Copyright by William Lloyd Bircher 2010 - The Laboratory for ...

9.2 Future Work
Run-time energy accounting and predictive power management are promising tools for
improving the energy efficiency and performance of computing systems. This
dissertation has demonstrated the initial implementation of these tools at improving
processor efficiency and performance. Listed below are a few similar research areas that
are likely to yield new, valuable discoveries.
Power Managing Cloud Computing Resources
The shift away from desktop computing to cloud computing is increasing the opportunity
for predictive power management. A typical desktop system has about sixty active
processes, with less than ten of them actively consuming most computing resources. In
contrast, cloud computing servers combine hundreds of active processes from many
clients. Each of the processes has phase behavior that is independent of the others. This
combination of heterogeneous tasks makes existing reactive power management difficult
since the active-idle patterns are an aggregation of multiple independent patterns. A
significant power savings opportunity exists in accounting for and predicting the effective
usage pattern of cloud computing servers.
Functional Unit Activity Prediction
The need to increase power efficiency is pushing microarchitectural power management
beyond the core-level to individual functional units. To save power during program
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