尚未缕清思路。 听领导说： http://news.sciencenet.cn/htmlnews/2019/7/427909.shtm “ 要利用好 朋友圈 ，把自己的冥思苦想变成 集思众创 ，共同把冷板凳 坐热 。” “提升自主创新能力，突破‘卡脖子’的关键创新技术，服务于国家和地方经济发展，迫切需要发挥人才的作用。” 一张 照片 引发的 惊悚 （ 磁场 起因的困惑） 正电子, positron; positive electron; antielectron，基本粒子的一种，带正电荷，质量和电子相等，是电子的反粒子。 Positron, also called positive electron, positively charged subatomic particle having the same mass and magnitude of charge as the electron and constituting the antiparticle of a negative electron. 由于“正、负”电子具有相同的质量、电荷，因此在磁场里的运动轨迹应该是“镜像对称”的。 然而，让人惊悚、细思极恐的是： 1932年，美国加州理工学院的安德森与内德梅耶在云雾室中充入过饱和的乙醚气，拍摄到了高能宇宙射线穿过铅板后，在磁场中长线了一对液滴线。即“正负电子对 positron-electron pair”的轨迹。即，下图： 从这张照片里可以看到，两个轨迹基本上都是圆形，所以可以推断： （1）该正负电子对的轨迹，应该基本在同一平面； （2）照相机镜头应该和该该正负电子对的运动平面垂直，没有非垂直夹角引起的“椭圆”投影变形。 细思极恐的是： 这两个圆形轨迹，直径大小有明显的差异。不是完全对称的镜像轨迹。 洛伦兹力 F = q v × B 到底针对正、负电子的初速度 v 不同，还是感受到的磁场 B 不同？ 从照片看，轨迹分裂后的一段时间，正、负电子的轨迹基本是对称的。即，初速度 v 的大小 v 应该是差不多的。下面是上图裁剪出的局部，再放大： 因此，最合理的解释也许是： 正、负电子在运动中，感受到的磁场的强度（磁感应强度）是不同的。 “磁场可能是依赖坐标系/参照系”的！ 目前认为磁场是电荷运动产生的，这样，磁场显然与运动有关。 多导体运动中的磁场，到底该怎么计算？麦克斯韦的经典电磁理论里没有进一步的说明。 是不是我想错了？ 相关资料： 正电子_百度百科 https://baike.baidu.com/item/%E6%AD%A3%E7%94%B5%E5%AD%90 Positron, subatomic particle, Britannica.com https://www.britannica.com/science/positron Positron, also called positive electron, positively charged subatomic particle having the same mass and magnitude of charge as the electron and constituting the antiparticle of a negative electron. The first of the antiparticles to be detected, positrons were discovered by Carl David Anderson in cloud-chamber studies of the composition of cosmic rays (1932). 中国科学院高能物理研究所，2010-04-15，云雾室 http://www.ihep.cas.cn/kxcb/kpcg/lztcq/201004/t20100415_2823066.html 1932年，安德森（Carl David Anderson，1905-1991）与内德梅耶（Seth Neddermeyer，1907-1988）将云室置入一个强磁场之中观察宇宙射线。宇宙射线进入云室后会留下轨迹，拍下轨迹的照片，即可用来进行分析。安德森当时每隔15秒钟使云室膨胀一次并拍摄照片。通过对1300张粒子轨迹照片的详细分析，发现有一种粒子的轨迹与当时已知的带电粒子的轨迹不一样。根据轨迹偏转的方向，可以判断这种粒子的电荷是正的，又根据轨迹曲率的大小，可推知这种粒子要比质子轻得多，且与电子的质量近乎相等。 SME张磊，2016-12-29，这是注定要拿诺贝尔奖的人，成功避开3次诺贝尔奖又算什么！ http://blog.sciencenet.cn/blog-2966991-1024151.html https://zhuanlan.zhihu.com/p/24632090 2019-04-22，感谢“中科院科学智慧火花”贴出评论：正负电子对在磁场中可能不是完全对称的镜像轨迹 http://blog.sciencenet.cn/blog-107667-1174944.html 2012-04-12，SI基本单位中安培定义的两种可能缺陷 http://idea.cas.cn/viewdoc.action?docid=4681 Biot-Savart law, physics, Britannica.com https://www.britannica.com/science/Biot-Savart-law MIT open Course, Physics II: Electricity and Magnetism https://ocw.mit.edu/courses/physics/8-02-physics-ii-electricity-and-magnetism-spring-2007/ MIT open Course, Biot-Savart's Law Ampere's Law https://ocw.mit.edu/high-school/physics/exam-prep/magnetic-fields/biot-savarts-law-amperes-law/ Biot-Savart Law http://hyperphysics.phy-astr.gsu.edu/hbase/magnetic/Biosav.html 感谢您的指教！ 感谢您指正以上任何错误！ 感谢您提供更多的相关资料！
面向云端融合计算环境的基于多移动Agent的混合协同管理环机制 Hybrid Collaborative Management Ring on Mobile Multi-Agent for Cloud-P2P Xiao-LongXu ， College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China Nik Bessis ， School of Computingand Mathematics, University of Derby, Derby DE22 1GB, UK Peter Norrington ， Institute for Research in Applicable Computing, University of Bedfordshire, Bedfordshire LU13JU, UK 收录于 IJAC Vol.13 No.6, 2016 文章简介 作为目前两种典型的网络计算模式，云计算和对等计算技术分别侧重于利用集群服务器端的资源和网络边缘节点的资源。 为了更加充分利用网络中集群服务器和边缘节点上各种可利用的潜在资源以及互联互通，在国家自然科学基金等项目的资助下，我们课题组提出将云计算和对等计算进行有效的融合，构建了一种基于多移动 Agent 的一种层次式云计算和 P2P 计算融合模型（称为 Cloud-P2P ），从而实现最大范围的分布式协作与资源共享。由于原来云计算和对等计算所采用的管理控制机制不再适用新型的云计算和对等计算融合计算环境，为保证 Cloud-P2P 环境中的任务执行效率和成功率，我们提出一套新的基于多移动 Agent 的复合协同管理环机制，如图1所示：将系统构建为核心环、云内环和若干个对等环；在每个环中采用节点两两协作监控以及结合移动 Agent 的串、并行巡弋方式来实现节点的状态和性能的分散监控，从而有效避免传统管理机制中的服务器端性能瓶颈与单点失效问题。在本文中，我们深入分析了云计算和 P2P 计算环境与节点情况，然后详细阐述了基于多移动 Agent 的云计算与对等计算融合模型和复合协同管理环机制以及环状网络拓扑结构的构建方法。 