CPU Scheduling for Power/Energy Management on Heterogeneous Multicore Processors

Power and energy have become increasingly important concerns in the design and implementation of today\u27s multicore/manycore chips. Many methods have been proposed to reduce a microprocessor\u27s power usage and associated heat dissipation, including scaling a core\u27s operating frequency. However, these techniques do not consider the dynamic performance characteristics of an executing process at runtime, the execution characteristics of the entire task to which this process belongs, the process\u27s priority, the process\u27s cache miss/cache reference ratio, the number of context switches and CPU migrations generated by the process, nor the system load. Also, many of the techniques that employ dynamic frequency scaling can lower a core\u27s frequency during the execution of a non-CPU intensive task, thus lowering performance. In addition, many of these methods require specialized hardware and have not been tested upon real hardware that is widely available, including the recent AMD or Intel multicore chips. One problem dealing with power/energy management for heterogeneous multicore processors is: Given a set of processes, each having identical default priorities, in a given task to be executed by a heterogeneous multicore/manycore processor system, schedule each process in this task to execute upon the CPU(s) in this system such that the global power budget is minimized, yet the performance gain of all processes is maximized, and the performance loss of all processes is minimized. Doing so, in a scenario where each process has a different (not necessarily unique) static or dynamic (but not necessarily the default) priority, without adversely affecting process completion order, as dictated by process priority is yet another problem. Finally, utilizing the cache miss/cache reference ratio and the number of context switches and CPU migrations as scheduling criteria are two other problems. This dissertation will elaborate upon these four problems, and will describe our four approaches to solving these problems