超级账本白皮书中文版
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AG=AMATEURGRAPH；业余系
MAG=AMATEURGRAPH Member。
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区块链技术白皮书分布式账本智能合约和去中心化应用开发
区块链技术白皮书分布式账本智能合约和去中心化应用开发1. 引言区块链技术随着比特币的发展而逐渐为人们所了解，这项技术具有分布式账本、智能合约和去中心化应用开发等特点，极大地改变了传统的中心化交易模式。
本白皮书旨在深入介绍区块链技术中的分布式账本、智能合约以及去中心化应用开发等关键概念和原理。
2. 分布式账本2.1 概述分布式账本是区块链技术的核心概念之一，它使用点对点网络，将交易记录以区块的形式链式连接起来，并经过加密和验证确认，从而实现交易的透明、可追溯和安全的特性。
2.2 工作原理分布式账本通过共识算法确保节点间的数据一致性，在区块链网络中，每个节点都保存了完整的账本副本，并使用加密算法对交易进行验证和记录。
一旦交易得到验证并被打包成区块，便会广播到所有节点中，同时进行共识验证，确保大部分节点认可并接受该区块，最后被添加进整个区块链中。
3. 智能合约3.1 定义智能合约是基于区块链技术的可编程合约，它能够自动执行、验证和执行合约的交易，并在特定条件满足时自动触发相应的操作。
智能合约主要由代码和数据组成，可以实现去中心化的合约执行。
3.2 实现原理智能合约使用区块链的分布式账本作为存储和执行环境，以及节点的计算能力作为合约执行的基础。
通过使用一种特定的编程语言和编译器，将合约代码转化为字节码，并通过区块链网络进行部署和执行。
当满足合约条件时，智能合约可以自动触发事务的执行。
4. 去中心化应用开发4.1 概述去中心化应用（DApp）是一种基于区块链技术的应用程序，它不依赖于中心化的服务器，而是通过区块链网络中的节点来实现数据存储和交互。
DApp具有去中心化、透明、安全和可靠等特点。
4.2 开发框架为了实现去中心化应用，需要使用特定的开发框架。
目前比较流行的DApp开发框架包括以太坊、EOS等。
这些框架提供了一系列的API 和工具，用于开发智能合约和基于区块链的应用程序。
4.3 开发流程去中心化应用的开发流程包括需求分析、智能合约编写、前端界面设计和测试等步骤。
中国与非洲的经贸合作白皮书(汉英对照版)
中国与非洲的经贸合作前言中国是世界上最大的发展中国家，非洲是发展中国家最集中的大陆，中国和非洲的人口占世界人口三分之一以上。
发展经济和推动社会进步是中国与非洲共同面临的任务。
多年来，在发展过程中，中国与非洲充分发挥双方资源条件和经济结构等方面的互补性，按照平等相待、讲求实效、互惠互利、共同发展的原则，不断加强经贸合作，努力实现互利共赢。
实践证明，中非经贸合作符合双方共同利益，有助于非洲实现联合国千年发展目标，促进了中非共同繁荣和进步。
２０世纪５０年代，中非经贸合作以贸易和对非援助为主。
在双方共同努力下，合作领域不断拓宽，合作内容日益丰富。
特别是２０００年中非合作论坛成立后，双方经贸合作进一步加强和活跃，贸易、投资、基础设施、能力建设全面推进，金融、旅游等领域的合作逐步拓展，形成了多层次、宽领域的格局，处在新的历史起点上。
中非经贸合作是南南合作的重要组成部分，为南南合作注入新的活力，提升了发展中国家在国际政治经济格局中的地位，为推动建立公正合理的国际政治经济新秩序发挥着重要作用。
中国也愿与其他国家和国际组织一道，加强与非洲国家的磋商与协调，共同参与非洲建设，共同推动非洲的和平、发展与进步一、促进贸易平衡发展贸易是中非经贸合作最初的形式。
伴随着中非关系的发展和交往的增多，中非贸易规模日益扩大。
１９５０年，中非双边贸易额仅为１２１４万美元，１９６０年达到１亿美元，１９８０年超过１０亿美元。
２０００年迈上百亿美元台阶后，中非贸易呈现快速增长势头。
２００８年突破了１０００亿美元，其中中国对非洲出口５０８亿美元，自非洲进口５６０亿美元。
２０００年至２００８年，中非贸易年均增长率高达３３.５％，占中国对外贸易总额的比重由２.２％升至４.２％，占非洲对外贸易总额的比重由３.８％升至１０.４％。
２００９年，虽然受国际金融危机影响，中非贸易额下降到９１０.７亿美元，但中国在当年首次成为非洲第一大贸易伙伴国。
随着世界经济复苏，中非贸易呈现良好的恢复发展态势。
大数据标准化白皮书
伯克利云计算白皮书(英文全)
Above the Clouds: A Berkeley View of CloudComputingMichael ArmbrustArmando FoxRean GriffithAnthony D. JosephRandy H. KatzAndrew KonwinskiGunho LeeDavid A. PattersonAriel RabkinIon StoicaMatei ZahariaElectrical Engineering and Computer SciencesUniversity of California at BerkeleyTechnical Report No. UCB/EECS-2009-28/Pubs/TechRpts/2009/EECS-2009-28.htmlFebruary 10, 2009Copyright 2009, by the author(s).All rights reserved.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission.AcknowledgementThe RAD Lab's existence is due to the generous support of the founding members Google, Microsoft, and Sun Microsystems and of the affiliate members Amazon Web Services, Cisco Systems, Facebook, Hewlett-Packard, IBM, NEC, Network Appliance, Oracle, Siemens, and VMware; by matching funds from the State of California's MICRO program (grants 06-152, 07-010, 06-148, 07-012, 06-146, 07-009, 06-147, 07-013, 06-149, 06-150, and 07-008) and the University of California Industry/University Cooperative Research Program (UC Discovery) grant COM07-10240; and by the National Science Foundation (grant #CNS-0509559).Above the Clouds:A Berkeley View of Cloud ComputingMichael 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@)UC Berkeley Reliable Adaptive Distributed Systems Laboratory∗/February10,2009KEYWORDS:Cloud Computing,Utility Computing,Internet Datacenters,Distributed System Economics1Executive SummaryCloud Computing,the long-held dream of computing as a utility,has the potential to transform a large part of the 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 outlays 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,companies with large batch-oriented tasks can get results as quickly as their programs can scale,since using1000servers for one hour costs no more than using one server for1000hours.This elasticity of resources,without paying a premium for 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 as Software as a Service(SaaS).The datacenter hardware and software is what we will call a Cloud.When a Cloud is made available in a pay-as-you-go manner to the general public,we call it a Public Cloud;the service being sold is 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 does not include Private Clouds.People can be users or providers of SaaS,or users or providers of Utility Computing.We 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 andincrease 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 hourand storage by