图1复合协同管理环示意图 图2复合协同管理环机制环状拓扑 作者简介 Xiao-Long Xu received the B. Sc. degree in computer and its applications, the M. Sc. degree in computer software and theories, and the Ph.D. degree in communications and information systems at Nanjing University of Posts Telecommunications, China in 1999, 2002 and 2008, respectively. He worked as a postdoctoral researcher at Center of Electronic Science and Technology, Nanjing University of Posts Telecommunications from 2011 to 2013. He is currently a professor in College of computer, Nanjing University of Posts Telecommunications, China. He is a senior member of China Computer Federation,China. He has published about 110 refereed journal and conference papers. His research interests include cloud computing, mobile computing, intelligent agent and information security. E-mail: xuxl@njupt.edu.cn (Corresponding author) ORCID iD: 0000-0001-6254-5864 Nik Bessis rec eived the B. A. degree from the Technological Educational Institute (TEI) of Athens, Greece, and the M. A. and the Ph. D.degrees at De Montfort University, Leicester, UK. He joined Department of Computer Science and Technology at University of Bedfordshire, UK as a Lecturerin 2001. In 2004 he was promoted to be senior lecturer and the postgraduate course manager. In 2009, he promoted to be a principal lecturer. In December 2010, he joined the University of Derby, UK, as a professor of computer science and led the research within the School. Since January 2011, he is the head of the distributed and intelligent Systems (DISYS) research group and the REFUOA11 leader. He has published over 165 works and has received three Best Papers Awards (2009, 2009 and 2012). His research interests include web-centric system developments, dynamic web applications, data integration, social networking, data analytics, and visualization and resource management. E-mail: n.bessis@derby.ac.uk Peter Norrington received the B. A. degree in linguistics and philosophy at the University of Sheffield, UK in 1990, the M. Sc. degree ininternet technologies at University of Bedfordshire, UK in 2004, and the Ph. D.degree in computer internet security at University of Bedfordshire, UK in 2009. He works for University of Bedfordshire, UK since 2008. He is now a Ph.D. research supervisor, higher education consultant and freelance. His research interests include Internet, information security and usability, and project and programme management. E-mail: p.norrington@gmail.com 原文出处 Xiao-LongXu, Nik Bessis, Peter Norrington. Hybrid Collaborative Management Ring onMobile Multi-agent for Cloud-P2P. InternationalJournal of Automation and Computing ,vol. 13, no. 6, pp. 541-551, 2016. 关键词 Cloud computing, peer-to-peer(P2P) computing, mobile multi-agent, integration, management . 全文链接 1）IJAC 官方网站 ： http://www.ijac.net/EN/abstract/abstract1837.shtml 2） Springer Link ： http://link.springer.com/article/10.1007/s11633-016-1002-6 3） 微信公众平台 ： http://mp.weixin.qq.com/s/ZQYV5jVA2Bvs0bvZyv3YLw
ISCCP (International Satellite Cloud Climatology Project) was established in 1982 as part of the World Climate Research Programme (WCRP) to collect and analyze satellite radiance measurements to infer the global distribution of clouds, their properties, and their diurnal, seasonal, and interannual variations. Data collection began on 1July 1983 and is currently planned to continue through 30 June 2010. The resulting datasets and analysis products are being used to improve understanding and modeling of the role of clouds in climate, with the primary focus being the elucidation of the effects of clouds on the radiation balance.These data can also used to support many other cloud studies, including understanding of the hydrological cycle. ISCCP Data Available ISCCP Data Products B3, BT, B1U, IS, TV, DX, C1, D1, C2, D2, FD-TOA, andetc..
