伯克利视角：云计算潜力与变革——云端服务详解

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云计算，一个革命性的概念，源自Berkeley可靠自适应分布式系统实验室的研究报告，其核心理念在于将计算能力转化为一种可共享的公共设施，从而彻底改变信息技术产业的格局。这篇论文深入探讨了云计算的多个方面，包括： 1. 基本概念：云计算的核心是将计算力作为一种服务，用户可以根据需求即时获取，按需付费，无需关心底层基础设施的细节。它将传统的软件购买模式转变为基于订阅的模式，使得软件服务变得更加吸引人。 2. 优势与潜力：云计算消除了企业对大规模硬件投资和维护团队的依赖。它允许开发者以较低的初始成本快速推出创新服务，避免了过度或不足部署带来的资源浪费和商业机会损失。同时，它显著提升了大规模并行任务处理的能力，通过弹性的资源分配，降低了使用成本。 3. 服务类型：云计算包括两种主要形式：一是软件即服务（SaaS），用户通过网络直接使用应用程序；二是公共云，即通过数据中心的硬件和系统软件向公众提供按需付费的服务。这两种形式都是云计算的实践体现。 4. 发展趋势：论文探讨了云计算的现状以及未来的机遇和挑战。随着技术的进步，云计算将继续推动IT行业的创新，如边缘计算、人工智能和物联网的融合，以及数据安全和隐私保护等问题。 5. 翻译与审校：这篇报告由中国电子学会云计算专家委员会组织翻译和审校，旨在让更多人了解Berkeley的观点。报告的翻译工作由姚宏宇博士负责，经过多位专家的校订，尽管可能存在一些错误或不准确之处，但其价值仍得到了专家的认可。 云计算的发展不仅改变了企业的IT策略，也正在重塑消费者体验，它的普及预示着IT行业将迎来一场深刻的变革。理解并掌握云计算的基本原理和应用策略，对于企业和个人而言都至关重要。

Above the Clouds: A Berkeley View of Cloud Computing

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Above the Clouds: A Berkeley View of Cloud Computing Michael Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy Katz, Andy Konwinski, Gunho Lee, David Patterson, Ariel Rabkin, Ion Stoica, and Matei Zaharia (Comments should be addressed to abovetheclouds@cs.berkeley.edu) UC Berkeley Reliable Adaptive Distributed Systems Laboratory  http://radlab.cs.berkeley.edu/ February 10, 2009 KEYWORDS: Cloud Computing, Utility Computing, Internet Datacenters, Distributed System Economics 1 Executive Summary Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of th IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Developers with innovative ideas for new Internet services no longer require the large capital outlay in hardware to deploy their service or the human expense to operate it. They need not be concerned about over provisioning for a service whose popularity does not meet their predictions, thus wasting costly resources, or under provisioning for one that becomes wildly popular, thus missing potential customers and revenue. Moreover, companie with large batch-oriented tasks can get results as quickly as their programs can scale, since using 1000 servers for on hour costs no more than using one server for 1000 hours. This elasticity of resources, without paying a premium fo large scale, is unprecedented in the history of IT. Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to a Software as a Service (SaaS). The datacenter hardware and software is what we will call a Cloud. When a Cloud i made available in a pay-as-you-go manner to the general public, we call it a Public Cloud; the service being sold i Utility Computing. We use the term Private Cloud to refer to internal datacenters of a business or other organization not made available to the general public. Thus, Cloud Computing is the sum of SaaS and Utility Computing, but doe not include Private Clouds. People can be users or providers of SaaS, or users or providers of Utility Computing. W focus on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SaaS Users. From a hardware point of view, three aspects are new in Cloud Computing. 1. The illusion of infinite computing resources available on demand, thereby eliminating the need for Cloud Com puting users to plan far ahead for provisioning. 2. The elimination of an up-front commitment by Cloud users, thereby allowing companies to start small and increase hardware resources only when there is an increase in their needs. 3. The ability to pay for use of computing resources on a short-term basis as needed (e.g., processors by the hou and storage by the day) and release them as needed, thereby rewarding conservation by letting machines and storage go when they are no longer useful.

Above_the_Clouds_A_Berkeley_View_of_Clou.pdf

2020-01-26 上传
Above the Clouds: a Berkeley view of Cloud Computing

Above the Clouds-- A Berkeley View of Cloud Computing.pdf

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很好的介绍云计算的文章， berkeley一帮科研人员的观点，值得一看

bioinformatics-in-the-clouds.rar_bioinformatics_cloud learning_c

2022-07-13 上传
【标题】"bioinformatics-in-the-clouds.rar" 提供了一个关于生物信息学在云端应用的学习资源，重点关注了“云学习”这一主题，并结合了“c”可能代表的计算机科学元素。这个压缩包中的核心内容是“bioinformatics_...

Research Advances in Cloud Computing-Springer(2017).pdf

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Cloud computing is a novel computing paradigm which has changed the way enterprise or Internet computing is performed. Today, for almost all the sectors in the world, cloud computing is synonym to on-...

