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https://techcrunch.com/2018/06/12/netflix-and-alphabet-will-need-to-become-isps-fast/
AT&T, Comcast都在投入内容服务,购买Time Warner等,不甘心沦为管道工。进而推动网络平权的废除。
Google,Netflix等反而在投入管道建设,不甘心被管道工剥削,更不用说平权废除后,进行不公平竞争。
Internet的流量集中化,被几个巨头公司把持,Google, FB, AWS都有成为新ISP的潜力,但管道工是辛苦的体力活,也是重资产,更多的是如同文章所说,防卡脖子同时提供谈判资本。
反观国内的ISP,占尽天时地利,联通是彻底消亡了,只能成为巨头的管道工;移动再学习AT&T,内容上投资不少,电信有最好的宽带资源,无非卖卖机房;如果不是政策限制,A,T这些公司是必定会学习国外同行自建骨干网的,无论对内容巨头,还是设备厂家,或者ISP自己,都是有健康促进作用的。
https://www.nextplatform.com/2017/07/17/google-wants-rewire-internet/
Google carries about 25 percent of Internet traffic today. In other words, one out of four bytes that are delivered to end users across the Internet originate from Google.
B4 is our private datacenter network interconnect, and B2 is what connects our datacenter to the public Internet.
(The traditional routing) is focused on individual boxes, and essentially what this means is that they have to take a very local view about connectivity. As soon as they can find a path between a source and a destination, two computers that want to talk to each other across the network, then BGP is happy. It doesn’t try to find the best path, and it doesn’t try to do dynamic optimization.
basically what Espresso does is it actually pulls the routing intelligence outside of individual routers into a server pool, where we can do offline analysis of the data.
Espresso is serving 20 percent of the traffic
However, it was the C5 instance, released just this month, that was first to offload the entire EC2 stack -- from networking and storage to management, security and monitoring -- onto dedicated hardware. Developed using technology AWS acquired in 2015 when it purchased Annapurna Labs, the C5 marked the debut of the "new EC2 hypervisor," DeSantis said.
Sample data file lininput_xeon64.
Current date/time: Fri Jul 20 12:14:46 2018
CPU frequency: 3.182 GHz
Number of CPUs: 1
Number of cores: 2
Number of threads: 4
Parameters are set to:
Number of tests: 15
Number of equations to solve (problem size) : 1000 2000 5000 10000 15000 18000 20000 22000 25000 26000 27000 30000 35000 40000 45000
Leading dimension of array : 1000 2000 5008 10000 15000 18008 20016 22008 25000 26000 27000 30000 35000 40000 45000
Number of trials to run : 4 2 2 2 2 2 2 2 2 2 1 1 1 1 1
Data alignment value (in Kbytes) : 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1
Maximum memory requested that can be used=12800801024, at the size=40000
=================== Timing linear equation system solver ===================
Size LDA Align. Time(s) GFlops Residual Residual(norm) Check
1000 1000 4 0.034 19.7983 1.165068e-12 3.973181e-02 pass
1000 1000 4 0.006 104.9613 1.165068e-12 3.973181e-02 pass
1000 1000 4 0.006 103.4335 1.165068e-12 3.973181e-02 pass
1000 1000 4 0.006 105.0405 1.165068e-12 3.973181e-02 pass
2000 2000 4 0.040 132.0736 5.001027e-12 4.350281e-02 pass
2000 2000 4 0.039 138.4960 5.001027e-12 4.350281e-02 pass
5000 5008 4 0.552 151.1293 2.715800e-11 3.786964e-02 pass
5000 5008 4 0.543 153.4799 2.715800e-11 3.786964e-02 pass
