The development of computer technology has reshaped the form and philosophy of landscape design, as evidenced by the growing importance of computer-aided design (CAD) in this area. This paper attempts to apply 3D visualization in the teaching of landscape design. For this purpose, three CAD teaching tools were selected, namely, AutoCAD, 3D Max and SketchUp, and subjected to theoretical analysis and teaching practice. The research results show that AutoCAD can guide the landscape design teaching from conception to positioning, 3D Max can upgrade the teaching form from 2D to 3D in the later phase, and SketchUp sup-ports the entire process of landscape design. In addition, the author summarized the specific process of supporting landscape design teaching with SketchUp. This research enriches the results on the application of CAD in landscape design teaching and has certain theoretical and practical significance.

Virtualization is one of the most fascinating techniques because it can facilitate the infrastructure management and provide isolated execution for running workloads. Despite the benefits gained from virtualization and resource sharing, improved resource utilization is still far from settled due to the dynamic resource requirements and the widely-used overprovision strategy for guaranteed QoS. Additionally, with the emerging demands for big data analytic, how to effectively manage hybrid workloads such as traditional batch task and long-running virtual machine (VM) service needs to be dealt with. In this paper, we propose a system to combine longrunning VM service with typical batch workload like MapReduce. The objectives are to improve the holistic cluster utilization through dynamic resource adjustment mechanism for VM without violating other batch workload executions. Furthermore, VM migration is utilized to ensure high availability and avoid potential performance degradation. The experimental results reveal that the dynamically allocated memory is close to the real usage with only 10% estimation margin, and the performance impact on VM and MapReduce jobs are both within 1%. Additionally, at most 50% increment of resource utilization could be achieved. We believe that these findings are in the right direction to solving workload consolidation issues in hybrid computing environments.In this paper, we propose a workload consolidation approach which combines traditional batch processing tasks (e.g, MapReduce) with online long-running VM workload in a hybrid computing environment. We design an elastic resource allocation mechanism to timely adjust the amount of the overprovision according to the real application resource usage with 978-1-4799-7615-7/14/$31.00 ©2014 IEEE

Abstract. Although the demand for taxis is increasing rapidly with the soaring population in big cities, the taxi number grows relatively slowly during these years. In this context, private transportation such as Uber is emerging as a flexible business model, supplementary to the regular forms of taxis. At present, many works mainly focus on how to effectively reduce the taxi cruising miles. However, these taxi-based approaches still have some limitations in private car transportation scenario because they do not fully utilize the sufficient order information introduced by the new type of business model. In this paper, we present a dispatching method based on a passenger demand model to further reduce the private car cruising mileage. In particular, we firstly split the urban areas into many separate regions by using spatial clustering algorithm and partition one day into several time slots according to statistics of historical orders. Secondly, Locally Weighted Linear Regression is adopted to depict the passenger demand model in one specified region over a time slot. Finally, a dispatching process is formalized as a weighted bipartite graph matching problem and we leverage this dispatching approach to schedule private vehicles. To evaluate the effectiveness of our methods, we conduct several experiments based on real datasets derived from a private car hiring company in China. The experimental results illustrate that at most 74% accuracy could be achieved on passenger demand inference. Additionally, the cruising mileage could be reduced by 22.5% in simulation test.

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