Multiprocessor platforms play important roles in modern computing
systems, and appear in various applications, ranging from energy-limited
hand-held devices to large data centers. As the performance requirements
increase, energy-consumption in these systems also increases
significantly. Dynamic Voltage and Frequency Scaling (DVFS), which allows
processors to dynamically adjust the supply voltage and the clock
frequency to operate on different power/energy levels, is considered an
effective way to achieve the goal of energy-saving. This book surveys
existing works that have been on energy-aware task scheduling on DVFS
multiprocessor platforms.
Energy-aware scheduling problems are intrinsically optimization
problems, the formulations of which greatly depend on the platform and
task models under consideration. Thus, Energy-aware Scheduling on
Multiprocessor Platforms covers current research on this topic and
classifies existing works according to two key standards, namely,
homogeneity/heterogeneity of multi-processor platforms and the task
types considered. Under this classification, other sub-issues are also
included, such as, slack reclamation, fixed/dynamic priority sched-uling,
partition-based/global scheduling, and application-specific power
consumption, etc.