云计算的发展与应用前景展望

随着大规模互联网应用的发展,分布式处理已经不能够满足互联网的发展要求。近几年提出云计算的构想并进行了初步的实现。通过研究,明确了何为云计算,云计算与传统的分布式计算的联系与区别,并对云计算在各领域中的应用进行了前景展望。     

基于云计算的多媒体专业教学资源库建设研究

研究了云计算理念下的多媒体专业教学资源库建设方法,对专业教学资源库的组成架构和功能模块进行了阐述,分析了专业教学资源库建设和使用的有效途径,提出了以用户为中心,以需求为导向,以提升用户的学习力和创造力为根本的多媒体专业教学资源库建设思路.     

云计算时代高校IT人才培养模式的创新

阐述了云计算的应用优势;基于对云计算技术特点和知名招聘网站IT职位描述信息进行综合分析,总结未来IT人才需求的趋势,并针对高校相关专业IT人才培养模式提出了改革措施。     

火焰动画快速模拟的新方法

针对火焰动画难以快速建模和绘制的问题,提出了完全利用GPU过程化生成火焰动画的高效算法。先用建模工具在预处理阶段构建一个由平面正方形组成的三维模型,在绘制阶段通过GPU编程对这些方形进行Billboard方式的变换,并用一些简单公式对他们的运动方式、大小和颜色来用过程化方式进行控制,最后通过alpha混合得到最终结果。算法完全在GPU里实现,能满足实时的绘制要求。     

多用户动态节能资源竞争计算卸载策略

针对移动边缘计算环境下,移动设备的计算、续航、存储能力的不足,导致其响应延迟、电池寿命降低等问题,设计了一种动态节能资源竞争计算卸载（DERCO）策略。该策略综合考虑了信道容量以及设备间的相互干扰等情况,以一种贪婪算法的思想,让所有边缘设备通过迭代竞争信道资源,节能效果最好的设备将获得卸载机会,再利用一次性卸载方法,根据实时的信道状况和卸载速率进行动态的细粒度子任务卸载决策。实验结果表明,该策略优于一种近似协同计算卸载方案,有效降低了设备的能耗与时延。     

移动边缘计算中基于中继辅助计算方法综述*

为了满足下一代网络在覆盖范围、部署成本以及容量方面的挑战,移动边缘计算（MEC）通常需要借助中继节点的辅助来完成计算密集型和延迟敏感型的任务。首先介绍了基于中继辅助MEC系统的基本架构,之后从任务卸载、资源分配和中继节点选择三方面对基于中继辅助MEC系统最新的研究方法进行归纳总结。更进一步,针对现有方法可能存在的问题与挑战进行了讨论与分析,并提出一些可行的解决方案为后续研究发展提供参考。     

基于负载预测的微服务混合自动扩展

由于边缘云没有比中心云更强大的计算处理能力,在应对动态负载时很容易导致无意义的扩展抖动或资源处理能力不足的问题,所以在一个真实的边缘云环境中对微服务应用程序使用两个合成和两个实际工作负载进行实验评估,并提出了一种基于负载预测的混合自动扩展方法（predictively horizontal and vertical pod autoscaling,Pre-HVPA）。该方法首先采用机器学习对负载数据特征进行预测,并获得最终负载预测结果。然后利用预测负载进行水平和垂直的混合自动扩展。仿真结果表明,基于该方法所进行自动扩展可以减少扩展抖动和容器使用数量,所以适用于边缘云环境中的微服务应用。     

岩石高边坡稳定性并行计算及其应用

在高地震烈度区的岩石高边坡抗震安全评价需要大规模数值计算,而现有商业软件无法有效解决这类复杂的工程问题,因此,本文开发了解决岩石高边坡接触稳定性问题的对等架构并行计算程序,并应用于泰山公司抽水蓄能电站上水库左岸边坡的静力和动力稳定性评价。在数值建模过程中,将岩石边坡滑块体、地质结构面、库水作用以及近场地基作为岩石高边坡动力接触系统。在稳定性安全评价中结合了有限元法、极限平衡法和接触算法,考虑影响高边坡稳定性的多种复杂因素,通过计算特定滑动面的安全系数或安全系数时程,基于现行水电工程水工建筑物抗震设计规范,完成了岩石高边坡稳定性评价,回避了求解复杂岩体材料非线性的问题。计算结果显示,所开发的程序具有较高的并行计算效率,对解决复杂岩石高边坡稳定问题具有显著的优势。     

A Data Security Framework for Cloud Computing Services

Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloud-based technologies, such as the Internet of Things. With increasing industry adoption and migration of traditional computing services to the cloud, one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies. This work proposes a Data Security Framework for cloud computing services (CCS) that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS. This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation. The paper presents the design details of the framework, which consists of three stages: identification of data security requirements, management of data security risks and evaluation of data security performance in CCS.     

LearningADD: Machine learning based acoustic defect detection in factory automation

Defect inspection of glass bottles in the beverage industrial is of significance to prevent unexpected losses caused by the damage of bottles during manufacturing and transporting. The commonly used manual methods suffer from inefficiency, excessive space consumption, and beverage wastes after filling. To replace the manual operations in the pre-filling detection with improved efficiency and reduced costs, this paper proposes a machine learning based Acoustic Defect Detection (LearningADD) system. Moreover, to realize scalable deployment on edge and cloud computing platforms, deployment strategies especially partitioning and allocation of functionalities need to be compared and optimized under realistic constraints such as latency, complexity, and capacity of the platforms. In particular, to distinguish the defects in glass bottles efficiently, the improved Hilbert-Huang transform (HHT) is employed to extend the extracted feature sets, and then Shuffled Frog Leaping Algorithm (SFLA) based feature selection is applied to optimize the feature sets. Five deployment strategies are quantitatively compared to optimize real-time performances based on the constraints measured from a real edge and cloud environment. The LearningADD algorithms are validated by the datasets from a real-life beverage factory, and the F-measure of the system reaches 98.48 %. The proposed deployment strategies are verified by experiments on private cloud platforms, which shows that the Distributed Heavy Edge deployment outperforms other strategies, benefited from the parallel computing and edge computing, where the Defect Detection Time for one bottle is less than 2.061 s in 99 % probability.