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Abstract A lot of cloud computing and cloud database techniques are adopted in industry and academia to face the explosion of the arrival of the big data era. Meanwhile, energy efficiency and energy saving become a major concern in data centers, which are in charge of large distributed systems and cloud databases. However, the phenomenon of energy wasting is related to resource provisioning. Hot-N-Cold model is introduced in this paper, which uses workload predictions and DVFS(Dynamic Voltage and Frequency Scaling) to cope with the resource provisioning problem within energy aware cloud database systems. In this model, the resource provisioning problem is considered as two bounded problems. A nonlinear programming algorithm and a multi-phase algorithm are proposed to solve them. The experimental results show that one of the proposed algorithms has great scalability which can be applied to a cloud database system deployed on 70 nodes. Using Hot-N-Cold model can save up to 21.5% of the energy of the running time. Highlights • The energy efficiency in cloud database systems is discussed. • The resource-providing problem in cloud database systems is considered as two bounded problems which are studied in this work. • Under Hot-N-Cold model, nonlinear programming algorithm and multiphase algorithm are proposed to solve the bounded problems. • The simulation and real case experiments are designed and implemented to measure the proposed algorithms in aspect of performance and assurance. [ABSTRACT FROM AUTHOR]

Despite calls for more socio-technical research on energy, there is little practical advice to how narratives collected through qualitative research may be melded with technical knowledge from the physical sciences such as engineering and then applied in energy efficiency social action strategies. This is despite established knowledge in the environmental management literature about domestic energy use regarding the utility of social practice theory and narrative framings that socialise everyday consumption. Storytelling is positioned in this paper both as a focus for socio-technical energy research, and as one potential practical tool that can arguably enhance energy efficiency interventions. We draw upon the literature on everyday social practices, and storytelling, to present our framework called ‘collective video storytelling’ that combines scientific and lay knowledge about domestic energy use to offer a practical tool for energy efficiency management. Collective video storytelling is discussed in the context of Energy+Illawarra, a 3-year cross-disciplinary collaboration between social marketers, human geographers, and engineers to target energy behavioural change within older low-income households in regional NSW, Australia. [ABSTRACT FROM AUTHOR]

Global warming is one of the most serious issues faced by today's world. The increase in world population and adoption of modern lifestyle have dramatically increased the demand for energy. Over the last decade Higher Educational Institution (HEI) buildings have seen massive increase in energy consumption due to increased use of IT equipments, longer occupancy and increased use of Heating Ventilation and Air Conditioning (HVAC) systems. Current Building Management Systems (BMS) fail to optimize energy consumption of HVAC systems in commercial and educational buildings. In this paper we present an intelligent agent based system to optimize energy consumption of HVAC system in HEI buildings. The system employs artificial intelligence techniques to predict the demand of the system and optimize energy consumption of the HVAC system. The experimental results have shown that the deployment of the system has resulted in 3% reduction in energy consumption of HVAC. [ABSTRACT FROM AUTHOR]

Holistic management of water and energy resources is critical for water utilities facing increasing energy prices, water supply shortage and stringent regulatory requirements. In the early 1990s, the concept of an integrated Energy and Water Quality Management System (EWQMS) was developed as an operational optimization framework for solving water quality, water supply and energy management problems simultaneously. Approximately twenty water utilities have implemented an EWQMS by interfacing commercial or in–house software optimization programs with existing control systems. For utilities with an installed EWQMS, operating cost savings of 8–15% have been reported due to higher use of cheaper tariff periods and better operating efficiencies, resulting in the reduction in energy consumption of ∼6–9%. This review provides the current state-of-knowledge on EWQMS typical structural features and operational strategies and benefits and drawbacks are analyzed. The review also highlights the challenges encountered during installation and implementation of EWQMS and identifies the knowledge gaps that should motivate new research efforts. [ABSTRACT FROM AUTHOR]

Abstract: DVFS is a ubiquitous technique for CPU power management in modern computing systems. Reducing processor frequency/voltage leads to a decrease of CPU power consumption and an increase in the execution time. In this paper, we analyze which application/platform characteristics are necessary for a successful energy-performance trade-off of large scale parallel applications. We present a model that gives an upper bound on performance loss due to frequency scaling using the application parallel efficiency. The model was validated with performance measurements of large scale parallel applications. Then we track how application sensitivity to frequency scaling evolved over the last decade for different cluster generations. Finally, we study how cluster power consumption characteristics together with application sensitivity to frequency scaling determine the energy effectiveness of the DVFS technique. [Copyright &y& Elsevier]