10000 10000 4 4.182 159.4505 1.026247e-10 3.618650e-02 pass
10000 10000 4 4.135 161.2604 1.026247e-10 3.618650e-02 pass
15000 15000 4 13.823 162.8074 2.383860e-10 3.754620e-02 pass
15000 15000 4 13.778 163.3317 2.383860e-10 3.754620e-02 pass
18000 18008 4 23.738 163.8125 3.012383e-10 3.298929e-02 pass
18000 18008 4 23.607 164.7210 3.012383e-10 3.298929e-02 pass
20000 20016 4 32.493 164.1619 3.488892e-10 3.088433e-02 pass
20000 20016 4 32.425 164.5075 3.488892e-10 3.088433e-02 pass
22000 22008 4 43.142 164.5649 4.480804e-10 3.282012e-02 pass
22000 22008 4 43.114 164.6711 4.480804e-10 3.282012e-02 pass
25000 25000 4 63.053 165.2235 5.548119e-10 3.155017e-02 pass
25000 25000 4 63.000 165.3626 5.548119e-10 3.155017e-02 pass
26000 26000 4 70.946 165.1765 5.690611e-10 2.992294e-02 pass
26000 26000 4 70.916 165.2471 5.690611e-10 2.992294e-02 pass
27000 27000 4 79.248 165.5989 6.225738e-10 3.035986e-02 pass
30000 30000 1 108.143 166.4630 8.671269e-10 3.418223e-02 pass
35000 35000 1 171.685 166.5016 1.019134e-09 2.958393e-02 pass
40000 40000 1 255.331 167.1161 1.394161e-09 3.100662e-02 pass
Performance Summary (GFlops)
Size LDA Align. Average Maximal
1000 1000 4 83.3084 105.0405
2000 2000 4 135.2848 138.4960
5000 5008 4 152.3046 153.4799
10000 10000 4 160.3554 161.2604
15000 15000 4 163.0695 163.3317
18000 18008 4 164.2668 164.7210
20000 20016 4 164.3347 164.5075
22000 22008 4 164.6180 164.6711
25000 25000 4 165.2930 165.3626
26000 26000 4 165.2118 165.2471
27000 27000 4 165.5989 165.5989
30000 30000 1 166.4630 166.4630
35000 35000 1 166.5016 166.5016
40000 40000 1 167.1161 167.1161
Residual checks PASSED
End of tests
2018/07/20 周五
12:41
What's this
What's new
WAN虚拟化技术,内部的WAN资源包装成产品提供给租户使用
建设在运营商网络资源基础上——使用了运营商的专线?or 走互联网
NetO - Ali 骨干网流量调度系统
运营商专线产品分类有组网专线(分支互联)和上网专线(internet服务)
云化之后大部分业务将在云上闭环,企业分支间的互联由云服务商提供免费服务,不再有专线互联诉求
云服务提供的带宽计费模式有按流量以及按带宽两种基本的,按带宽计费中一般取当月最高带宽作为计费标准,但是由于一般应用带宽不是始终平缓的,而是有波动的,因而提供了一种稍有良心的计费方式 - 95计费:5分钟为一个采样点,在一个月的采样数据中,取95%的位置带宽值作为最大带宽。这种方式允许了5%的突发带宽。
上规模的云服务提供商在建立超大规模集中式的数据中心,所谓卫星站点,贴近用户,用距离降低网络时延的需求是否存在?性价比如何?AWS,Ali,Azure,Google,FB是否有所谓这种模式?
ENA 收购Annapurna 设计的智能网卡
ENA 从15年随X1提供25G网络开始
从驱动看,ENA于82599EB的区别主要在:
ENA的驱动不区分PF/VF,但卡应该判断通过ethtool暴露api的权限。重要点:
https://barryoreilly.com/2017/05/31/busy-to-death/amp/
When a Cloud is made available in a pay-as-you-go manner to the general public, we call it a Public Cloud;
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. ---- like fab-less
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. ---- who can be cloud provider
James Hamilton’s estimates [23] that very large datacenters (tens of thousands of computers) can purchase hardware, network bandwidth, and power for 1/5 to 1/7 the prices offered to a medium-sized (hundreds or thousands of computers) datacenter. Further, the fixed costs of software development and deployment can be amortized over many more machines.
why intel does not provide cloud computing? It's all about *computing*
it’s cheaper to ship data over fiber optic cables than to ship electricity over high-voltage transmission lines.
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.
