Adaptation Framework for Wireless Thin-client Computing

Mohammad Al-Turkistany

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

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 application’s screen hyper-activity Resources variability of the wireless network and the

mobile device

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

Client Resources Variability Processing speed, battery energy,

transmission power Causes of variability

OS decides to decrease processor’s frequency when battery energy reaches some threshold.

Decrease in processor’s frequency due to overheating

Switching the network card to low power mode

Proposed Approach Dynamic adaptation of thin-client system

operation to optimize performance

Adaptive system needs to discover thin-client's context (processor’s frequency, wireless bandwidth ) and use it to make tradeoff decisions that affect system performance

Thin-client Computing Model

Thin-Client

Server: Application processing & data management

Events

Display updates

Wireless Thin-client Computing Constraints

Major thin-clients systems Citrix's Winframe and Microsoft's Windows

Terminal Server and ATT’s VNC Performance limiting factors

Latency in wireless networks Limited processing power of mobile devices Low bandwidth wireless networks Mobility and resources variability

(bandwidth etc)

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

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)

AT&T’s 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 client’s processing power

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

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

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 powerServer ClientChannel

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

1

Proposed Performance Model

10

B

1

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

1

Proposed Performance Model

10

D

1

Decoding rate bps Compression ratio Avg Total Latency =

Our goal is to control the total latency

)(D10

)(

11

DB

B link bandwidth bps

Ave rectangle size bits/rectangle

Ave arrival rate rectangles/sec

1

Proposed Performance Model

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

When operating in client pull mode, then

and

Avg Total Latency=

Virtual Bandwidth of Thin-client system

total

virtual TBD

DBBW

1

B D

)(

11

DB

Virtual Bandwidth of Thin-client system

Data Rate

Compression Ratio ( )

)(D

/B

Service Rate

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

0DBWvirtual

0

0DQBWvirtual

Proposed Thin-client Adaptation Framework

Thin-client events

Wavelet decoder

Wavelet Encoder

Adaptation proxy

Rectangle updates

Server

Thin-client

FuzzyRule-base

Context Discovery

Rectangle updatesQoS

Wireless link

Application

Proposed Thin-client Adaptation Framework

Rule base

Fuzzy inference engine

Thin-client system

Ref

Fuzzification Defuzzification

Virtual bandwidth

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 (Q=1 corresponds to worst screen quality)

Error signal is used to drive a fuzzy controller that outputs the value for compression ratio 0DQBWError virtualBW

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

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

Rule-Based Fuzzy Controller

vBWError

vBWError

vBWError

vBWError

Rule-based Fuzzy Controller

1

0normal

1

0Fuzzy Set normal

1

0

normal

Bandwidth

Bandwidth’s rate of change

Compression Level

Actual Bandwidth

Actual Bandwidth’s rate of change

min

0.75

0.4

0.4

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

1

0The result

Compression Level

1

0Final output value

Compression Level

Experimental Evaluation

0

500

1000

1500

2000

2500

0 0.5 1 1.5 2

Bit Rate (bpp)

Dec

od

ing

Tim

e (m

illi

sec)

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

Experimental Evaluation

Adaptation Proxy (Linux)

Wireless Access Point

Linux Server

IPAQ PDA

CBQ-base traffic control

Compression Level Control

0

10

20

30

40

50

60

70

80

90

100

0 500 1000 1500 2000 2500 3000

Link Bandwidth (kbps)

Co

mp

ress

ion

Lev

el (

1/a)

Pentium 4, 1.8 GHz Pentium 3, 450 MHz

Standard VNC, P 3, 450 MHz

Latency=1.7 sec

Latency= 3.36

Tuning Controller’s Gain is dominating parameter:

higher value results in better latency control but with more fluctuation

aK

aKWireless Thin-client

Rule Base

bK

cK

1

VirtBW

VirtBW

Controller Tuning (Ka)

0

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100 120

Link Bandwidth (kbps)

Co

mp

ress

ion

Lev

el (

1/a)

Ka=0.004 Ka=0.001 Ka=0.01

Fluctuation Effect Chart Title

0

5

10

15

20

25

30

35

0 10 20 30 40 50

Time(x1.74 Sec)

Com

pres

sion

Lev

el (1

/a)

BW from 100 kbit/s to 30 kbit/s

Rules Reduction Effect

virtualBW

virtualBW Neg_Small Near_Zero Pos_Small

Neg_Med Pos_Med Pos_Med Pos_Small

Neg_Small Pos_Small Pos_Small Near_Zero

Near_Zero Pos_Small Near_Zero Neg_Small

Pos_Small Near_Zero Neg_Small Neg_Small

Pos_Med Neg_Small Neg_Med Neg_Med

Rules Reduction Effect

virtualBW

virtualBW Neg_Small Near_Zero Pos_Small

Neg_Med Pos_Med

Neg_Small Pos_Small Near_Zero

Near_Zero Near_Zero

Pos_Small Near_Zero Neg_Small

Pos_Med Neg_Med

Rules Reduction Effect

0

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100 120

Link Bandwidth (kbps)

Co

mp

ress

ion

Lev

el (

1/a)

15 Inference Rules 7 Inference Rules

Fuzzy Variable

0

0.2

0.4

0.6

0.8

1

1.2

-100 -50 0 50 100

Normalized BW

Mem

bers

hip

Valu

e

NM NS AZ PS PM

vBWError

Fuzzy Variable

0

0.2

0.4

0.6

0.8

1

1.2

-100 -50 0 50 100

Normalized BW

Mem

bers

hip

Valu

e

NS AZ PS

vBWError

Fuzzy Variable compLevel

0

0.2

0.4

0.6

0.8

1

1.2

-100 -50 0 50 100

compLevel

NS AZ PS PM NM

Quality Factor Effect

0

10

20

30

40

50

60

70

80

0 20 40 60 80 100 120

Link Bandwidth (kbps)

Com

pres

sion

Lev

el (1

/a)

SQF=1.0 SQF=0.9 SQF=0.8

Performance under Variable CPU Frequency

Adaptation Proxy (Linux)

Linux App Server

Wireless Access Point

IPAQ PDA

XScale Frequency Scaling Unit

Performance under Variable CPU Frequency

05

101520253035404550

300 350 400 450 500 550 600 650

Processor Frequency (MHz)

Com

pres

sion

Lev

el (1

/a)

SQF=0.6 SQF=0.7 SFQ=0.8 SFQ=0.9

Performance under Variable CPU Frequency

0

10

20

30

40

50

300 350 400 450 500 550 600 650

Processor Frequency (MHz)

Com

pres

sion

Lev

el (1

/a)

SQF=0.6 SQF=0.7 SFQ=0.8 SFQ=0.9

Controlling Total Latency

0

0.5

1

1.5

2

2.5

3

3.5

0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

Quality Factor

Late

ncy (

sec)

variable MIPS variable BW

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)

Quality-Latency Trade-offs

kQ

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

1

Virt

Virt BWBW

0DBWVirt 0D

kQ

Client’s Decoding Rate

Chart Title

0

100

200

300

400

500

600

700

300 5300 10300 15300 20300 25300

Rectangle Size (pixels)

Deco

ding

Tim

e (m

s)

compLevel=5.5

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

oldFull

rectnew compLevel

A

AcompLevel

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

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: CCCT’04, 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.