I dreamed about clouds in college, and dreaded them. Why? I had nightmare, in which I was asked to write down equations for every cloud! I was so glad to wake up from the nightmare. Naturally, I was not going to touch the cloud. If you use cloud, you may want to read this article: http://www.npr.org/blogs/alltechconsidered/2014/04/25/306837205/you-love-the-cloud-but-it-may-not-be-as-secure-as-you-think
Encoding Words into Cloud Models from Interval-valued Data via Fuzzy Statistics and Membership Function Fitting Xiaojun Yang , Liaoliao Yan , Hui Peng , Xiangdong Gao Abstract When constructing the model of a word by collecting interval-valued data from a group of individuals, both interpersonal and intrapersonal uncertainties coexist. Similar to the interval type-2 fuzzy set (IT2 FS) used in the enhanced interval approach (EIA), the Cloud model characterized by only three parameters can manage both uncertainties. Thus, based on the Cloud model, this paper proposes a new representation model for a word from interval-valued data. In our proposed method, firstly, the collected data intervals are preprocessed to remove the bad ones. Secondly, the fuzzy statistical method is used to compute the histogram of the surviving intervals. Then, the generated histogram is fitted by a Gaussian curve function. Finally, the fitted results are mapped into the parameters of a Cloud model to obtain the parametric model for a word. Compared with eight or nine parameters needed by an IT2 FS, only three parameters are needed to represent a Cloud model. Therefore, we develop a much more parsimonious parametric model for a word based on the Cloud model. Generally a simpler representation model with less parameters usually means less computations and memory requirements in applications. Moreover, the comparison experiments with the recent EIA show that, our proposed method can not only obtain much thinner footprints of uncertainty (FOUs) but also capture sufficient uncertainties of words. Keywords Computing with words; Cloud model; Enhanced interval approach; Fuzzy statistics; Membership function fitting; Histogram Framework of the Proposed Method Step 1: Data collection. The datasets introduced in “ D. Wu, J. M. Mendel, and S. Coupland , “ Enhanced Interval Approach for Encoding Words Into Interval Type-2 Fuzzy Sets and Its Convergence Analysis ,” IEEE Transactions on Fuzzy Systems , vol. 20 , no. 3 , pp. 499-513 , Jun . 20 12 ” are used in our research. Step 2 : Data preprocessing. 1) Bad data processing 2) Outlier processing 3) Tolerance limit processing 4) Reasonable-interval processing Step 3 : Fuzzy statistics of data intervals. The fuzzy statistics is to compute the histogram of the m intervals. ( Fig. 4, red solid curve ) Fig. 4 . The fuzzy statistics histogram and fitting curve of the word “Some” . Step 4 : MF fitting using Gaussian curve function. The fuzzy statistical histogram obtained in Step 3 can be considered as the MF of the fuzzy opinions of a word. Nevertheless, in some situations such as in applications of CWW, the obtained fuzzy opinions may be manipulated later in arithmetic operations. In these cases, mathematically explicit and continuous forms of MFs may be a necessity. In order to develop a more parsimonious parametric model for a word, we fit the fuzzy statistical histogram data ( x s , m ( x s )) obtained in Step 3 using Gaussian curve function. ( Fig. 4, black dashed curve ) Step 5 : Representation of a word by a Cloud model. To handle the uncertainties of a word, we choose the Cloud model with the minimum number of parameters that best approximates the data to represent a word, so as to obtain a more parsimonious parametric model for a word. ( Fig.5 ) Fig. 5 . The representation of the word “Some” by a Cloud model . Experimental Results Fig. 6 . The fuzzy statistics histograms (red curves) and fitted curves (black curves) of all 32 words. Fig. 7 . The resulting Cloud models of all 32 words . Fig. 10 . The area (light-colored shaded area) and error (dark shaded areas) of the word “A lot”. The sum of dark shaded areas is defined as the error of an FOU. (a) The result of EIA, (b) the result of our proposed method. Fig. 11 . The areas and errors of the IT2 FS FOUs for all 32 words obtained by the EIA. Fig. 12 . The areas and errors of the Cloud model FOUs for all 32 words obtained by our proposed method. Fig. 13 . The visualized comparisons of the areas and errors between the EIA and our proposed method for all 32 words. (a) The areas, (b) the errors. Discussions and Conclusion Based on the Cloud model, we have developed a more parsimonious parametric model for a word from interval data. From the experimental results, we have observed that the proposed method can result in much thinner FOUs. 1) The representation of a Cloud model FOU is simpler than that of an IT2 FS FOU. It only needs three parameters to define a Cloud model FOU, whereas an IT2 FS FOU needs eight or nine parameters. “Generally a MF shape with simpler representation is preferred, especially when the parameters of the MF need to be optimized, because simpler representation usually means faster convergence”. Therefore, we should obtain a parsimonious parametric model with the minimum number of parameters that best approximates the data for a word. 2) The Cloud model FOUs are much thinner than the IT2 FS FOUs. Thinner FOUs may represent a more desirable tradeoff between uncertainty and accuracy, and