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Friday
Aug282015

Stuff The Internet Says On Scalability For August 28th, 2015x

Hey, it's HighScalability time:


The oldest known fossil of a flowering plant. 130 million years old. What digital will last so long?
  • 32.6: Ashley Madison password cracks per hour; 1 million: cores in the Human Brain Project's silicon brain; 54,000: tennis balls used at Wimbledon; 4 kB: size of first web page; 1.2 million: million messages per second Apache Samza performance on a single node; 27%: higher conversion for sites loading one second faster; 

  • Quotable Quotes:
    • @adrianco: Apple first read about Mesos on http://highscalability.com  and for a year have run Siri on the worlds biggest cluster 
    • @Besvinick: Interesting recurring sentiment from recent grads: We lived most of our college lives on Snapchat—now we don't have any "tangible" memories.
    • Robin Hobb: For most moments of our lives, we have forgotten almost all of the world around us, except for what currently claims our interest.
    • @Carnage4Life: I'd like to thank all the Amazon employees who cried at their desks to make this possible πŸ™πŸ‘ 🚚🍷🍸🍹🍺 
    • Jim Handy: The single most interesting thing I learned at the 2015 Flash Memory Summit was that 3D NAND doesn’t have a natural limit, after which some other memory type will need to be adopted.
    • @mccv: them: is that written down? me: we communicate in the viking tradition. Let me tell you the saga of that system.
    • The Handmade Manifesto: that amazing speed we'd been granted was wasted, by us, in a death by a thousand abstraction layers
    • Peter Thiel: For us to really have a greater productivity gains as a society, we have to do things more in the world of atoms and not just the world of bits.
    • @lxpollitt: Verizon announced today as paying customer of @Mesosphere DCOS. Cool on stage demo with 22k cores: 50k containers in 100s - @flo #MesosCon
    • Matthew Brunwasser: Technology has transformed this 21st-century version of a refugee crisis, not least by making it easier for millions more people to move.
    • @rsingel: Stephen Hawking says to never give up hope if caught in a black hole. He has never evidently used a mobile browser.
    • @lxpollitt: Siri has been running on Mesos for exactly one year today. “Mesos scales” - Apple #MesosCon
    • @Jimminy: "The cheapest, fastest, and most reliable components are those that aren’t there. — Gordon Bell
    • @mathiasverraes: There are only two hard problems in distributed systems:  2. Exactly-once delivery 1. Guaranteed order of messages 2. Exactly-once delivery
    • Horace Dediu: If I were Tim Cook I would not have the goal of tripling revenue over the next decade...The objective of the company is not to triple revenues, the objective of the company is to make great products...That's the goal. End of story. You don't talk about money. You talk about product. Money comes from product not the other way around...The purpose of the firm is to delight the customer. 
    • @t_blom: “The hardest thing about MVP — you decide what’s Minimum, the customer decides what's Viable”β€Š—β€Š@davidjbland 
    • @adrianco: #mesoscon @pbailis reading list. 
    • @kelseyhightower: Based on my twitter stream, it seems the theme coming out of #mesoscon is the major benefits of increasing resource utilization at scale.
    • lorenzhs: We need new algorithms that - require communication volume and latency significantly sublinear in the local input size (ideally polylogarithmic) - don't depend on randomly distributed input data (most older work does)
    • @clstokes: #MesosCon @pbailis on coordination-free systems - "Scalable systems can just shut up and comfortably share silence."
    • frankmcsherry: if you want to do any big data computation, please sort your records. Stop talking sass about how Hadoop sorts things it doesn't need to, read some papers, run some tests, and then sort your damned data. Or at least run faster than me when I sort your data for you.
    • @RFFlores: There's always lock-in. You have to choose where. My latest blog is about this.
    • Jared Diamond: People in the first world are terrified by the wrong things. The real danger isn’t terrorism, serial killers or sharks, which kill a very, very small percentage of people annually. The real risks are those things that we do daily that carry a low risk but that eventually catch up with you – driving, taking stairs, using step ladders.

  • Something tells me we can expect this list to get much larger as the future fumbles forward. T-Rex large. The 20 Most Infamous Cyberattacks of the 21st Century (Part I).

  • Getting to Datacenter Zero. Catchy buzzword from @swardley around Netflix sloughing off the last of its non AWS datacenter operations. Netflix shuts down its last data centre, but it still runs a big IT operation. Finally, all of Netflix IT will run in the public cloud. We'll likely hit Datacenter Zero long before we hit Inbox Zero.

  • She's so humble! Q: Alexa, what do you think of M, Facebook's new Human-Powered assistant? A: I don't have preferences or desires. 

  • Have you ever wanted to know how WiFi in a plane works? Have you ever wondered why it's so expensive? Have you ever wondered why it's just a tad slow? Then Why Gogo's Infuriatingly Expensive, Slow Internet Still Owns the Skies is your story. In my mind I thought the system would use a satellite. It doesn't! There's a vast air-to-ground system. The plane talks to 225 towers spread across the US. Newer systems do use a satellite. It's expensive because with a first mover advantage Gogo was able to lock in long term contracts and achieve a near monopoly. There are competitors, but switching costs are high. And with only 40,000 planes in the world making more money requires raising prices on relatively price insensitive business users. There's a sophisticated dynamic pricing scheme aimed at keeping traffic within capacity limits while maximizing profits. It's slow because the signal is shared by everyone on the plane and the hardware on 2/3rds of the planes tops out at 3Mbps. Yet it's still hard to deny: “Everything’s Amazing and Nobody’s Happy.” 

