A Multilevel Introspective Dynamic Optimization System For Holistic Power-Aware Computing

Abstract

Power consumption is rapidly becoming the dominant limiting factor for 
further improvements in computer design. Curiously, this applies both
at the "high end" of workstations and servers and the "low end" of
handheld  devices and embedded computers. At the high-end, the
challenge lies in  dealing with exponentially growing power
densities. At the low-end, there  is a demand to make mobile devices
more powerful and longer lasting, but battery technology is not
improving at the same 
rate that power consumption is rising. Traditional  power-management
research is fragmented; techniques are being developed  at specific
levels, without fully exploring their synergy with other  levels. 
Most software techniques target either operating  systems or
compilers but do not explore the interaction between the two
layers. These techniques also have not fully explored the potential
of  virtual machines for power management. 

In contrast, we are developing
a system  that integrates information from multiple levels of software
and hardware, connecting these levels through a  communication
channel. At the heart of this 
system are a virtual machine that compiles and dynamically profiles
code, and an optimizer that  reoptimizes
all code, including that of applications and the virtual machine itself.
We believe this  introspective, holistic approach
enables more informed power-management decisions.