thinner FOUs can be used to better distinguish close words . Experimental results show that the Cloud model FOUs for words are not only much thinner but also can capture sufficient uncertainties. The reason may be that, we approximate the fuzzy statistical histograms of the words using the Cloud models directly in our proposed method, while in the IA and EIA, each person’s data interval is assumed to be uniformly distributed but later a symmetrical triangle T1 MF, a left-shoulder T1 MF, or a right-shoulder T1 MF is used to approximate a uniformly distributed data interval. Constructing the models of words is the first step in the CWW paradigm. In this paper we have proposed a method of e ncoding w ords into Cloud models from i nterval-valued d ata using f uzzy s tatistics and MF f itting. Similar to the IT2 FS , the Cloud model is able to represent both interpersonal and intrapersonal uncertainties about collecting word data from a group of individuals. Based on the Cloud model, we have developed a more parsimonious parametric model for a word. The experimental results show that our proposed method can not only result in much thinner FOUs but also capture sufficient uncertainties of words. Thus, the Cloud model may be a suitable model for a word, and this may explore an other efficient way in the CWW and the representation of human knowledge and perceptions . Future researches will be concerned with constructing the system of CWW using Cloud models. Access this article: Knowledge-Based Systems, Available online 19 October 2013 http://www.sciencedirect.com/science/article/pii/S0950705113003250#f0020 http://dx.doi.org/10.1016/j.knosys.2013.10.014
The 2nd IEEE International Conference on Cloud Networking (IEEE CloudNet 2013) 云网络 Cloud Networking Accepted Full Papers: Vulnerability Evaluation for Securely Offloading Mobile Apps in the Cloud He Zhu (Carleton University, Canada); Changcheng Huang (Carleton University, Canada); James Yan (Carleton University, Canada) Trust Management System for Opportunistic Cloud Services Eric Kuada (Aalborg University Copenhagen, Denmark) Automatic Server Role Identification for Cloud Infrastructure Construction Shinya Kitajima (Fujitsu Laboratories Ltd., Japan); Tetsuya Uchiumi (Fujitsu Laboratories Ltd., Japan); Shinji Kikuchi (Fujitsu Laboratories Ltd., Japan); Yasuhide Matsumoto (Fujitsu Laboratories Ltd., Japan) Zeppelin - A Third Generation Data Center Network Virtualization Technology based on SDN and MPLS James Kempf (Ericsson Research, USA); Ying Zhang (Ericsson Research, USA); Ramesh Mishra (Ericsson, USA); Neda Beheshti (Ericsson Research, USA) SAFE: Structure-Aware File and Email Deduplication for Cloud-based Storage Systems Daehee Kim (University of Missouri-Kansas City, USA); Baek-Young Choi (University of Missouri - Kansas City, USA); Sejun Song (Texas AM University, college station, USA) Virtual Network Embedding with Collocation:Benefits and Limitations of Pre-Clustering Carlo Fuerst (T-Labs / TU Berlin, Germany); Stefan Schmid (T-Labs TU Berlin, Germany); Anja Feldmann (TU-Berlin, Germany) Quality-of-Service (QoS) for Virtual Networks in OpenFlow MPLS Transport Networks Ashiq Khan (NTT DOCOMO, Inc., Germany); Wolfgang Kiess (DOCOMO Euro-Labs, Germany); David Perez-Caparros (DOCOMO Euro-Labs, Germany); Joan Triay (DOCOMO Communications Laboratories Europe, Germany) An Efficient Flow Cache algorithm with Improved Fairness in Software-Defined Data Center Networks Bu Sung Lee (Nanyang Technological University, Singapore); Renuga Kanagavelu (A star Data Storage Institute, Singapore); Khin Mi Mi Aung (A*STAR, Data Storage Institute, Singapore) Inter and Intra Cloud Networking Gateway as a Service Marouen Mechtri (TELECOM SudParis, France) Networking in a Virtualized Environment: the TCP case Guillaume Urvoy-Keller (Université de Nice Sophia-Antipolis, France); Dino Martin Lopez Pacheco (University of Nice (EPU), France); Hai S Ha (University of Nice, France) End-to-End Privacy Policy Enforcement in Cloud Infrastructure Stéphane Betgé-Brezetz (Alcatel-Lucent Bell Labs, France); Guy-Bertrand Kamga (Alcatel-Lucent Bell Labs, France); Marie-Pascale Dupont (Alcatel-Lucent Bell Labs, France); Aoues Guesmi (Alcatel-Lucent Bell Labs, France) CLAudit: Planetary-Scale Cloud Latency Auditing Platform Ondrej Tomanek (Czech Technical University in Prague, Czech Republic); Lukas Kencl (Czech Technical University in Prague, Czech Republic) Classification of Applications in HTTP Tunnels Gajen Piraisoody (Carleton University, Canada); Changcheng Huang (Carleton University, Canada); Biswajit Nandy (Solana Networks, Canada); Nabil Seddigh (Solana Networks, Canada) VirtualTransits: a Plateform for Network Virtualization across Data Centers Mon-Yen Luo (National Kaohsiung University of Applied Sciences, Taiwan); Jun-Yi Chen (National Kaohsiung University of Applied Sciences, Taiwan) Migration-based Virtual Machine Placement in Cloud Systems Kangkang Li (Temple University, USA); Huanyang Zheng (Temple University, USA); Jie Wu (Temple University, USA) Ethernet Routing for Large Scale Distributed Data Center Fabrics David Allan (Ericsson, USA); Janos Farkas (Ericsson, Hungary); Panagiotis Saltsidis (Ericsson, Sweden); Jeff Tantsura (Ericsson, USA) MDP Based Optimal Policy for Collaborative Processing using Mobile Cloud Computing Mona Nasseri (University of Toledo, USA); Robert Green, II (Bowling Green State University, USA); Mansoor Alam (EECS