# 多帧融合 def multi_frame_fusion(point_clouds): # 使用NDT和扩展卡尔曼滤波进行多帧融合 # 实现多帧点云数据的运动补偿和配准 # 返回融合后的点云 fused_point_cloud = ... return fused_point_cloud 上述代码中，这里的...需要写什么信息

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initial_point_cloud = point_clouds[0] # 初始点云作为参考 fused_point_cloud = initial_point_cloud for i in range(1, len(point_clouds)): current_point_cloud = point_clouds[i] # 使用ICP算法进行配准 ...

var s2Sr = ee.ImageCollection('COPERNICUS/S2'); var s2Clouds = ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY'); var START_DATE = ee.Date('2018-01-01'); var END_DATE = ee.Date('2018-12-31'); var MAX_CLOUD_PROBABILITY = 65; function maskClouds(img) { var clouds = ee.Image(img.get('cloud_mask')).select('probability'); var isNotCloud = clouds.lt(MAX_CLOUD_PROBABILITY); return img.updateMask(isNotCloud); } // The masks for the 10m bands sometimes do not exclude bad data at // scene edges, so we apply masks from the 20m and 60m bands as well. // Example asset that needs this operation: // COPERNICUS/S2_CLOUD_PROBABILITY/20190301T000239_20190301T000238_T55GDP function maskEdges(s2_img) { return s2_img.updateMask( s2_img.select('B8A').mask().updateMask(s2_img.select('B9').mask())); } // Filter input collections by desired data range and region. var criteria = ee.Filter.and( ee.Filter.bounds(table), ee.Filter.date(START_DATE, END_DATE)); s2Sr = s2Sr.filter(criteria).map(maskEdges); s2Clouds = s2Clouds.filter(criteria); // Join S2 SR with cloud probability dataset to add cloud mask. var s2SrWithCloudMask = ee.Join.saveFirst('cloud_mask').apply({ primary: s2Sr, secondary: s2Clouds, condition: ee.Filter.equals({leftField: 'system:index', rightField: 'system:index'}) }); var s2CloudMasked = ee.ImageCollection(s2SrWithCloudMask).map(maskClouds).median(); var rgbVis = {min: 0, max: 3000, bands: ['B4', 'B3', 'B2']}; var rgbVis = {min: 0, max: 3000, bands: ['B4', 'B3', 'B2']}; Map.addLayer( s2CloudMasked, rgbVis, 'S2 SR masked at ' + MAX_CLOUD_PROBABILITY + '%', true);将合成图像的全波段下载

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要将合成图像的全波段下载，可以使用 `Export.image.toDrive` 方法将影像导出为 Google Drive 上的 GeoTIFF 文件，然后在本地使用 GIS 软件打开。以下是一个示例代码，可以将中值影像导出为 GeoTIFF 文件： ...

// Load Sentinel-2 TOA reflectance data. var sentinel = ee.ImageCollection('COPERNICUS/S2') .filterDate('2019-01-01', '2019-12-31') .filterBounds(table) .map(function(image) { var cloud_mask = ee.Image(0).where( image.select('QA60').bitwiseAnd(1<<10), 1).rename('cloud_mask'); var cloud_probability = image.select('QA60').bitwiseAnd(1024).rightShift(10).rename('cloud_probability'); var cloud_shadow_probability = image.select('QA60').bitwiseAnd(2048).rightShift(11).rename('cloud_shadow_probability'); var cloud_mask_combined = cloud_mask.or(cloud_probability.gt(20)).or(cloud_shadow_probability.gt(20)); return image.addBands(cloud_mask_combined); }) .map(function(image) { return image.clip(table); }); // Function to mask clouds using the Sentinel-2 cloud mask. var maskClouds = function(image) { var cloudMask = image.select('cloud_mask').not(); return image.updateMask(cloudMask); }; // Function to calculate the NDVI. var calculateNDVI = function(image) { var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi'); return image.addBands(ndvi); }; // Function to calculate the EVI. var calculateEVI = function(image) { var evi = image.expression( '2.5 * (nir - red) / (nir + 6 * red - 7.5 * blue + 1)', { 'nir': image.select('B8'), 'red': image.select('B4'), 'blue': image.select('B2') }).rename('evi'); return image.addBands(evi); }; // Apply the cloud mask, calculate the NDVI and EVI, and combine the bands. var sentinel_ndvi_evi = sentinel .map(maskClouds) .map(calculateNDVI) .map(calculateEVI) .select(['B2', 'B3', 'B4', 'B8', 'ndvi', 'evi']); // Function to filter images based on the quality of the NDVI and EVI. var filterQuality = function(image) { var ndvi_quality = image.select('ndvi').qualityMosaic('ndvi').gte(0.6); var evi_quality = image.select('evi').qualityMosaic('evi').gte(0.6); return image.updateMask(ndvi_quality.and(evi_quality)); }; // Filter the images based on the quality of the NDVI and EVI. var sentinel_filtered = sentinel_ndvi_evi.filter(filterQuality); // Create a median composite of the filtered images and display it. var sentinel_median = sentinel_filtered.median(); Map.addLayer(sentinel_median, {bands: ['B4', 'B3', 'B2'], min: 0, max: 0.3}, 'Sentinel-2 Median Composite');

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这段代码使用 Google Earth Engine 平台加载 Sentinel-2 的 TOA 反射率数据，并对其进行云掩膜、NDVI、EVI 计算，最后使用质量筛选函数过滤图像，并生成中值合成图像。其中，变量 `sentinel` 是 Sentinel-2 图像集合...

concatenate_clouds.zip_PCD_clouds_pcd 合并

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在3D点云处理领域，Point Cloud Data (PCD) 文件格式是一种常用的数据存储标准，它允许用户存储、传输和处理3D空间中的点集。当你有多个PCD文件需要合并时，通常是为了整合来自不同源或不同时间点的数据，形成一个...

云计算Cloud Computing-伯克利

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本次分享《Above the Clouds: A Berkeley View of Cloud Computing》由Armando Fox等人撰写，深入探讨了云计算的概念、发展历程以及未来方向。 #### 二、数据中心：新时代的服务器 随着互联网应用的不断扩展，传统...
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