the day)and release them as needed,thereby rewarding conservation by letting machines and storage go when they are no longer useful.We argue that the construction and operation of extremely large-scale,commodity-computer datacenters at low-cost locations was the key necessary enabler of Cloud Computing,for they uncovered the factors of5to7decrease in cost of electricity,network bandwidth,operations,software,and hardware available at these very large economies ∗The RAD Lab’s existence is due to the generous support of the founding members Google,Microsoft,and Sun Microsystems and of the affiliate members Amazon Web Services,Cisco Systems,Facebook,Hewlett-Packard,IBM,NEC,Network Appliance,Oracle,Siemens,and VMware;by matching funds from the State of California’s MICRO program(grants06-152,07-010,06-148,07-012,06-146,07-009,06-147,07-013,06-149, 06-150,and07-008)and the University of California Industry/University Cooperative Research Program(UC Discovery)grant COM07-10240;and by the National Science Foundation(grant#CNS-0509559).of scale.These factors,combined with statistical multiplexing to increase utilization compared a private cloud,meant that cloud computing could offer services below the costs of a medium-sized datacenter and yet still make a good profit.Any application needs a model of computation,a model of storage,and a model of communication.The statistical multiplexing necessary to achieve elasticity and the illusion of infinite capacity requires each of these resources to be virtualized to hide the implementation of how they are multiplexed and shared.Our view is that different utility computing offerings will be distinguished based on the level of abstraction presented to the programmer and the level of management of the resources.Amazon EC2is at one end of the spectrum.An EC2instance looks much like physical hardware,and users can control nearly the entire software stack,from the kernel upwards.This low level makes it inherently difficult for Amazon to offer automatic scalability and failover,because the semantics associated with replication and other state management issues are highly application-dependent.At the other extreme of the spectrum are application domain-specific platforms such as Google AppEngine.AppEngine is targeted exclusively at traditional web applications, enforcing an application structure of clean separation between a stateless computation tier and a stateful storage tier. AppEngine’s impressive automatic scaling and high-availability mechanisms,and the proprietary MegaStore data storage available to AppEngine applications,all rely on these constraints.Applications for Microsoft’s Azure are written using libraries,and compiled to the Common Language Runtime,a language-independent managed environment.Thus,Azure is intermediate between application frameworks like AppEngine and hardware virtual machines like EC2.When is Utility Computing preferable to running a Private Cloud?Afirst case is when demand for a service varies with time.Provisioning a data center for the peak load it must sustain a few days per month leads to underutilization at other times,for example.Instead,Cloud Computing lets an organization pay by the hour for computing resources, potentially leading to cost savings even if the hourly rate to rent a machine from a cloud provider is higher than the rate to own one.A second case is when demand is unknown in advance.For example,a web startup will need to support a spike in demand when it becomes popular,followed potentially by a reduction once some of the visitors turn away.Finally,organizations that perform batch analytics can use the”cost associativity”of cloud computing tofinish computations faster:using1000EC2machines for1hour costs the same as using1machine for1000hours.For the first case of a web business with varying demand over time and revenue proportional to user hours,we have captured the tradeoff in the equation below.UserHours cloud×(revenue−Cost cloud)≥UserHours datacenter×(revenue−Cost datacenter Utilization)(1)The left-hand side multiplies the net revenue per user-hour by the number of user-hours,giving the expected profit from using Cloud Computing.The right-hand side performs the same calculation for afixed-capacity datacenter by factoring in the average utilization,including nonpeak workloads,of the datacenter.Whichever side is greater represents the opportunity for