Department, USA) QCN with Delay-based Congestion Detection for Limited Queue Fluctuation in Data Center Networks Yuki Tanisawa (Kansai University, Japan); Miki Yamamoto (Kansai University, Japan) Crosslayer Cooperation to Boost Multipath TCP Performance in Cloud Networks Matthieu Coudron (UPMC, France); Stefano Secci (University Pierre et Marie Curie - Paris 6, France); Guy Pujolle (University Pierre et Marie Curie - Paris 6, France); Patrick Raad (UPMC, France); Pascal Gallard (Non Stop Systems, France) An Optimal Control Policy in a Mobile Cloud Computing System Based on Stochastic Data Xue Lin (University of Southern California, USA); Yanzhi Wang (University of Southern California, USA); Massoud Pedram (University of Southern California, USA) Elasticity-aware Virtual Machine Placement for Cloud Datacenters Kangkang Li (Temple University, USA); Jie Wu (Temple University, USA); Adam Blaisse (Temple University, USA) Accepted Short Papers: D2ENDIST-FM: Flow Migration in Routing of OpenFlow-based Cloud Networks Wei-Chu Lin (National Chiao Tung University, Taiwan); Gen-Hen Liu (National Chiao Tung University, Taiwan); Kuan-Tsen Kuo (National Chiao Tung University, Taiwan); Charles H.-P. Wen (National Chiao Tung University, Taiwan) A Comparative Study of Applying Real-Time Encryption in Cloud Computing Environments Faraz Fatemi Moghaddam (Staffordshire University, Malaysia); Omidreza Karimi (Staffordshire University, Malaysia); Maen Alrashdan (Asia Pacific University (APU), Malaysia) Adaptive provisioning of Connectivity-as-a-Service for Mobile Cloud Computing Ines Ayadi (Telecom Paristech, France); Nomie Simoni (Telecom Paristech, France) Request Dispatching for Cheap Energy Prices in Cloud Data Centers Hiroshi Yamada (Tokyo University of Agriculture and Technology, Japan); Takumi Sakamoto (Keio University, Japan); Hikaru Horie (Keio University, Japan); Kenji Kono (Keio University, Japan) A Cloud Data Center Optimization Approach using Dynamic Data Interchanges Efstratios Rappos (University of Applied Sciences of Western Switzerland, Switzerland); Stephan Robert (HEIG-Vd, Switzerland); Rudolf H Riedi (University of Applied Sciences of Western Switzerland, Fribourg, Switzerland) Service-Oriented Trust and Reputation Management System for Multi-Tier Cloud Hasen Nicanfar (The University of British Columbia, Canada); Mohsen Amiri (University of British Columbia, Canada); Chunsheng Zhu (The University of British Columbia, Canada); Peyman TalebiFard (The University of British Columbia, Canada); Victor CM Leung (The University of British Columbia, Canada); Panos Nasiopoulos (University of British Columbia, Canada) Autonomic Scaling of Cloud Computing Resources using BN-based Prediction Models Abul Bashar (Prince Mohammad Bin Fahd University, Saudi Arabia) An Architecture for Automatic Deployment of Service on Cloud Environment Lenin Abadie Otero (University of Pernambuco, Brazil); Tércio de Morais (Federal University of Alagoas, Brazil); Nelson Souto Rosa (Federal University of Pernambuco, Brazil); Silvio Meira (Universidade Federal de Pernambuco, Brazil) Performance vs Cost for Windows and Linux Platforms in Windows Azure Cloud Sasko Ristov (Ss. Cyril and Methodius University, Macedonia, the former Yugoslav Republic of); Marjan Gusev (Ss. Cyril and Methodius University, Macedonia, the former Yugoslav Republic of) Achieving Elasticity for Cloud MapReduce Jobs Khaled Salah (Khalifa University of Science, Technology and Research (KUSTAR), UAE) Understanding TCP Cubic Performance in the Cloud: a Mean-field Approach Sonia Belhareth (INRIA, I3S, CNRS, Université de Nice Sophia, France); Lucile Sassatelli (I3S - CNRS Universite de Nice UMR, France); Denis Collange (Orange Labs, France); Dino Martin Lopez Pacheco (University of Nice (EPU), France); Guillaume Urvoy-Keller (Université de Nice Sophia-Antipolis, France) New Control Plane in 3GPP LTE/EPC Architecture for On-Demand Connectivity Service Siwar Ben Hadj Said (Orange Labs, France); Malla Reddy Sama (Orange Labs, France); Karine Guillouard (Orange Labs, France); Lucian Suciu (France Télécom RD, France); Gwendal Simon (Institut Telecom - Telecom Bretagne, France); Xavier Lagrange (Institut Mines Telecom / Telecom Bretagne, France); Jean-Marie Bonnin (Institut Mines Telecom / Telecom Bretagne, France)
Anton Beloglazov, Jemal H. Abawajy, Rajkumar Buyya: Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing . Future Generation Comp. Syst. 28(5): 755-768 (2012) 1. 本文首先提出了一个节能云计算(energy-efficient Cloud computing)的架构框架(architectural framework)和原则。 下图 系统架构图 (S3.1) 如上图所示，分为四层，其中Green Service Allocator一层角色众多，承担了对服务用户请求进行分析、SLA协商、服务定价、VM调度、VM管理等功能。 2. (S3.2)中则提出了Power model，建立了能耗与CPU利用率的管理： 其中Pmax是服务器完全利用时最高的能耗，k是空闲服务器效率的能源比例（通常为70%)，u则是CPU的利用率。 (能耗跟内存、磁盘、网络使用情况也有关系，但主要与CPU有关，所以上面的公式里只体现了CPU利用率，这个论断见S3.2) 3. 在此基础上，(S4)提出了资源供给与分配的算法，以改进云计算环境的节能。该算法是启发式的(heuristics)，可以在确保满足客户QoS的前提下，使得数据中心的节能效果得到改进。 (S4.1) “VM placement”： 是关于针对创建新的VM请求，分配到哪台PM使得能耗增加最小的问题。此问题被建模为“bin packing problem with variable bin sizes and prices”，使用了修改版的“Best Fit Decreasing (BFD)”算法。 (S4.2)“VM selection”: 是关于如何优化当前的VM分配，以优化能耗的问题。主要分成两个步骤：首先选择迁移对象（一组VMs）；然后使用MBFD算法，确定选出的迁移对象将被放置到哪些PMs上。 4. 针对“何时选择哪些迁移对象的问题”，(S4.2)中提出了三种选择策略（基本思想差不多）： the minimization of migrations(MM) policy the highest potential growth(HPG) policy the random choice(RC) policy 以MM策略为例，下图是该策略的规则： 大意如下： 如果某一台PM的CPU利用率过高超出了上限，则找出一组个数最少的VM，这组VM就是迁移对象； 如果某一台PM的CPU利用率过低达不到下限，则该PM上所有VM都是迁移对象。 5. (S5)进行了实验验证 (S5.1)中介绍了性能度量指标： total energy consumption SLA violation percentage number of VM migrations initiated by the VM manager average SLA violation 实验是在模拟平台(CloudSim toolkit)上进行的。具体的实验过程和结果详见原文。 6. 作者在(S6)提出了相关的open challenges optimization of VM placement according to the utilization of multiple system resources optimization of virtual network topologies optimization of thermal states and cooling system operation efficient consolidation of VMs for managing heterogeneous workloads a holistic approach to energy-aware resource management via: http://www.cnblogs.com/ykt/archive/2013/02/14/2892562.html
