The Experts below are selected from a list of 116625 Experts worldwide ranked by ideXlab platform
Andre Stephan - One of the best experts on this subject based on the ideXlab platform.
-
multi scale life cycle Energy analysis of a low density suburban neighbourhood in melbourne australia
Building and Environment, 2013Co-Authors: Andre Stephan, Robert H Crawford, Kristel De MyttenaereAbstract:Abstract Many cities are likely to expand in the coming decades and this expansion will probably include low-density neighbourhoods. There is an increasing pressure on cities worldwide to accommodate an increasing population. It is therefore crucial to assess the Energy demand and related greenhouse gas emissions implications of such development. This paper uses a representative low-density neighbourhood in Melbourne, Australia, assesses its Energy Consumption and greenhouse gas emissions over 100 years and investigates various scenarios related to house size, transport technology, housing typology and the temporal evolution of parameters. Results show that the Energy required to produce and replace building materials and infrastructures constitutes 26.9% of the Total Energy Consumption, while operational and transport requirements represent 39.4% and 33.7% respectively. One of the analysed scenarios reveals that replacing half of the built area of the suburb with apartment buildings reduces the Total Energy Consumption per capita by 19.6%, compared to a typical single storey detached house layout. Regardless of the uncertainty in the data, the main conclusion is that each of the embodied, operational and transport Energy demand and associated greenhouse gas emissions can be reduced in order to improve the overall environmental performance of new urban neighbourhoods.
Nam Thoai - One of the best experts on this subject based on the ideXlab platform.
-
EMinRET: Heuristic for Energy-Aware VM Placement with Fixed Intervals and Non-preemption
Proceedings - 2015 International Conference on Advanced Computing and Applications ACOMP 2015, 2016Co-Authors: Nguyen Quang-hung, Nam ThoaiAbstract:Infrastructure-as-a-Service (IaaS) clouds have become more popular enabling users to run applications under virtual machines. This paper investigates the Energy-aware virtual machine (VM) allocation problems in IaaS clouds along characteristics: multiple resources, and fixed interval times and non-preemption of virtual machines. Many previous works proposed to use a minimum number of physical machines, however, this is not necessarily a good solution to minimize Total Energy Consumption in the VM placement with multiple resources, fixed interval times and non-preemption. We observed that minimizing Total Energy Consumption of physical machines is equivalent to minimize the sum of Total completion time of all physical machines. Based on the observation, we propose EMinRET algorithm. The EMinRET algorithm swaps an allocating VM with a suitable overlapped VM, which is of the same VM type and is allocated on the same physical machine, to minimize Total completion time of all physical machines. The EMinRET uses resource utilization during executing time period of a physical machine as the evaluation metric, and will then choose a host that minimizes the metric to allocate a new VM. In addition, this work studies some heuristics for sorting the list of virtual machines (e.g., sorting by the earliest starting time, or the longest duration time first, etc.) to allocate VM. Using the realistic log-trace in the Parallel Workloads Archive, our simulation results show that the EMinRET algorithm could reduce from 25% to 45% Energy Consumption compared with power-aware best-fit decreasing (PABFD)) and vector bin-packing norm-based greedy algorithms. Moreover, the EMinRET heuristic has also less Total Energy Consumption than our previous heuristics (e.g. MinDFT and EPOBF) in the simulations (using same virtual machines sorting method).
-
eminret heuristic for Energy aware vm placement with fixed intervals and non preemption
International Conference on Advanced Computing, 2015Co-Authors: Nguyen Quanghung, Nam ThoaiAbstract:Infrastructure-as-a-Service (IaaS) clouds have become more popular enabling users to run applications under virtual machines. This paper investigates the Energy-aware virtual machine (VM) allocation problems in IaaS clouds along characteristics: multiple resources, and fixed interval times and non-preemption of virtual machines. Many previous works proposed to use a minimum number of physical machines, however, this is not necessarily a good solution to minimize Total Energy Consumption in the VM placement with multiple resources, fixed interval times and non-preemption. We observed that minimizing Total Energy Consumption of physical machines is equivalent to minimize the sum of Total completion time of all physical machines. Based on the observation, we propose EMinRET algorithm. The EMinRET algorithm swaps an allocating VM with a suitable overlapped VM, which is of the same VM type and is allocated on the same physical machine, to minimize Total completion time of all physical machines. The EMinRET uses resource utilization during executing time period of a physical machine as the evaluation metric, and will then choose a host that minimizes the metric to allocate a new VM. In addition, this work studies some heuristics for sorting the list of virtual machines (e.g., sorting by the earliest starting time, or the longest duration time first, etc.) to allocate VM. Using the realistic log-trace in the Feitelson's Parallel Workloads Archive, our simulation results show that the EMinRET algorithm could reduce from 25% to 45% Energy Consumption compared with power-aware best-fit decreasing (PABFD) [1]) and vector binpacking norm-based greedy algorithms (VBP-Norm-L1/L2 [2]). Moreover, the EMinRET heuristic has also less Total Energy Consumption than our previous heuristics (e.g. MinDFT and EPOBF) in the simulations (using same virtual machines sorting method).