higher profit.Table1below previews our ranked list of critical obstacles to growth of Cloud Computing in Section7.Thefirst three concern adoption,the nextfive affect growth,and the last two are policy and business obstacles.Each obstacle is paired with an opportunity,ranging from product development to research projects,which can overcome that obstacle.We predict Cloud Computing will grow,so developers should take it into account.All levels should aim at hori-zontal scalability of virtual machines over the efficiency on a single VM.In addition1.Applications Software needs to both scale down rapidly as well as scale up,which is a new requirement.Suchsoftware also needs a pay-for-use licensing model to match needs of Cloud Computing.2.Infrastructure Software needs to be aware that it is no longer running on bare metal but on VMs.Moreover,itneeds to have billing built in from the beginning.3.Hardware Systems should be designed at the scale of a container(at least a dozen racks),which will be isthe minimum purchase size.Cost of operation will match performance and cost of purchase in importance, rewarding energy proportionality such as by putting idle portions of the memory,disk,and network into low power mode.Processors should work well with VMs,flash memory should be added to the memory hierarchy, and LAN switches and W AN routers must improve in bandwidth and cost.2Cloud Computing:An Old Idea Whose Time Has(Finally)ComeCloud Computing is a new term for a long-held dream of computing as a utility[35],which has recently emerged as a commercial reality.Cloud Computing is likely to have the same impact on software that foundries have had on theTable1:Quick Preview of Top10Obstacles to and Opportunities for Growth of Cloud Computing.Obstacle Opportunity1Availability of Service Use Multiple Cloud Providers;Use Elasticity to Prevent DDOS2Data Lock-In Standardize APIs;Compatible SW to enable Surge Computing3Data Confidentiality and Auditability Deploy Encryption,VLANs,Firewalls;Geographical Data Storage4Data Transfer Bottlenecks FedExing Disks;Data Backup/Archival;Higher BW Switches5Performance Unpredictability Improved VM Support;Flash Memory;Gang Schedule VMs6Scalable Storage Invent Scalable Store7Bugs in Large Distributed Systems Invent Debugger that relies on Distributed VMs8Scaling Quickly Invent Auto-Scaler that relies on ML;Snapshots for Conservation9Reputation Fate Sharing Offer reputation-guarding services like those for email10Software Licensing Pay-for-use licenses;Bulk use saleshardware industry.At one time,leading hardware companies required a captive semiconductor fabrication facility, and companies had to be large enough to afford to build and operate it economically.However,processing equipment doubled in price every technology generation.A semiconductor fabrication line costs over$3B today,so only a handful of major“merchant”companies with very high chip volumes,such as Intel and Samsung,can still justify owning and operating their own fabrication lines.This motivated the rise of semiconductor foundries that build chips for others, such as Taiwan Semiconductor Manufacturing Company(TSMC).Foundries enable“fab-less”semiconductor chip companies whose value is in innovative chip design:A company such as nVidia can now be successful in the chip business without the capital,operational expenses,and risks associated with owning a state-of-the-art fabrication line.Conversely,companies with fabrication lines can time-multiplex their use among the products of many fab-less companies,to lower the risk of not having enough successful products to amortize operational costs.Similarly,the advantages of the economy of scale and statistical multiplexing may ultimately lead to a handful of Cloud Computing providers who can amortize the cost of their large datacenters over the products of many“datacenter-less”companies.Cloud Computing has been talked about[10],blogged about[13,25],written about[15,37,38]and been featured in the title of workshops,conferences,and even magazines.Nevertheless,confusion remains about exactly what it is and when it’s useful,causing Oracle’s CEO to vent his frustration:The interesting thing about Cloud Computing is that we’ve redefined Cloud Computing to include ev-erything that we already do....I don’t understand what we would do differently in the light of CloudComputing other than change the wording of some of our