Real world estimates of server utilization in datacenters range from 5% to 20%
Elastic is the key feature of Cloud Computing
If new technologies or pricing plans become available to a cloud vendor, existing applications and customers can potentially benefit from them immediately, without incurring a capital expense.
A second opportunity is to find other reasons to make it attractive to keep data in the cloud, for once data is in the cloud for any reason it may no longer be a bottleneck and may enable new services that could drive the purchase of Cloud Computing cycles.
One of the difficult challenges in Cloud Computing is removing errors in these very large scale distributed systems. A common occurrence is that these bugs cannot be reproduced in smaller configurations, so the debugging must occur at scale in the production datacenters.
A fast and easy-to-use snapshot/restart tool might further encourage conservation of computing resources.
the licensing model for commercial software is not a good match to Utility Computing.
白盒交换机目的并不是减少成本网络设备的成本,通过OEM获取交换机设备,运维成本不一定小于商业设备,而且对使用者有很高的技能要求。
白盒交换机的诱惑在于所能够提供的想象空间,通过自定义的软件层,提供编程接口,满足对数据中心所有关键设备控制的欲望,归一化设备的行为和表现,减少整体(非网络)运维成本。
Hyper-Converged
超融合架构是一种技术手段,而并不是某一类特殊的硬件产品。
超融合只是一种架构设计,并不依赖于某种硬件设备。但是从硬件优化的角度来说。我们要求服务器有较多的本地盘,从而让尽量多的 IO 在本地就实现了访问。 https://community.qingcloud.com/topic/345/%E6%B7%B1%E5%BA%A6%E5%89%96%E6%9E%90-%E8%B6%85%E8%9E%8D%E5%90%88%E6%9E%B6%E6%9E%84%E5%BA%94%E7%94%A8%E4%B8%8E%E5%AE%9E%E8%B7%B5%E5%88%86%E4%BA%AB
什么是超融合?
– 共享计算、存储、网络资源的平台
– 软件定义存储、软件定义计算、软件定义网络
超融合市场驱动力:方便
– 运维简单,管理方便
– 部署简单,变更方便
– 方案简单,调整方便
HXDP分布式存储软件基本原理
• Hypervisor和Controller VM的软件都安装在HyperFlex的64G SD卡上,每
台物理服务器都要安装
• Hypervisor启动pass through直通模式,所有物理盘包括SSD和磁盘的控
制都是由Controller VM进行控制。所有Controller VM组成Cluster,逻辑上
整个集群是一块本地硬盘
https://blogs.cisco.com/datacenter/nvme-for-absolute-beginners
Serial Attached SCSI (SAS) ... no matter how many CPU cores you’ve got or how dense your flash, all data must move serially. NVMe, on the other hand, offers thousands of parallel queues, representing exponentially greater communication potential.
NVMe is the standardized interface for PCIe SSDs
SCSI controller slow down the throughput of the flash, because it's made for disk, one command at one time.
NVMe just release the potential by multiple queues.
顺序读写 (吞吐量,常用单位为MB/s):文件在硬盘上存储位置是连续的。
适用场景:大文件拷贝(比如视频音乐)。速度即使很高,对数据库性能也没有参考价值。
4K随机读写 (IOPS,常用单位为次):在硬盘上随机位置读写数据,每次4KB。
适用场景:操作系统运行、软件运行、数据库。
https://tech.meituan.com/about-desk-io.html
SSD以Page为单位做读写,以Block为单位做垃圾回收,Page一般有16KB大小,Block一般有几十MB大小,SSD写数据的逻辑是:
1)将该块数据所在的Page读出
2)修改该Page中该块数据的内容
3)找出一个新的空闲Block将2)中的Page写入,并将1)中提到的Page所在的Block中的Page标志为脏
理解了写原理,也就明白了为什么顺序写比随机写好了。四个字:垃圾回收!写相同数据量的情况下,顺序写制造更少的垃圾Block,所以比随机写有更高的性能。
http://perspectives.mvdirona.com/2017/04/how-many-data-centers-needed-world-wide/
why do most cloud providers have data centers in the same US areas (NW states and VA area) but the central, northern area of North America don’t have as many data center locations?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.