In this paper, both software model visualization with path simulation and associated machining product are produced based on the step ring-based three-axis path planning to demo model-driven graphics processing unit (GPU) feature in tool path planning and 3D image model classification by GPU simulation. Subtractive 3D printing (i.e., 3D machining) is represented as integration between 3D printing modeling and computer numerical control (CNC) machining via GPU simulated software. Path planning is applied through visualization of surface material removal in high-resolution and 3D path simulation via ring selective path planning based on accessibility of path through pattern selection. First, the step ring selects critical features to reconstruct computer-aided design (CAD) design model as stereolithography (STL) voxel, and then, local optimization is attained within interested ring area for time and energy saving of GPU volume generation as compared to global automatic path planning with longer latency. The reconstructed CAD model comes from an original sample (GATech buzz) with 2D image information. CAD model for optimization and validation is adopted to sustain manufacturing reproduction based on system simulation feedback. To avoid collision with the produced path from retraction path, we pick adaptive ring path generation and prediction in each planning iteration, which may also minimize material removal. Moreover, we did partition analysis and G-code optimization for large-scale model and high density volume data. Image classification and grid analysis based on adaptive 3D tree depth are proposed for multilevel set partition of the model to define no cutting zones. After that, accessibility map is computed based on accessibility space for rotational angular space of path orientation to compare step ring-based pass planning verses global path planning of all geometries. Feature analysis via central processing unit (CPU) or GPU processor for GPU map computation contributes to high-performance computing and cloud computing potential through parallel computing application of subtractive 3D printing in the future.

In this paper, both software model visualization with path simulation and associated machining product are produced based on the step ring based 3-axis path planning to demo model-driven graphics processing unit (GPU) feature in tool path planning and 3D image model classification by GPU simulation. Subtractive 3D printing (i.e., 3D machining) is represented as integration between 3D printing modeling and CNC machining via GPU simulated software. Path planning is applied through material surface removal visualization in high resolution and 3D path simulation via ring selective path planning based on accessibility of path through pattern selection. First, the step ring selects critical features to reconstruct computer aided design (CAD) design model as STL (stereolithography) voxel, and then local optimization is attained within interested ring area for time and energy saving of GPU volume generation as compared to global all automatic path planning with longer latency. The reconstructed CAD model comes from an original sample (GATech buzz) with 2D image information. CAD model for optimization and validation is adopted to sustain manufacturing reproduction based on system simulation feedback. To avoid collision with the produced path from retraction path, we pick adaptive ring path generation and prediction in each planning iteration, which may also minimize material removal. Moreover, we did partition analysis and g-code optimization for large scale model and high density volume data. Image classification and grid analysis based on adaptive 3D tree depth are proposed for multi-level set partition of the model to define no cutting zones. After that, accessibility map is computed based on accessibility space for rotational angular space of path orientation to compare step ring based pass planning verses global all path planning. Feature analysis via central processing unit (CPU) or GPU processor for GPU map computation contributes to high performance computing and cloud computing potential through parallel computing application of subtractive 3D printing in the future.

Power management is becoming very important in data centers. To apply power management in cloud computing, Green Computing has been proposed and considered. Cloud computing is one of the new promising techniques, that are appealing to many big companies. In fact, due to its dynamic structure and property in online services, cloud computing differs from current data centers in terms of power management. To better manage the power consumption of web services in cloud computing with dynamic user locations and behaviors, we propose a power budgeting design based on the logical level, using distribution trees. By setting multiple trees or forest, we can differentiate and analyze the effect of workload types and Service Level Agreements (SLAs, e.g. response time) in terms of power characteristics. Based on these, we introduce classified power capping for different services as the control reference to maximize power saving when there are mixed workloads.

In order to optimally schedule energy, consumers need to understand their energy utility function. The traditional utility function must be modified when consumers acquire production capabilities and become prosumers. This paper presents a formal economic model for prosumers, appropriately integrating distributed generation, storage and demand response capabilities. We identify the limitations of existing prosumer-like models. Finally, we demonstrate how the proposed prosumer model is applied to energy evaluation to distributed prosumer decisions regarding environmental objectives.

Just as the other informatics-related domains (e.g., Bioinformatics) have discovered in recent years, the ever-growing domain of Energy Informatics (EI) can benefit from the use of ontologies, formalized, domain-specific taxonomies or vocabularies that are shared by a community of users. In this paper, an overview of the Ontology for Energy Investigations (OEI), an ontology that extends a subset of the well-conceived and heavily-researched Ontology for Biomedical Investigations (OBI), is provided as well as a motivating example demonstrating how the use of a formal ontology for the EI domain can facilitate correct and consistent knowledge sharing and the multi-level analysis of its data and scientific investigations.

Power management is becoming very important in data centers. Cloud computing is also one of the newest promising data center techniques which is appealing to many big companies. As cloud computing is different from current data centers in terms of power management due to a dynamic structure and property for its online service. Power budgeting, in terms of its important role in power management, provides powerful solutions for cloud computing with dynamic capabilities. To be specific, existing methods for data centers are based on power distribution units (PDU) divided by fixed locations on physical levels. However, it is not suitable for cloud with the dynamic property. We propose a power management design based at the logical level which uses a distribution tree with classified power capping by different service or workload types. By setting multiple trees, we can differentiate and analyze the effect of workload types and Service Level Agreements (SLAs) in terms of power characteristics.

Power management is becoming very important in data centers. Cloud computing is also one of the newer promising techniques, that are appealing to many big companies. To apply power management in cloud computing has been proposed and considered as green computing. Cloud computing, due to its dynamic structure and property in online services, differs from current data centers in terms of power management. To better manage the power consumption of web services in cloud computing with dynamic user locations and behaviors, we propose a power budgeting design based on the logical level, using a distribution tree. By setting multiple trees, we can differentiate and analyze the effect of workload types and Service Level Agreements (SLAs, e.g. response time) in terms of power characteristics. Base on these, we introduce classified power capping for different services as the control reference to maximize power saving when there are mixed workloads.