ads.Larry Ellison,quoted in the Wall Street Journal,September26,2008 These remarks are echoed more mildly by Hewlett-Packard’s Vice President of European Software Sales:A lot of people are jumping on the[cloud]bandwagon,but I have not heard two people say the same thingabout it.There are multiple definitions out there of“the cloud.”Andy Isherwood,quoted in ZDnet News,December11,2008 Richard Stallman,known for his advocacy of“free software”,thinks Cloud Computing is a trap for users—if applications and data are managed“in the cloud”,users might become dependent on proprietary systems whose costs will escalate or whose terms of service might be changed unilaterally and adversely:It’s stupidity.It’s worse than stupidity:it’s a marketing hype campaign.Somebody is saying this isinevitable—and whenever you hear somebody saying that,it’s very likely to be a set of businessescampaigning to make it true.Richard Stallman,quoted in The Guardian,September29,2008 Our goal in this paper to clarify terms,provide simple formulas to quantify comparisons between of cloud and conventional Computing,and identify the top technical and non-technical obstacles and opportunities of Cloud Com-puting.Our view is shaped in part by working since2005in the UC Berkeley RAD Lab and in part as users of Amazon Web Services since January2008in conducting our research and our teaching.The RAD Lab’s research agenda is to invent technology that leverages machine learning to help automate the operation of datacenters for scalable Internet services.We spent six months brainstorming about Cloud Computing,leading to this paper that tries to answer the following questions:•What is Cloud Computing,and how is it different from previous paradigm shifts such as Software as a Service (SaaS)?•Why is Cloud Computing poised to take off now,whereas previous attempts have foundered?•What does it take to become a Cloud Computing provider,and why would a company consider becoming one?•What new opportunities are either enabled by or potential drivers of Cloud Computing?•How might we classify current Cloud Computing offerings across a spectrum,and how do the technical and business challenges differ depending on where in the spectrum a particular offering lies?•What,if any,are the new economic models enabled by Cloud Computing,and how can a service operator decide whether to move to the cloud or stay in a private datacenter?•What are the top10obstacles to the success of Cloud Computing—and the corresponding top10opportunities available for overcoming the obstacles?•What changes should be made to the design of future applications software,infrastructure software,and hard-ware to match the needs and opportunities of Cloud Computing?3What is Cloud Computing?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 as Software as a Service(SaaS),so we use that term.The datacenter hardware and software is what we will call a Cloud.When a Cloud is made available in a pay-as-you-go manner to the public,we call it a Public Cloud;the service being sold is Utility Computing.Current examples of public Utility Computing include Amazon Web Services,Google AppEngine,and Microsoft Azure.We use the term Private Cloud to refer to internal datacenters of a business or other organization that are not made available to the public.Thus,Cloud Computing is the sum of SaaS and Utility Computing,but does not normally include Private Clouds.We’ll generally use Cloud Computing,replacing it with one of the other terms only when clarity demands it.Figure1shows the roles of the people as users or providers of these layers of Cloud Computing,and we’ll use those terms to help make our arguments clear.The advantages of SaaS to both end users and service providers are well understood.Service providers enjoy greatly simplified software installation and maintenance and centralized control over versioning;end users can access the service“anytime,anywhere”,share data and collaborate more easily,and keep their data stored safely in the infrastructure.Cloud Computing does not change these arguments,but it does give more application providers the choice of deploying their product as SaaS without provisioning a datacenter:just as the emergence of semiconductor foundries gave chip companies the opportunity to design and sell chips without owning a fab,Cloud Computing allows deploying SaaS—and scaling on demand—without building or