With respect to the continuous growth of computing systems, the energy-efficiency requirement of their processes becomes even more important. Different configurations, implying different energy-efficiency of the system, could be used to perform the process. A configuration denotes the choice among different hard- and software settings (e.g., CPU frequency, number of threads, the concrete algorithm, etc.). The identification of the most energy-efficient configuration demands to benchmark all configurations. However, this benchmarking is time- and energy-consuming, too. This thesis explores (a) the effect of dynamic voltage and frequency scaling (DVFS) in combination with dynamic concurrency throttling (DCT) on the energy consumption of (de)compression, DBMS query executions, encryption/decryption and sorting; and (b) a generic approach to reduce the benchmarking efforts to determine the optimal configuration. Our findings show that the utilization of optimal configurations can save wavg. 15.14% of energy compared to the default configuration. Moreover, we propose a generic heuristic (fractional factorial design) that utilizes data mining (adaptive instance selection) together with machine learning techniques (multiple linear regression) to decrease benchmarking effort by building a regression model based on the smallest feasible subset of the benchmarked configurations. Our approach reduces the energy consumption required for benchmarking by 63.9% whilst impairing the energy-efficiency of performing the computational process by only 1.88 pp, due to not using the optimal but a near-optimal configuration.

With respect to the continuous growth of computing systems, the energy-efficiency requirement of their processes becomes even more important. Different configurations, implying different energy-efficiency of the system, could be used to perform the process. A configuration denotes the choice among different hard- and software settings (e.g., CPU frequency, number of threads, the concrete algorithm, etc.). The identification of the most energy-efficient configuration demands to benchmark all configurations. However, this benchmarking is time- and energy-consuming, too. This thesis explores (a) the effect of dynamic voltage and frequency scaling (DVFS) in combination with dynamic concurrency throttling (DCT) on the energy consumption of (de)compression, DBMS query executions, encryption/decryption and sorting; and (b) a generic approach to reduce the benchmarking efforts to determine the optimal configuration. Our findings show that the utilization of optimal configurations can save wavg. 15.14% of energy compared to the default configuration. Moreover, we propose a generic heuristic (fractional factorial design) that utilizes data mining (adaptive instance selection) together with machine learning techniques (multiple linear regression) to decrease benchmarking effort by building a regression model based on the smallest feasible subset of the benchmarked configurations. Our approach reduces the energy consumption required for benchmarking by 63.9% whilst impairing the energy-efficiency of performing the computational process by only 1.88 pp, due to not using the optimal but a near-optimal configuration.