Energy-aware application performance management in virtualized data centers

Both performance and energy cost are important concerns for current data center operators. Traditionally, however, IT and mechanical engineers have separately optimized the cyber and physical aspects of data center operations. This paper considers both of these aspects with the eventual goal of developing performance and power management techniques that operate holistically to control the entire cyber-physical complex of data center installations. Toward this end, we propose a balance of payments model for holistic power and performance management. As an example of coordinated cyber-physical system management, the energy-aware cyber-physical system (EaCPS) uses an application controller on the cyber side to guarantee application performance, and on the physical side, it utilizes electric current-aware capacity management (CACM) to smartly place executables to reduce the energy consumption of each chassis present in a data center rack. A web application, representative of a multi-tier web site, is used to evaluate the performance of the controller on the cyber side, the CACM control on the physical side, and the holistic EaCPS methods in a mid-size instrumented data center. Results indicate that coordinated EaCPS outperforms separate cyber and physical control modules.     

Photoshop色阶和曲线工具的对比分析

介绍了Photoshop软件中色阶和曲线工具的主要功能。通过几种不同的色彩调整示例,给出了色阶和曲线工具达到同样调整效果的方法要点和对比分析,从而得到曲线和色阶工具在调整功能、范围和难易等方面的区别,方便在实际应用中针对不同的调整效果选择不同的调整工具。     

汉江中下游水体重金属时空分布及污染评价

为探明汉江中下游水体中重金属的污染状况,分别于2019年6月(丰水期)和2020年1月(枯水期)在汉江中下游选取20个监测断面采集水样,监测水体中Cu、Zn、Pb、Fe、Mn、As、Se和Cd 8种重金属的含量,分析汉江中下游水体中重金属污染的时空分布特征,并对水体重金属污染状况进行评价。结果表明:汉江中下游水体重金属污染以Fe、Mn为主,其他6种重金属浓度均满足地表水Ⅱ类水标准。汉江中下游水体中Fe和Mn含量沿程波动较大,二者变化规律基本一致。Fe和Mn含量最大值在支流竹皮河,其中Fe浓度超标4.63倍,Mn超标13%;此外,位于汉川市的S17断面Fe元素含量超标3.89倍。结合历史数据分析,发现近年来汉江中下游水体重金属含量总体呈上升趋势。评价结果表明,汉江中下游大部分水体水质良好,钟祥市和汉川市是汉江中下游重金属污染相对较重的两个城市,支流竹皮河的水体重金属污染问题需引起重视。     

洪湖沉积物碳氮磷分布特征及污染评价

洪湖是中国第七大淡水湖,富营养化问题日益突出。全面了解洪湖沉积物氮、磷、有机质的含量及分布特征,对掌握其富营养化现状与氮磷污染生态风险具有重要意义。在湖区布设了8个采样点,2019年10月采集50 cm柱状沉积物,分别测定不同深度沉积物总氮(TN)、总磷(TP)以及有机质(OM)含量,分析了TN、TP 和OM 含量的空间分布特征及相关性,并运用综合污染指数法评价其对应的污染程度。结果表明:洪湖沉积物TN含量在467.8~8 454.5 mg/kg之间,平均值2 167 mg/kg,为重度污染,其中近一半的采样点表层沉积物TN含量>5 000 mg/kg;TP含量在502.7~1 252.4 mg/kg之间,平均值693.8 mg/kg,除杨柴湖为重度污染外,其他大部分地区为中度污染;有机质含量占比在5.0%~24.9%之间,平均值9.6%,为重度污染。在垂直空间上,TN、TP 和OM 均在沉积物表层垂向深度0~20 cm存在明显的积累,其含量随垂向深度的增加而迅速降低。研究成果可为洪湖富营养化的控制与治理提供科学数据支撑。     

Entity-level sentiment prediction in Danmaku video interaction

The Journal of Supercomputing - Sentiment analysis in Danmaku video interaction aims at measuring public mood in respect of the video, which is helpful for the potential applications in behavioral...     

A frequency-aware and energy-saving strategy based on DVFS for Spark

The Journal of Supercomputing - With the fast growth of big data applications, it has brought about a huge increase in the energy consumption for big data processing in Cloud data centers. In this...     

Evaluating Visual Properties via Robust HodgeRank

International Journal of Computer Vision - Nowadays, how to effectively evaluate visual properties has become a popular topic for fine-grained visual comprehension. In this paper we study the...     

LabVIEW机械故障诊断系统的Web实现

本文针对机械设备远程故障诊断的振动数据分析问题,设计了一种在Web浏览器中进行机械设备振动数据实时显示和频谱分析的方法。首先,LabVIEW程序通过以太网从工业现场采集实时振动数据并以网络共享变量发布;然后,LabVIEW Web Service服务程序根据用户请求获取实时振动波形数据或对数据进行频谱分析,将数据以XML方式返回;最后,客户端的浏览器中分别采用Flex和Ajax对XML进行解析并显示实时波形和特征参数。该方法快速、稳定、可靠,很好地解决了远程故障诊断的问题。     

Enhancing collaborative filtering by user interest expansion via personalized ranking

Recommender systems suggest a few items from many possible choices to the users by understanding their past behaviors. In these systems, the user behaviors are influenced by the hidden interests of the users. Learning to leverage the information about user interests is often critical for making better recommendations. However, existing collaborative-filtering-based recommender systems are usually focused on exploiting the information about the user's interaction with the systems; the information about latent user interests is largely underexplored. To that end, inspired by the topic models, in this paper, we propose a novel collaborative-filtering-based recommender system by user interest expansion via personalized ranking, named iExpand. The goal is to build an item-oriented model-based collaborative-filtering framework. The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues that exist in traditional collaborative-filtering approaches, such as the overspecialization problem and the cold-start problem. Finally, we evaluate iExpand on three benchmark data sets, and experimental results show that iExpand can lead to better ranking performance than state-of-the-art methods with a significant margin.     

A low distortion image enhancement scheme based on multi-resolutions analysis in next generation network

With the advent of Next Generation Network (NGN), services that are currently provided by multiple specific network-centric architectures. NGN provides AAA (Anytime, Anywhere and Always on) access to users from different service providers with consistent and ubiquitous provision of services as necessary. This special issue of NGN includes pervasive, grid, and peer-to-peer computing to provide computing and communication services at anytime and anywhere. In fact, the application of NGN includes digital image processing, multimedia systems/services, and so on. Here we focus on the digital image processing technology in NGN environments. Low-contrast structure and heavy noise in NGN environments can be found in many kinds of digital images, which makes the images vague and uncertainly, especially in x-ray images. As result, some useful tiny characteristic are weakened—which are difficult to distinguish even by naked eyes. Based on the combination of no-linear grad-contrast operator and multi-resolution wavelet analysis, a kind of image enhancement processing algorithm for useful tiny characters is presented. The algorithm can enhance the tiny characters while confine amplifying noise. The analysis of the results shows that local regions of the image are enhanced by using the concept of the grad contrast to make image clearer adaptively. Experiments were conducted on real pictures, and the results show that the algorithm is flexible and convenient.