provisioning a datacenter.Analogously to how SaaS allows the user to offload some problems to the SaaS provider,the SaaS provider can now offload some of his problems to the Cloud Computing provider.From now on,we will focus on issues related to the potential SaaS Provider(Cloud User)and to the Cloud Providers,which have received less attention.We will eschew terminology such as“X as a service(XaaS)”;values of X we have seen in print include Infrastruc-ture,Hardware,and Platform,but we were unable to agree even among ourselves what the precise differences among them might be.1(We are using Endnotes instead of footnotes.Go to page20at the end of paper to read the notes, which have more details.)Instead,we present a simple classification of Utility Computing services in Section5that focuses on the tradeoffs among programmer convenience,flexibility,and portability,from both the cloud provider’s and the cloud user’s point of view.From a hardware point of view,three aspects are new in Cloud Computing[42]: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 andincrease hardware resources only when there is an increase in their needs;and3.The ability to pay for use of computing resources on a short-term basis as needed(e.g.,processors by the hourand storage by the day)and release them as needed,thereby rewarding conservation by letting machines and storage go when they are no longer useful.Figure1:Users and Providers of Cloud Computing.The benefits of SaaS to both SaaS users and SaaS providers are well documented,so we focus on Cloud Computing’s effects on Cloud Providers and SaaS Providers/Cloud users.The top level can be recursive,in that SaaS providers can also be a SaaS users.For example,a mashup provider of rental maps might be a user of the Craigslist and Google maps services.We will argue that all three are important to the technical and economic changes made possible by Cloud Com-puting.Indeed,past efforts at utility computing failed,and we note that in each case one or two of these three critical characteristics were missing.For example,Intel Computing Services in2000-2001required negotiating a contract and longer-term use than per hour.As a successful example,Elastic Compute Cloud(EC2)from Amazon Web Services(AWS)sells1.0-GHz x86 ISA“slices”for10cents per hour,and a new“slice”,or instance,can be added in2to5minutes.Amazon’s Scalable Storage Service(S3)charges$0.12to$0.15per gigabyte-month,with additional bandwidth charges of$0.10to$0.15 per gigabyte to move data in to and out of AWS over the Internet.Amazon’s bet is that by statistically multiplexing multiple instances onto a single physical box,that box can be simultaneously rented to many customers who will not in general interfere with each others’usage(see Section7).While the attraction to Cloud Computing users(SaaS providers)is clear,who would become a Cloud Computing provider,and why?To begin with,realizing the economies of scale afforded by statistical multiplexing and bulk purchasing requires the construction of extremely large datacenters.Building,provisioning,and launching such a facility is a hundred-million-dollar undertaking.However,because of the phenomenal growth of Web services through the early2000’s,many large Internet companies,including Amazon, eBay,Google,Microsoft and others,were already doing so.Equally important,these companies also had to develop scalable software infrastructure(such as MapReduce,the Google File System,BigTable,and Dynamo[16,20,14,17]) and the operational expertise to armor their datacenters against potential physical and electronic attacks.Therefore,a necessary but not sufficient condition for a company to become a Cloud Computing provider is that it must have existing investments not only in very large datacenters,but also in large-scale software infrastructure and operational expertise required to run them.Given these conditions,a variety of factors might influence these companies to become Cloud Computing providers:1.Make a lot of money.Although10cents per server-hour seems low,Table2summarizes James Hamilton’sestimates[23]that very large datacenters(tens of thousands of computers)can purchase hardware,network bandwidth,and power for1/5to1/7the prices offered to a medium-sized(hundreds or thousands of computers) datacenter.Further,thefixed costs of software development and deployment can be amortized over many more machines.Others