Most every hot topic coming to my mind these days, I will check our social media system to see how social media reflects it. Word clouds are intriguing vehicles to present the common social image. Most word clouds generated by other systems are based on statistics of keywords mentioned together with the topic query, but our system can mine and present word clouds of various types of classified data, including positive/negative emotions towards a topic, pros and cons (or likes and dislikes) of the topic, etc. They reflect the social images of a topic from different perspectives. Today is Christmas Day, so it naturally becomes the topic of the day. The last post presents the social image of Xmas in Chinese social media , how about the word clouds in the English social media world? How about French? Here it is, as a way of celebrating the holiday season. Emotion-wise, as expected, Christmas time is full of joy and happy spirit, reflected by all the big green fonts of the positive sentiments expressed in social media. Negative sentiments do exist, but they are much fewer and scattered in smaller (red) fonts. The major complaints seem to be caused by the expectations not being met, disappointment in lack of fun and excitement. As for pros and cons, also as expected, pros are overwhelming. Both the emotion word cloud above and the pros-cons word cloud below seem to be bothered by some issues from some Christmas sweater party. In the French world known for its romance and passion, aimer+ and aimer- (love or not love) is a most striking sentiment word pair.. 【置顶：立委科学网博客NLP博文一览（定期更新版）】
CLOUD DELPHI METHOD， International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems(IJUFKS) Volume: 20, Issue: 1 (2012) pp. 77-97 DOI: 10.1142/S0218488512500055 2010年4月6日投稿 2010年10月4日一审结果，大修 2010年11月20日提交第一次修改版 2011年10月4日accepted with revision 2011年11月2日提交第二次修改版 2011年11月17日Accept 2012年2月7日在线出版 Abstract: Group decision making is an important category of problem solving techniques for complicated problems, among which the Delphi method has been widely applied. In this paper an improved Delphi method based on Cloud model is proposed in order to deal with the fuzziness and uncertainty in experts' subjective judgments. The proposed Cloud Delphi Method (CDM) describes experts' opinions by Cloud model and we aggregate the experts' Cloud opinions by synthetic algorithm and weighted average algorithm. Another key point of CDM is to stabilize and accommodate the individual fuzzy estimates by the defined stability rules rather than having to force them to converge, or reduce. The Cloud opinions and aggregation results can be exhibited in a graphically way leading experts to judge intuitively and it can decrease the number of repetitive surveys and/or interviews. Moreover, it is more scientific and easier to represent experts' opinion base on Cloud model which can combine fuzziness and uncertainty well. A numerical example is examined to demonstrate applicability and implementation process of CDM. Keywords: Decision making; Cloud Delphi method; Cloud model; fuzzy; uncertai nty 全文链接
From wikipedia: Criticism of the term This article's Criticism or Controversy section(s) may mean the article does not present a neutral point of view of the subject . It may be better to integrate the material in those sections into the article as a whole. (March 2010) During a video interview, Forrester Research VP Frank Gillett expresses criticism about the nature of and motivations behind the push for cloud computing. He describes what he calls cloud washing in the industry whereby companies relabel their products as cloud computing resulting in a lot of marketing innovation on top of real innovation. The result is a lot of overblown hype surrounding cloud computing. Gillett sees cloud computing as revolutionary in the long term but over-hyped and misunderstood in the short term, representing more of a gradual shift in our thinking about computer systems and not a sudden transformational change. Larry Ellison , CEO of Oracle Corporation has stated that cloud computing has been defined as everything that we already do and that it will have no effect except to change the wording on some of our ads. Oracle Corporation has since launched a cloud computing center and worldwide tour. Forrester Research Principal Analyst John Rymer dismisses Ellison's remarks by stating that his comments are complete nonsense and he knows it. Richard Stallman said that cloud computing was simply a trap aimed at forcing more people to buy into locked, proprietary systems that would cost them more and more over time. It's stupidity. It's worse than stupidity: it's a marketing hype campaign, he told The Guardian. Somebody is saying this is inevitable and whenever you hear somebody saying that, it's very likely to be a set of businesses campaigning to make it true.
