model (incremental screen updates)
• Assumes very high server processing power

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Proposed Performance Model
B Link Bandwidth bps Avg
Rectangle Size bits/rectangle Avg
Arrival Rate rectangles/sec
Compression Ratio Transmission Latency Avg
time period that starts when screen rectangle
enters the queue and ends when the server
finishes processing the rectangle

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Proposed Performance Model
B Link Bandwidth bps Avg
Rectangle Size bits/rectangle Avg
Arrival Rate rectangles/sec
Compression Ratio Decoding Latency Avg time
period that starts when screen rectangle enters
the queue and ends when the server finishes
processing the rectangle

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Proposed Performance Model

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Proposed Performance Model

• In general, D( , S, T, algorithm)
• S is RFB rectangle size
• T represents the information content of RFB
  rectangle
• Decoding rate function is usually non-linear and
  not easy to model mathematically
• Fuzzy control is used to control the system
  latency
• Used to control complex non-linear processes,
  when there is no simple mathematical model
• Relies on experimental knowledge to design the
  controller

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Virtual Bandwidth of Thin-client system

• When operating in client pull mode, then
• and
• Avg Total Latency

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Virtual Bandwidth of Thin-client system

Service Rate
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Update Quality-Latency Trade-off

• The maximum virtual bandwidth achievable
  (best-case latency) is and this
  happens when
• Set the target virtual bandwidth according to
  quality of screen update requirement
• Dynamic adaptation is achieved by controlling
  at the server (or proxy) side using fuzzy
  controller

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Proposed Thin-client Adaptation Framework
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Proposed Thin-client Adaptation Framework
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Proposed Thin-client Adaptation Framework

• Goal Minimize the average latency observed by
  the user by controlling the compression ratio
• Trade-off between total latency and screen
  updates quality (Q1 corresponds to worst screen
  quality)
• Error signal is used to drive a fuzzy controller
  that outputs the value for compression ratio

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Proposed Thin-client Adaptation Framework

• Avoids direct measurement of available wireless
  bandwidth (B) and the processing speed of the
  thin-client device
• Approximate estimate of virtual bandwidth
  measure the time period between two successive,
  wavelet-encoded, full screen rectangles sent to
  thin-client

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Rule-Based Fuzzy Controller

• Approximate expert knowledge is used instead of
  differential equations to describe system
  dynamics
• Rule-based inference system
• If is normal and is normal
  then 1/ shall be normal
• If is low and is low then
  1/ shall be high
• Fuzzy rules fires in parallel to contribute to
  the control action

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Rule-based Fuzzy Controller

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Rule-based Fuzzy Controller

• Different rules results overlap to yield the
  overall output. The result of the fuzzy
  controller is a fuzzy set.
• To get one representative crisp value as the
  output, we find the center of gravity of the
  fuzzy set

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Experimental Evaluation
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Experimental Evaluation

• Fuzzy controller adapts to variations in link
  bandwidth by controlling compression level to
  maintain target total latency
• For fast processor, the fuzzy controller has to
  compress more to keep up with the fast decoding
  rate and prevent data transmission bottleneck

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Experimental Evaluation
CBQ-base traffic control
Adaptation Proxy (Linux)
Wireless Access Point
IPAQ PDA
Linux Server
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Compression Level Control
Latency1.7 sec
Latency 3.36
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Tuning Controllers Gain

• is dominating parameter
• higher value results in better latency control
  but with more fluctuation

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Controller Tuning (Ka)
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Fluctuation Effect
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Rules Reduction Effect
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Rules Reduction Effect
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Rules Reduction Effect
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Fuzzy Variable
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Fuzzy Variable
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Fuzzy Variable compLevel
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Quality Factor Effect
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Performance under Variable CPU Frequency
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Performance under Variable CPU Frequency
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Performance under Variable CPU Frequency
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Controlling Total Latency
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Quality-Latency Trade-offs

• The ratio is determined by activity
  characteristics of each application. It
  estimates average screen update traffic generated
  by the application
• Assign higher Q values for active applications (k
  is distortion tolerance)

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Quality-Latency Trade-offs

• Tradeoff between latency and screen rectangles
  quality (distortion)
• Higher value of (Q) results in lower total
  latency at the cost of increased distortion
• For stable thin-client system
• Since then

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Clients Decoding Rate
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Optimizing Small Screen Areas

• For small size screen rectangles, high
  compression level may be an overkill
• Improvement method
• Allows the controller to adapt to variable-size
  screen updates

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Conclusion

• We propose a proxy-based adaptation framework for
  wireless thin-client systems
• Dynamically adapts the performance of wireless
  thin-client
• Context information is used by fuzzy rule-based
  inference engine to optimize wireless resources
  usage by trading off among different quality of
  service parameters
• Uses highly scalable wavelet-based image coding
  technique to provide high scalability of quality
  of service
• Shields the user from the ill effects of abrupt
  variability of wireless and mobile device
  resources

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Publications

• M. Al-Turkistany, A. Helal, Fuzzy Rule-based
  Adaptation Framework for Wireless Thin-Clients,
  Proceedings of International Conference on
  Computing, Communications and Control
  Technologies CCCT04, August, 2004, Austin,
  Texas.
• M. Al-Turkistany, A. Helal, Modelling and
  Performance of Adaptive Wireless Thin-client
  Computing, to be submitted to IEEE Transactions
  on Mobile Computing.