estimate the price advantage as a factor of3to5[37,10].Thus,a sufficiently large company could leverage these economies of scale to offer a service well below the costs of a medium-sized company and still make a tidy profit.2.Leverage existing investment.Adding Cloud Computing services on top of existing infrastructure provides anew revenue stream at(ideally)low incremental cost,helping to amortize the large investments of datacenters.Indeed,according to Werner V ogels,Amazon’s CTO,many Amazon Web Services technologies were initially developed for Amazon’s internal operations[42].3.Defend a franchise.As conventional server and enterprise applications embrace Cloud Computing,vendorswith an established franchise in those applications would be motivated to provide a cloud option of their own.For example,Microsoft Azure provides an immediate path for migrating existing customers of Microsoft enter-prise applications to a cloud environment.Table2:Economies of scale in2006for medium-sized datacenter(≈1000servers)vs.very large datacenter(≈50,000 servers).[24]Technology Cost in Medium-sized DC Cost in Very Large DC RatioNetwork$95per Mbit/sec/month$13per Mbit/sec/month7.1Storage$2.20per GByte/month$0.40per GByte/month 5.7Administration≈140Servers/Administrator>1000Servers/Administrator7.1Table3:Price of kilowatt-hours of electricity by region[7].Price per KWH Where Possible Reasons Why3.6¢Idaho Hydroelectric power;not sent long distance10.0¢California Electricity transmitted long distance over the grid;limited transmission lines in Bay Area;no coalfired electricity allowed in California.18.0¢Hawaii Must ship fuel to generate electricity4.Attack an incumbent.A company with the requisite datacenter and software resources might want to establish abeachhead in this space before a single“800pound gorilla”emerges.Google AppEngine provides an alternative path to cloud deployment whose appeal lies in its automation of many of the scalability and load balancing features that developers might otherwise have to build for themselves.5.Leverage customer relationships.IT service organizations such as IBM Global Services have extensive cus-tomer relationships through their service offerings.Providing a branded Cloud Computing offering gives those customers an anxiety-free migration path that preserves both parties’investments in the customer relationship.6.Become a platform.Facebook’s initiative to enable plug-in applications is a greatfit for cloud computing,aswe will see,and indeed one infrastructure provider for Facebook plug-in applications is Joyent,a cloud provider.Yet Facebook’s motivation was to make their social-networking application a new development platform.Several Cloud Computing(and conventional computing)datacenters are being built in seemingly surprising loca-tions,such as Quincy,Washington(Google,Microsoft,Yahoo!,and others)and San Antonio,Texas(Microsoft,US National Security Agency,others).The motivation behind choosing these locales is that the costs for electricity,cool-ing,labor,property purchase costs,and taxes are geographically variable,and of these costs,electricity and cooling alone can account for a third of the costs of the datacenter.Table3shows the cost of electricity in different locales[10]. Physics tells us it’s easier to ship photons than electrons;that is,it’s cheaper to ship data overfiber optic cables than to ship electricity over high-voltage transmission lines.4Clouds in a Perfect Storm:Why Now,Not Then?Although we argue that the construction and operation of extremely large scale commodity-computer datacenters was the key necessary enabler of Cloud Computing,additional technology trends and new business models also played a key role in making it a reality this time around.Once Cloud Computing was“off the ground,”new application opportunities and usage models were discovered that would not have made sense previously.4.1New Technology Trends and Business ModelsAccompanying the emergence of Web2.0was a shift from“high-touch,high-margin,high-commitment”provisioning of service“low-touch,low-margin,low-commitment”self-service.For example,in Web1.0,accepting credit card payments from strangers required a contractual arrangement with a payment processing service such as VeriSign or ;the arrangement was part of a larger business relationship,making it onerous for an individual or a very small business to accept credit cards