Power-Aware Data Management for Small Devices

Sami Rollins and Kevin C. Almeroth
Department of Computer Science       
University of California            
Santa Barbara, California 93016-5110
{srollins, almeroth}@cs.ucsb.edu   

Dejan Milojicic
Hewlett Packard Laboratories
Palo Alto, CA
dejan@exch.hpl.hp.com

Kiran Nagaraja
Rutgers University
Knagaraj@cs.rutgers.edu

Pervasive computing devices such as Personal Digital Assistants (PDAs) and 
laptop computers are becoming increasingly ubiquitous.   The future promises 
even more advanced devices such as digital watches, jewelry, and even 
clothing.  However, as pervasive devices become more widely used for more 
advanced applications, their resource limitations are becoming more apparent. 
In this work, we focus on data management and power limitations.  We 
investigate the benefit of using power-aware schemes to automatically manage 
content across a collection of devices.  We monitor the available energy 
supply on each device and migrate content from devices that are in danger of 
dying to prolong data availability.  In our simulated environment, we have 
found that, using intelligent techniques for data management can increase the 
amount of time a collection of devices remains usable by over 2 times.  
Furthermore, our techniques can perform autonomously, independent of user 
intervention.