Cloud Indexing by Seth Maislin (September 24, 2010 - 10:14 GMT) Last week, Seth Earley blogged about the inefficacy of social tagging , but there's one scenario in which social tagging will breathe new life into an esoteric, 200-year industry: book indexing. I've written hundreds of book indexes, presided over the American Society for Indexing, managed an international indexing partnership, taught courses, established standards, built tools, and consulted with a lot of influential folks, so trust me when I tell you that it pains me to see this happening. I believe with every fiber of my professional being that the human work of subject indexing is and will continue to be superior in quality to every alternative ever imagined. Oh well. There is just too much information to index by hand, period. Books, periodicals, websites, blogs, messages, and documents are being produced or transformed too quickly for humans to keep pace, regardless of training and tools. Perhaps in response, the use of search algorithms becomes ever more popular, while overly optimistic expectations of retrieval quality grows increasingly preposterous. A more realistic response would be an increase in subject indexers' fees -- after all, demand is outpacing supply at an astounding rate -- but indexers haven't experienced a rate increase since the 1990s. The truth is that editorial indexing and all smart hands-on tagging is disappearing in favor of automatic approximations. And it is a reasonable argument that the substandard tagging of millions of pages and documents is better than leaving most of them without any subject metadata whatsoever. As industries grow more and more interested in taxonomy and semantics and autoclassification, it is the book publishing industry that will experience the most pronounced change. Printed books will disappear, and electronic book readers will become networked. It is intriguing that books, with their inherent depth and complexity, can be completely deconstructed into chapters, pages, and sentences, and then subjected to the same social ingenuity applied to photographs and songs. Individuals will be able to mark up (tag) their copies of books, and those notes will feed into a cloud of content that can be shared among all readers. Following in the footsteps of social bookmarking comes social book- marking . Readers of popular books (and in particular works of fiction) will enjoy having access to deep and faceted lists of keywords, for use in both search- and browse-based user environments. These cloud indexes can be combined arbitrarily to create global indexes for entire book collections, personal or otherwise, and these global indexes can be refined using standard book metadata (e.g., publisher), personal metadata (e.g., date of first read), cloud metadata (e.g., Lady Gaga's favorites), and thesauri or taxonomies. Books that aren't popular are doomed in the cloud indexing paradigm. Publishers and authors must work hard to encourage readers to contribute, especially because traditional SEO techniques won't work well with book content. Book publishing will remain as competitive as always. And for the first time in the history of book publishing, it will be impossible to judge a book by its cover. Instead, we'll use the index.
Cloud Computing refers to shared computing resources accessed remotely as services (via the Internet, Intranet, dedicated network etc) , that can be provisioned on demand and billed according to usage . Most current Cloud Service providers (such as Amazon and Google) primarily gear their services at SMEs. However, many larger Enterprises are now beginning to think of: a) how they can implement Cloud Computing internally - Cloud Private b) how they can extend their Cloud Private to make use of available Cloud services from external vendors - Hybrid Cloud c) or how they can offer their own Cloud services - Cloud Public The term Cloud refers to on-demand services accessed over the Internet, however these services can be classified into 3 layers , each layer defining a Cloud of services on its own: 1. The Infrastructure Cloud provides on-demand access to server capacity, storage space, network connection and middleware software , with flexibility to increase or decrease capacity as required 2. IT Services or the Service Cloud provides a set of software components from which the Independent Software Vendors (ISVs) can create end-user applications. These components are generally exposed as web services. 3. Software-as-a-Service or the Application Cloud is a software application that is licensed for use as a service provided to customers on demand Usually, the term Cloud Computing refers to the Infrastructure Cloud. This set of Cloud services includes on demand access to server, storage, network services as well as the ability to design and deploy complex IT configuration which supports the business applications. Platform- as-a-Service is another service within Cloud Computing which offers developers a full suite of tools to design their applications without struggling with deployment details. When companies are considering whether or not to build a Cloud Private (also called Internal Cloud), it refers to a Cloud Computing platform. What has Virtualisation got to do with Cloud Computing? Cloud Computing relies on Virtualisation. However it requires an additional layer of automation and intelligence to deliver the on demand services. Virtualisation is the enabling technology whilst a Cloud Computing platform provides the level of automation and autonomics required to offer a zero touch self service Data Centre to its users. Public Cloud, Private Cloud and Cloud Hybrid Cloud Computing Principles: Shared computing resources, virtualised and accessed as a service Public Cloud Open market for on demand computing and IT resources Amazon, Google, IBM... Huge adoption by SMEs and currently tested by Enterprise However Maturing and Limited SLA Reliability and availability to be improved Security to be demonstrated Trust and confidence to be acquired Corporates and large Enterprises have existing Data Centres running their mission critical applications Large Enterprises are not likely to move their core or strategic applications onto Public Cloud in the short to medium time frame Private Cloud For Enterprises