online.With the emergence of PayPal,however,any individual can accept credit card payments with no contract,no long-term commitment,and only modest pay-as-you-go transaction fees.The level of“touch”(customer support and relationship management)provided by these services is minimal to nonexistent,butthe fact that the services are now within reach of individuals seems to make this less important.Similarly,individuals’Web pages can now use Google AdSense to realize revenue from ads,rather than setting up a relationship with an ad placement company,such DoubleClick(now acquired by Google).Those ads can provide the business model for Wed2.0apps as well.Individuals can distribute Web content using Amazon CloudFront rather than establishing a relationship with a content distribution network such as Akamai.Amazon Web Services capitalized on this insight in2006by providing pay-as-you-go computing with no contract: all customers need is a credit card.A second innovation was selling hardware-level virtual machines cycles,allowing customers to choose their own software stack without disrupting each other while sharing the same hardware and thereby lowering costs further.4.2New Application OpportunitiesWhile we have yet to see fundamentally new types of applications enabled by Cloud Computing,we believe that several important classes of existing applications will become even more compelling with Cloud Computing and contribute further to its momentum.When Jim Gray examined technological trends in2003[21],he concluded that economic necessity mandates putting the data near the application,since the cost of wide-area networking has fallen more slowly(and remains relatively higher)than all other IT hardware costs.Although hardware costs have changed since Gray’s analysis,his idea of this“breakeven point”has not.Although we defer a more thorough discussion of Cloud Computing economics to Section6,we use Gray’s insight in examining what kinds of applications represent particularly good opportunities and drivers for Cloud Computing.Mobile interactive applications.Tim O’Reilly believes that“the future belongs to services that respond in real time to information provided either by their users or by nonhuman sensors.”[38]Such services will be attracted to the cloud not only because they must be highly available,but also because these services generally rely on large data sets that are most conveniently hosted in large datacenters.This is especially the case for services that combine two or more data sources or other services,e.g.,mashups.While not all mobile devices enjoy connectivity to the cloud100% of the time,the challenge of disconnected operation has been addressed successfully in specific application domains, 2so we do not see this as a significant obstacle to the appeal of mobile applications.Parallel batch processing.Although thus far we have concentrated on using Cloud Computing for interactive SaaS,Cloud Computing presents a unique opportunity for batch-processing and analytics jobs that analyze terabytes of data and can take hours tofinish.If there is enough data parallelism in the application,users can take advantage of the cloud’s new“cost associativity”:using hundreds of computers for a short time costs the same as using a few computers for a long time.For example,Peter Harkins,a Senior Engineer at The Washington Post,used200EC2 instances(1,407server hours)to convert17,481pages of Hillary Clinton’s travel documents into a form more friendly to use on the WWW within nine hours after they were released[3].Programming abstractions such as Google’s MapReduce[16]and its open-source counterpart Hadoop[11]allow programmers to express such tasks while hiding the operational complexity of choreographing parallel execution across hundreds of Cloud Computing servers.Indeed, Cloudera[1]is pursuing commercial opportunities in this space.Again,using Gray’s insight,the cost/benefit analysis must weigh the cost of moving large datasets into the cloud against the benefit of potential speedup in the data analysis. When we return to economic models later,we speculate that part of Amazon’s motivation to host large public datasets for free[8]may be to mitigate the cost side of this analysis and thereby attract users to purchase Cloud Computing cycles near this data.The rise of analytics.A special case of compute-intensive batch processing is business analytics.While