Emerging mobile platforms integrate heterogeneous, multicore processors to efficiently deal with the heterogeneity of data (in magnitude, type, and quality). The main goal is to achieve a high degree of energy-proportionality which corresponds with the nature and fluctuation of mobile workloads. Most existing power and energy consumption analyses of these architectures rely on simulation or static benchmarks neither of which truly reflects the type of workload the processors handle in reality. By contrast, we generate two types of stochastic workloads and employ four types of dynamic voltage and frequency scaling (DVFS) policies to investigate the energy proportionality and the dynamic power consumption characteristics of a heterogeneous processor architecture when operating in different configurations. The analysis illustrates, both qualitatively and quantitatively, that knowledge of the statistics of the incoming workload is critical to determine the appropriate processor configuration.

Nowadays, our daily live concerns the usage of Information Technology, increasingly. As a result, a huge amount of data has to be processed which is outsourced from local devices to data centers. Due to fluctuating demands these are not fully utilized all the time and consume a significant amount of energy while idling. A common approach to avoid unnecessary idle times is to consolidate running services on a subset of machines and switch off the remaining ones. Unfortunately, the services on a single machine interfere with each other due to the competition for shared resources such as caches after the consolidation, which leads to a degradation of performance. Hence, data centers have to trade off between reducing the energy consumption and certain performance criteria defined in the Service Level Agreement. In order to make the trade off in advance, it is necessary to characterize services and quantify the impact to each other after a potential consolidation. Our approach is to use random variables for characterization, which includes the fluctuations of the resource consumptions. Furthermore, we would like to model the interference of services to provide a probability of exceeding a certain performance criterion.