Prudence Wong
Liverpool

Energy efficient job scheduling with speed scaling and sleep management

Energy usage has become a major issue in the design of microprocessors,
especially for battery-operated devices. Many modern processors support
dynamic speed scaling to reduce energy usage. The speed scaling model
assumes that a processor, when running at speed s, consumes energy at
the rate of s^\alpha, where \alpha is typically 2 or 3.

In older days when speed scaling was not available, energy reduction was
mainly achieved by allowing a processor to enter a low-power sleep
state, yet waking up requires extra energy. It is natural to study job
scheduling on a processor that allows both sleep state and speed
scaling. In the awake state, a processor running at speed s>0 consumes
energy at the rate s^\alpha + \sigma , where \sigma > 0 is the static
power and s^\alpha is the dynamic power. In this case, job scheduling
involves two components: a sleep management algorithm to determine when
to work or sleep, and a speed scaling algorithm to determine which job
to run and at what speed to run.  Adding a sleep state changes the
nature of speed scaling. Without sleep state, running a job slower is a
natural way to save energy. With sleep state, one can also save energy
by working faster to allow a longer sleep period. It is not trivial to
strike a balance.

In this talk, we will discuss some scheduling results involving both
speed scaling and sleep management.