the large database industry was originally dominated by transaction processing,that demand is leveling off.A growing share of computing resources is now spent on understanding customers,supply chains,buying habits,ranking,and so on. Hence,while online transaction volumes will continue to grow slowly,decision support is growing rapidly,shifting the resource balance in database processing from transactions to business analytics.Extension of compute-intensive desktop applications.The latest versions of the mathematics software packages Matlab and Mathematica are capable of using Cloud Computing to perform expensive evaluations.Other desktop applications might similarly benet from seamless extension into the cloud.Again,a reasonable test is comparing the cost of computing in the Cloud plus the cost of moving data in and out of the Cloud to the time savings from using the Cloud.Symbolic mathematics involves a great deal of computing per unit of data,making it a domain worth investigating.An interesting alternative model might be to keep the data in the cloud and rely on having sufficient bandwidth to enable suitable visualization and a responsive GUI back to the human user.Offline image rendering or3D animation might be a similar example:given a compact description of the objects in a3D scene and the characteristics of the lighting sources,rendering the image is an embarrassingly parallel task with a high computation-to-bytes ratio.“Earthbound”applications.Some applications that would otherwise be good candidates for the cloud’s elasticity and parallelism may be thwarted by data movement costs,the fundamental latency limits of getting into and out of the cloud,or both.For example,while the analytics associated with making long-termfinancial decisions are appropriate。
金融科技行业中超级账本技术的使用教程与金融业务应用案例
金融科技行业中超级账本技术的使用教程与金融业务应用案例超级账本技术（Hyperledger）是一种基于区块链技术的开源项目，该项目旨在通过提供一个可靠、可扩展的平台来构建跨行业的分布式账本解决方案。
金融科技行业作为一种应用区块链技术的行业，也可以充分利用超级账本技术来改进金融业务流程及安全性。
本文将介绍超级账本技术的使用教程，并提供一些金融业务应用案例。
使用超级账本技术的前提是具备一定的区块链技术基础，了解区块链的基本概念，如分布式账本、共识机制和智能合约等。
下面是超级账本技术的使用教程：1. 安装与配置超级账本技术环境超级账本技术是一个开源项目，可以从官方网站上下载并安装到本地环境中。
在安装完成后，需要进行一些基本的配置，例如设置节点的身份和权限、配置网络拓扑等。
2. 创建网络与通道在超级账本技术中，网络由多个节点组成，节点可以是Peer节点、Orderer节点或CA节点。
首先需要创建一个网络，然后在网络中创建一个或多个通道，通道用于不同节点之间的交流与数据传输。
通过创建通道，可以将不同节点连接在一起，形成一个分布式的账本网络。
3. 定义链码与智能合约在超级账本技术中，链码（Chaincode）是一种特殊的智能合约，用于定义业务逻辑和数据模型。
可以使用Go或Java等编程语言编写链码，并将其部署到网络中的节点上。
链码可以对账本数据进行读写操作，实现业务流程的自动执行。
4. 执行交易与查询操作在超级账本技术中，交易是指对账本数据进行读写操作的过程。
可以通过调用链码提供的函数来执行交易，例如转账、存证等操作。
另外，超级账本技术还提供了强大的查询功能，可以根据特定条件查询账本中的数据，并返回查询结果。
5. 实现隐私与加密保护在金融科技行业中，数据的隐私与安全性至关重要。
超级账本技术提供了多种方式来加强隐私与加密保护，例如使用身份验证、访问控制、加密等技术手段。
通过合理配置，可以保证账本中数据的机密性和完整性，防止未授权的访问与篡改。
小年祝福_日常祝福语_
小年祝福20xx小年篇一一、当您看见这信息时，幸运已降临到你头上，财神已进了您家门，荣华富贵已离您不远祝福您朋友：小年快乐!二、小年来到喜临门，送你一只聚宝盆，装书装本装学问，装金装银装财神，装了健康装事业，装了朋友装亲人，时时刻刻都幸福，平平安安交鸿运!三、毛主席说：拜年、祝福不是资产阶级的专利，我们无产阶级也要拜，就是拜得晚一点也不怕。
无非拱拱手，说些吉利话嘛，红包那些东西，腐朽得很，消磨意志。
四、小年到了，想想没什么送给你的，又不打算给你太多，只有给你五千万：千万快乐!千万要健康!千万要平安!千万要知足!千万不要忘记我!小年祝福祝语贺语小年祝词五、说一句恭喜发财，答一句全家安康;说一句朋友祝福语万事如意，答一句工作顺利。
今天就是说吉祥话儿的日子，今天就是顺心的日子。
六、感谢你的关怀，感谢你的帮助，感谢你对我做的一切……请接受我新春的祝愿，祝你平安幸福。
七、小年快乐!万事大吉!合家欢乐!财源广进!吉祥如意!花开富贵!金玉满堂!福禄寿禧!恭喜发财!八、一年四季，即将岁末，新年新气息，说一声恭喜，五路财神运财来;赌一下运气，好事情排队来;饮一杯美酒，今后笑口常开;开一朵春花，新春大家乐开怀!九、新的一年，祝好事接二连三，心情四季如春，生活五颜六色，七彩缤纷，偶尔八点小财，烦恼抛到九霄云外!请接受我十全十美的祝福。
祝免年春节快乐!十、小年圣旨到：从今起你的烦恼失意扫进“回收站”，新建一个“小年开心文件夹”，写一篇“快乐”文档，放一幅“如意”幻灯，用CAD绘出美好小年，祝你小年快乐吉祥!十一、让我告诉你七种活得开心的方法：多关心我;多想我;多照顾我;多疼我;多见我;多发信息给我;最重要的就是认识到一个十二、友情是香喷喷的大米饭，热腾腾的涮火锅，火辣辣的二锅头。
又馋了吧，小年喝一盅吧!十三、小年里，孙悟空翻着筋斗云送来一把“铁扫帚”，猪八戒扛着“烦恼清洁剂”来敲门，沙僧送来一个“万事如意”灶台，唐僧对您行合掌礼：阿弥陀佛，小僧这厢有礼了，小年吉祥，略表心意!十四、春节送你个福，送福祝福全是福，有福藏福家家福，享福见福时时福，金福银福处处福，大福小福天天福，接福纳福年年福，守福祈福岁岁福。
区块链技术白皮书模板
区块链技术白皮书模板1. 引言在这个数字化时代，区块链技术以其去中心化、透明度高以及安全性强的特点，成为了诸多行业的热点关注。
本篇白皮书旨在介绍一个针对某特定领域的区块链技术解决方案，为读者提供全面的信息，并激发对该领域中潜在机遇的兴趣。
2. 领域背景在此部分，将详细介绍所涉及的领域的现状和问题。
通过对该领域的分析，将引出使用区块链技术解决这些问题的合理性和必要性。
3. 技术概述3.1 区块链基础在此部分，将对区块链技术的核心原理进行详细的介绍，包括分布式账本、去中心化、共识机制等基本概念。
3.2 应用场景在此部分，将列举适用于该领域的区块链应用场景，并详细描述其运作方式和优势。
3.3 技术架构在此部分，将提供一个具体的技术架构图，并解释各个组成部分的功能和关系。
4. 解决方案在此部分，将详细介绍使用区块链技术解决该领域问题的方案。
通过详细的案例和技术流程图，向读者展示方案的可行性和优势。
5. 实施计划5.1 发展阶段在此部分，将详细介绍该方案的发展阶段，并说明每个阶段实际操作的目标和措施。
5.2 时间规划在此部分，将列出实施该方案的时间规划表，并解释关键节点的意义和相关工作的安排。
5.3 风险评估在此部分，将对实施过程中可能出现的风险进行评估，并提出相应的风险控制措施。
6. 市场前景在此部分，将详细阐述该方案在当前市场环境下的前景，包括市场规模、增长预测等相关数据和分析。
7. 总结在此部分，将对全文进行简要的总结，并再次强调该方案的优势和潜在机遇。
8. 参考文献此部分列出了白皮书中所引用的所有参考资料。
至此，本篇区块链技术白皮书模板的编写已完毕，希望能帮助读者更好地了解和掌握该领域相关信息。
如读者对该方案感兴趣，可进一步联系我们获取更多详细信息。
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