Mobile Code, Adaptive Mobile Applications, and Network Architectures

Thomas Kunz

Systems and Computer Engineering

Carleton University

http://kunz-pc.sce.carleton.ca/

tkunz@sce.carleton.ca

Outline

Background: Motivation and Related Work

Mobile Code Toolkit

Experimental Results

Scalability: Performance Models (LQM)

Conclusions and Future Research

Related Work

Typically, related work focuses on bandwidth variation

Other resources are also constrained (memory, power, CPU capabilities) and may vary over time

Majority of solutions: introduce proxy between client (application executing on mobile device) and server, proxy compresses and filters data stream

sometimes, degree of compression depends on available bandwidth

user mobility often not addressed/supported

Other solutions:

view mobile purely as User Interface (WWW browsers, Teleporting, ....)

partition client application between mobile and proxy

partition directed by application (Rover, MaROS, ....)

partition transparent to application (our own focus)

Mobile Code/Agents for Adaptive Mobile Applications

Mobile Code Toolkit: Use Proxy Objects

Mobile Code Toolkit: Components

Extend JVM on client and proxy server

Monitor

Code Storage

Object References and Profiling

Object Server

Remote Method Invocation Protocol

Dynamic Decision

Communication Control Layer

Extend garbage collection to deal with remote references

Provide abstractions to notify runtime system about environment and changes in environment (bandwidth, power, …), based on events and event notification

Experimental Results

Implemented MP3 player, encapsulated decoder as mobile agent, to execute locally or remotely

Various performance experiments, study impact of

Available Bandwidth (19.2 kbps – 2 Mbps)

Relative CPU speed (Mobile CPU: Proxy CPU)

1:4 for laptop as client device

1:116 for Windows CE palmtop as client device

Results:

for high bandwidth and slow client, offloading decoding “agent” to proxy can speed up application performance by a factor of 20 and reduce power consumption by 95%

For faster client devices, independent of bandwidth, no benefit (actually worse performance) when decoding sound samples in access network

Scalability: LQM

Scalability (cont)

Many parameters deduced from traces collected from live cellular network (Bell Mobility’s MicroBrowser)

Study system capacity under changing load conditions, vary computational load off-loaded to proxy

Results:

Proxy makes slow handsets/clients faster

System capacity decreases slightly

For system under study, application server more likely to become bottleneck than proxy

è single proxy server can support current client population

Caveat: results specific for application studied, may not generalize for more demanding multi-medial 3^rd-generation mobile applications

Conclusions and Future Work

Conclusions:

Implemented Mobile Code Toolkit with small footprint for Windows CE devices

Collected experimental evidence to verify validity of our approach

Developed a general approach to study scalability

Future Work:

Improve toolkit: better RMI performance, automatic generation of proxy objects, port to Palm OS

Study additional applications and more dynamic scenarios

How to predict scalability when agent’s functionality is not similar to a “server” (i.e., decode next frame and return sampled sound)

Interaction between application-aware (MP3 player reduces sound quality, sampling rate, …. ) and application-transparent (runtime system migrates decode agent from handset to proxy) adaptation


	Thomas Kunz
	Systems and Computer Engineering
	Carleton University
	http://kunz-pc.sce.carleton.ca/
	tkunz@sce.carleton.ca


	Background: Motivation and Related Work
	Mobile Code Toolkit
	Experimental Results
	Scalability: Performance Models (LQM)
	Conclusions and Future Research


Typically, related work focuses on bandwidth variation
Other resources are also constrained (memory, power, CPU capabilities) and may vary over time
Majority of solutions: introduce proxy between client (application executing on mobile device) and server, proxy compresses and filters data stream
	sometimes, degree of compression depends on available bandwidth
	user mobility often not addressed/supported
Other solutions:
	view mobile purely as User Interface (WWW browsers, Teleporting, ....)
	partition client application between mobile and proxy
		partition directed by application (Rover, MaROS, ....)
		partition transparent to application (our own focus)


	Extend JVM on client and proxy server
		Monitor
		Code Storage
		Object References and Profiling
		Object Server
		Remote Method Invocation Protocol
		Dynamic Decision
		Communication Control Layer
	Extend garbage collection to deal with remote references
	Provide abstractions to notify runtime system about environment and changes in environment (bandwidth, power, …), based on events and event notification


Implemented MP3 player, encapsulated decoder as mobile agent, to execute locally or remotely
Various performance experiments, study impact of
	Available Bandwidth (19.2 kbps – 2 Mbps)
	Relative CPU speed (Mobile CPU: Proxy CPU)
		1:4 for laptop as client device
		1:116 for Windows CE palmtop as client device
Results:
	for high bandwidth and slow client, offloading decoding “agent” to proxy can speed up application performance by a factor of 20 and reduce power consumption by 95%
	For faster client devices, independent of bandwidth, no benefit (actually worse performance) when decoding sound samples in access network


	Many parameters deduced from traces collected from live cellular network (Bell Mobility’s MicroBrowser)
	Study system capacity under changing load conditions, vary computational load off-loaded to proxy
	Results:
		Proxy makes slow handsets/clients faster
		System capacity decreases slightly
		For system under study, application server more likely to become bottleneck than proxy
		è single proxy server can support current client population
	Caveat: results specific for application studied, may not generalize for more demanding multi-medial 3^rd-generation mobile applications


	Conclusions:
		Implemented Mobile Code Toolkit with small footprint for Windows CE devices
		Collected experimental evidence to verify validity of our approach
		Developed a general approach to study scalability
	Future Work:
		Improve toolkit: better RMI performance, automatic generation of proxy objects, port to Palm OS
		Study additional applications and more dynamic scenarios
		How to predict scalability when agent’s functionality is not similar to a “server” (i.e., decode next frame and return sampled sound)
		Interaction between application-aware (MP3 player reduces sound quality, sampling rate, …. ) and application-transparent (runtime system migrates decode agent from handset to proxy) adaptation