and Corporates with large scale IT For their core and strategic applications To take Cloud Computing benefits Data Centre consolidation and reduction of Capex Opex reduction by automation Flexibility, cycle time improvement and internal quality of service Cloud Hybrid Enterprises will continue to test and adopt Public Cloud Overflow management and peak adaptation via Public Cloud Hybrid Cloud = Private Cloud with connections to Public Cloud Cloud Private - internal and secure for your Enterprise. The Benefits of Cloud Private Cloud Private enables a more dynamic and cost effective use of IT infrastructure and resources. The top line benefits to your organisation include: Reduced Capital investment On demand scalability scale up when you need it, scale back in a downturn Get processes right first time around Reduce business process cycles Location independence for customers and Data Centre assets Lower price point for hardware/software usage Pay as You Go (PAYG) model Increased Enterprise agility Usage billing (PAYG model in IT Units) Automated provisioning Reference: cloud essential: http://www.cloudessential.com/cloudcomputing/ Cloud Computing Explained.pdf
维基百科(Wikipedia.org)对云计算的定义: 云计算是分布式计算技术的一种,其最基本的概念,是通过网络将庞大的计算处理程序自动分拆成无数个较小的子程序,再交由多部服务器所组成的庞大系统进行搜寻和计算分析,最后将处理结果回传给用户.通过这项技术,网络服务提供者可以在数秒内,处理数以千万计甚至亿计的信息,达到和超级计算机同样强大效能的网络服务. 加州大学伯克利分校(University of California at Berkeley)的Michael Armbrust等对云计算的定义: 云计算包括互联网上各种服务形式的应用以及这些服务所依托数据中心的软硬件设施,这些应用服务一直被称作软件即服务(SaaS),而数据中心的软硬件设施就是所谓的云, 云计算就是SaaS和效用计算.云分为公共云(Public Cloud)和私有云(Private Cloud). 云计算（Cloud computing）是一种基于互联网的超级计算模式,其原理非常类似于网格计算.它是把存储在大量分布式计算机产品中的大量数据和处理器资源整合在一起协同工作.作为一种新兴的共享基础架构的方法,它可以将巨大的系统池连接在一起以提供各种IT 服务.这使得企业能够将资源切换到需要的应用上,根据需求访问计算机和存储系统. 云安全联盟(Cloud Security Alliance, CSA)的研究报告提出的SPI模型(SPI Model)把云计算的服务形式分为基础架构即服务(Infrastructure as a Service,IaaS),平台即服务(Platform as a Service,PaaS)和软件即服务(Software as a Service,SaaS)三大类。开放云宣言组织(Open Cloud Manifesto)则根据研究的需要把云计算细分成终端用户―云、企业―云―终端用户、企业―云(综合)、企业―云―企业、企业―云(便携式)、私有(内部)云等6种模式。 作为网络时代的新计算形式,云计算是以数据、用户和服务为三大中心为导向的.在功能方面,真正的云计算平台应该能具备以下三方面的功能特性. ⑴提供资源――包括计算、存储及网络资源,需要服务提供者架设出规模巨大的全球化的数据库及存储中心,能够实现海量的存储、出色的安全性和高度的隐私性和可靠性.此外,它还应是高效的、低价的、节省能源的. ⑵提供动态的数据服务――包括原始数据、半结构化数据和经过处理的结构化数据,一个优秀的云计算架构一定要有提供大规模数据存储、分享、管理、挖掘、搜索、分析和服务的智能. ⑶提供云计算平台――包括软件开发API、环境和工具.云计算需要真正形成一个有生命力、有黏性、可持续发展的生态系统.
from http://www.bnl.gov/newsroom Extensive field study may offer clues to climate change, weather UPTON, NY, During May and June 2009, scientists from the U.S. Department of Energys (DOE) Brookhaven National Laboratory, Argonne National Laboratory, and the University of Colorado at Boulder will use high-tech scanners analogous to those in medical settings to make observations of clouds. The research, conducted at the Climate Research Facility (ACRF) in Ponca City, Oklahoma, run by DOEs Atmospheric Radiation Measurement (ARM) program, could lead to more accurate weather forecasts and predictions about climate. Clouds play a critical role in Earths weather and climate, said Brookhaven atmospheric scientist Dong Huang, lead researcher for this study. But poor understanding of clouds has long limited scientists ability to make accurate predictions about weather and climate change. One major challenge is the shear scale of the problem: Cloud processes occur on spatial scales ranging from sub-micrometers (millionths of a meter) to thousands of kilometers. The typical probes used by scientists sample a tiny volume. Using these methods, it would take hundreds of years to take readings from an entire cloud, while the typical lifetime of a cloud is just tens of minutes, said ARM chief scientist Warren Wiscombe, a co-investigator on this study. To scan a larger area in a short time, the team will use a novel cloud tomography approach to reconstruct three-dimensional cloud structure. Our approach is very similar to x-ray computed tomography (CT), used by doctors to diagnose disease, but this time the patients are clouds, said Huang. A CT scanner obtains x-ray data of the body slice by slice using detectors that rotate around the patient. Similarly a cloud tomography system uses multiple microwave sensors to scan clouds from several distinct ground locations. The interior structure of a cloud can then be inferred from the resultant radiometric measurements using sophisticated algorithms. The scientists will use a network of five microwave sensors to probe clouds thermal emission, or release of heat energy, along with two cloud radars, a variety of optical and infrared sensors, and weather balloons to measure other characteristics. Using this combination of instrumentation, we will be able to obtain three-dimensional maps of the distribution of clouds, atmospheric moisture, and other characteristics over a domain of about 10 kilometers, said Huang. The quality of the cloud and moisture reconstructions will be evaluated using concurrent cloud measurements collected by a research aircraft operated by another field experiment led by Brookhaven atmospheric scientist Andy Vogelmann. This study will collect long-term statistics on low-optical-depth clouds. The combined data will enable scientists to better understand the role of clouds in regulating Earths radiation energy budget or how the planet absorbs and re-radiates energy from the sun. These data will also be used to assess the validity of how various cloud processes are represented in computer models of cloud behavior. The combination of intensive field experiments, long-term observations, and modeling will provide important insights that could directly benefit weather forecasting and climate modeling, Huang said. This research is supported by DOEs Office of Science (Office of Biological and Environmental Research). Note to local editors: Dong Huang lives in Long Island, New York. Related Links: ACRF: http://www.arm.gov/acrf/ ARM: http://www.arm.gov/about/
The Open Cloud Consortium (OCC): supports the development of standards for cloud computing and frameworks for interoperating between clouds; supports the development of benchmarks for cloud computing; supports open source software for cloud computing; manages a testbed for cloud computing called the Open Cloud Testbed; sponsors workshops and other events related to cloud computing. 具体情况见网站: http://www.opencloudconsortium.org/index.html
标签: Cloud
