Online Power Measurement and Prediction of PCs

Abstract. Since more power consumption results in more failures and degradations in system performance, reliability, and power bills, it has been a critical problem for not only large scale server system but also personal computers (PCs). Though much literature has focused on energy management and power budgeting for server systems, power consumption of PCs does not attain sufficient attentions fairly. In this paper an online power measurement and prediction framework is proposed and used to save more energy considering the PC as a whole controlled system. The framework includes parts such as power measurement unit, power prediction unit and a simple execution unit of power reduction decisions. A hardware-software joint prototype is implemented based on an intelligent digital multimeter. Experiments on a desktop PC and a laptop show that PC with the framework can save more power consumptions than that of the PCs without this framework

Scalability and performance analysis of BDPS in clouds

International audienc

Scalability and performance analysis of BDPS in clouds

International audienc

Spatio-Temporal Land-Use/Land-Cover Change Dynamics in Coastal Plains in Hangzhou Bay Area, China from 2009 to 2020 Using Google Earth Engine

Land-use classification is fundamental for environmental and water resource evaluation in coastal plain areas. However, comprehensive remote sensing image-based land-use analysis is challenged by the lack of massive remote sensing images and the massive computing power of large-scale server systems. In this paper, the spatial-temporal land-use change characteristics of the Hangzhou Bay area coastal plain are investigated on the Google Earth Engine platform. The proposed model uses a random forest algorithm to assist the land-use classification. The dataset is selected from the year 2009 to 2020 and classified with an average classification accuracy of 89% and Kappa coefficient of 88%. The results show that the land use in the selected region is affected by urbanization, the balance of cultivated land occupation and compensation, construction of economic development zone, and other activities. The investigation also shows that in the past 12 years, land use has changed rapidly, and each land-use type maintains the dynamic balance of occupation and compensation. Although the overall land-use distribution is stable, the information entropy fluctuates at a high level, with an average value of 1.15, and the multi-year average value of equilibrium is as high as 0.83. The driving force of land-use change is analyzed and accounted as demographics and human population dynamics, social-economic development, urbanization, and coupling effects of the above-mentioned factors

Anticipation-based Green Data Classification Strategy in Cloud Storage System

The energy consumption problem is one of the critical issues to be addressed in current large-scale storage systems. In order to reduce energy consumption of cloud storage system and meet the performance requirements of users, this paper proposes a green data classification strategy based on anticipation (AGDC), which classify the data in cloud storage system: the hot data stored in the hot disk regions, the cold data stored in the cold disk regions. AGDC employ neural-network prediction on seasonal data, prediting the temperature of data in the next period, executing seasonal data migration in cold and hot regions. This paper also adopts a new correlating algorithm on new data, analyzes its relations with old data in the storage system and prediting the data temperature. New energy consumption model also established in this paper. Simulation experiments based on Gridsim showed that the cloud storage system with green data classification strategy based on anticipation has a good effect on reducing energy consumption. At the expense of average response time of 0.005s, proposed algorithm saved about 16% of energy consumption compared TDCS

Scalability and performance analysis of BDPS in clouds

International audienc

PPCTS: Performance Prediction-Based Co-located Task Scheduling in Clouds

International audienc