Yiming Wei - One of the best experts on this subject based on the ideXlab platform.
-
china s Energy Consumption in the building sector a life cycle approach
Energy and Buildings, 2015Co-Authors: Yan Zhang, Baojun Tang, Yiming WeiAbstract:Abstract Currently, there is no clear and unified understanding about the status quo of China's Energy Consumption in the building sector. In addition, a considerable underestimation of Energy associated with buildings has impeded the effective implementation of measures to improve building Energy efficiency of China. Thus, in this paper, we seek to identify the building sector's Energy Consumption of China by establishing an estimation model of building Energy Consumption from a life cycle perspective. On the basis of macro-level statistical data and relevant literature, we analyze the activities in each phase and calculate associated Energy Consumptions throughout buildings’ whole life cycle in China from 2001 to 2013. The results show that China's Energy Consumption associated with buildings has reached 1.66 billion tons coal equivalent in 2013, with a stable growth rate of 7% annually since 2001. Buildings’ life-cycle Energy has approximately accounted for 43% of China's Total Energy Consumption for recent three years (2011–2013). What's more, Energy Consumption in buildings’ operation phase has been salient, accounting for over 20% of China's Total Energy Consumption. More focus should be drawn on Energy efficiency in building material production phase and Energy consumed in China's rural residential buildings as both have been significantly neglected.
Raymond Chiong - One of the best experts on this subject based on the ideXlab platform.
-
solving the Energy efficient job shop scheduling problem a multi objective genetic algorithm with enhanced local search for minimizing the Total weighted tardiness and Total Energy Consumption
Journal of Cleaner Production, 2016Co-Authors: Rui Zhang, Raymond ChiongAbstract:Abstract In recent years, there has been a growing concern over the environmental impact of traditional manufacturing, especially in terms of Energy Consumption and related emissions of carbon dioxide. Besides the adoption of new equipment, production scheduling could play a key role in reducing the Total Energy Consumption of a manufacturing plant. In this paper, we explicitly introduce the objective of minimizing Energy Consumption into a typical production scheduling model, i.e., the job shop scheduling problem, based on a machine speed scaling framework. To solve this bi-objective optimization problem, we propose a multi-objective genetic algorithm incorporated with two problem-specific local improvement strategies. These local improvement procedures aim to enhance the solution quality by utilizing the mathematical models of two restricted subproblems derived from the original problem. Comprehensive computational experiments have been carried out to verify the effectiveness of the proposed solution approach. The results presented in this work may be useful for future research on Energy-efficient production scheduling.
Yan Zhang - One of the best experts on this subject based on the ideXlab platform.
-
china s Energy Consumption in the building sector a life cycle approach
Energy and Buildings, 2015Co-Authors: Yan Zhang, Baojun Tang, Yiming WeiAbstract:Abstract Currently, there is no clear and unified understanding about the status quo of China's Energy Consumption in the building sector. In addition, a considerable underestimation of Energy associated with buildings has impeded the effective implementation of measures to improve building Energy efficiency of China. Thus, in this paper, we seek to identify the building sector's Energy Consumption of China by establishing an estimation model of building Energy Consumption from a life cycle perspective. On the basis of macro-level statistical data and relevant literature, we analyze the activities in each phase and calculate associated Energy Consumptions throughout buildings’ whole life cycle in China from 2001 to 2013. The results show that China's Energy Consumption associated with buildings has reached 1.66 billion tons coal equivalent in 2013, with a stable growth rate of 7% annually since 2001. Buildings’ life-cycle Energy has approximately accounted for 43% of China's Total Energy Consumption for recent three years (2011–2013). What's more, Energy Consumption in buildings’ operation phase has been salient, accounting for over 20% of China's Total Energy Consumption. More focus should be drawn on Energy efficiency in building material production phase and Energy consumed in China's rural residential buildings as both have been significantly neglected.
