Driven by the demand for higher computational power and communication ca-pabilities, the power consumption of electronic computing devices has increased drastically in the past decade. This calls for innovative and more energy efficient system designs not only for battery-powered portable devices but also for com-puter servers and server clusters that face the overheating problem. The concept of power-awareness has been incorporated into today's hardware and software development as an important design criteria....[ Read more ]

Driven by the demand for higher computational power and communication ca-pabilities, the power consumption of electronic computing devices has increased drastically in the past decade. This calls for innovative and more energy efficient system designs not only for battery-powered portable devices but also for com-puter servers and server clusters that face the overheating problem. The concept of power-awareness has been incorporated into today's hardware and software development as an important design criteria.

This thesis studies the use of software strategies to improve energy efficiency in computing and wireless communications. In particular, we investigate the impact of scheduling on power management and propose new strategies for processor scheduling and wireless data transmission/reception to improve energy efficiency without violating the QoS requirements of the applications.

We first address the issue of power-aware voltage scaling for real-time peri-odic tasks. The concept of "blocking-awareness" is introduced in the context of scheduling tasks with non-preemptible sections. The proposed blocking-aware algorithms dynamically adjust the processor speed based on run-time blocking occurrences instead of worst-case blocking expectation, therefore they can greatly reduce processor energy consumption over static speed schemes.

The workload of desktop PCs and server systems is usually aperiodic and an average delay guarantee is desirable. We design stationary voltage scaling policies that adjust the processor speed according to the number of tasks in the system. Markov models are employed to compute the statistically energy-optimal policy. This approach is then extended to multiprocessor systems and heterogeneous server clusters, where some processors are selectively turned off to further reduce energy dissipation.

The wireless transceiver is another major energy consumer in a networked computer system. For wireless networks with limited energy and a designed lifetime, it is important to maximize the utility of available energy instead of minimizing energy usage. We present an iterative algorithm to compute the opti-mal transmission power setting to multiple receivers. The transmission schedule produced fully utilizes the available energy and achieves maximum data through-put in each operating cycle. When data transmission is associated with values, the value maximization problem is formulated as a convex optimization problem, and is solved efficiently using a dynamic programming approach.

Finally, we analyze the energy consumption of data reception in a wireless LAN environment. We point out that existing standards are not energy effi-cient as they may cause mobile clients to wake up frequently and wait for an extended period of time before actual data reception. Considering streaming me-dia applications over the Internet, we propose schemes that combine power-aware scheduling and traffic shaping at the local proxy server. The proposed schemes minimize the waiting time of the mobile clients and effectively reduce their total energy consumption. Furthermore, we extend our algorithms to take the residue battery capacities of the clients into consideration so that clients at low battery level could spend less energy and enjoy a prolonged battery life.

It is our wish that these analyses and algorithms will benefit the design of energy-efficient computing and wireless communication software systems.