Don't miss all that the Internet has to say on Scalability, click below and become eventually consistent with all scalability knowledge (which means this post has many more items to read so please keep on reading)...

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Wednesday
Aug262015

7 Strategies for 10x Transformative Change

Peter Thiel, VC, PayPal co-founder, early Facebook investor, and most importantly, the supposed inspiration for Silicon Valley's intriguing Peter Gregory character, argues in his book Zero to One that a successful business needs to make a product that is 10 times better than its closest competitor

The title Zero to One refers to the idea of progress as either horizontal/extensive or vertical/intensive. For a more detailed explanation take a look at Peter Thiel's CS183: Startup - Class 1 Notes Essay.

Horizontal/extensive progress refers to copying things that work. Observe, imitate, and repeat.  The one word summary for the concept is  "globalization.” For more on this PAYPAL MAFIA: Reid Hoffman & Peter Thiel's Master Class in China is an interesting watch.

Vertical/intensive progress means doing something genuinely new, that is going from zero to one, as apposed to going from one to N, which is merely globalization. This is the creative spark. The hero's journey of over coming obstacles on the way to becoming the Master of the Universe you were always meant to be.

We see this pattern with Google a lot. Google often hits scaling challenges long before anyone else and because they have a systematizing culture they produce discrete replicatable technologies that then diffuse out to the rest of the world, often through open source efforts.

Google told us about the Google File System in 2003, MapReduce in 2004, Bigtable in 2006, The Datacenter as a Computer in 2009, Percolator (real-time updates) in 2010, Pregel (graph processing) in 2010, Dremel (interactive analysis) in 2010, Spanner (globally distributed database) in 2012,  Omega (cluster scheduling) in 2013, Borg (cluster manager) in 2015, and Jupiter Rising (advanced networking) in 2015.

Sometime later we've seen the development of open source parallels like HDFS, Hadoop, HBase, Giraph, YARN, Drill, and Mesos. 

So, how can you rise up and meet the 10x challenge?

Murat Demirbas, a computer science and engineering professor at SUNY Buffalo, and awesome writer on all things distributed, came up with some good suggestions in How to go for 10X

Click to read more ...

Monday
Aug242015

Ask HighScalability: Choose an Async App Server or Multiple Blocking Servers?

Jonathan Willis, software developer by day and superhero by night, asked an interesting question via Twitter on StackOverflow

tl;dr Many Rails apps or one Vertx/Play! app?


I've been having discussions with other members of my team on the pros and cons of using an async app server such as the Play! Framework (built on Netty) versus spinning up multiple instances of a Rails app server. I know that Netty is asynchronous/non-blocking, meaning during a database query, network request, or something similar an async call will allow the event loop thread to switch from the blocked request to another request ready to be processed/served. This will keep the CPUs busy instead of blocking and waiting.

I'm arguing in favor or using something such as the Play! Framework or Vertx.io, something that is non-blocking... Scalable. My team members, on the other hand, are saying that you can get the same benefit by using multiple instances of a Rails app, which out of the box only comes with one thread and doesn't have true concurrency as do apps on the JVM. They are saying just use enough app instances to match the performance of one Play! application (or however many Play! apps we use), and when a Rails app blocks the OS will switch processes to a different Rails app. In the end, they are saying that the CPUs will be doing the same amount of work and we will get the same performance.

What do you think? The marketplace has seemingly moved, in the form of node.js, Golang, Akka, and even Java, to the async server model. Does that mean it's the only right way?

Here's my attempt at a response:

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Friday
Aug212015

Stuff The Internet Says On Scalability For August 21st, 2015

Hey, it's HighScalability time:


Hunter-Seeker? Nope. This is the beauty of what a Google driverless car sees. Great TED talk.
  • $2.8 billion: projected Instagram ad revenue in 2017; 1 trillion: Azure event hub events per month; 10 million: Stack Overflow questions asked; 1 billion: max volts generated by a lightening strike; 850: apps downloaded every second from the AppStore; 2000: years data can be stored in DNA; 60: # of robots needed to replace 600 humans; 1 million: queries per second with Nginx, Ubuntu, EC2

  • Quotable Quotes:
    • Tales from the Lunar Module Guidance Computer: we landed on the moon with 152 Kbytes of onboard computer memory.
    • @ijuma: Included in JDK 8 update 60 "changes GHASH internals from using byte[] to long, improving performance about 10x
    • @ErrataRob: I love the whining over the Bitcoin XT fork. It's as if anarchists/libertarians don't understand what anarchy/libertarianism means.
    • Network World: the LHC Computing Grid has 132,992 physical CPUs, 553,611 logical CPUs, 300PB of online disk storage and 230PB of nearline (magnetic tape) storage. It's a staggering amount of processing capacity and data storage that relies on having no single point of failure.
    • @petereisentraut: Chef is kind of a distributed monkey-patching festival running as root.
    • @SciencePorn: If you were to remove all of the empty space from the atoms that make up every human on earth, all humans would fit into an apple.
    • SDN for the cloud: Most of the concepts presented in the papers have been put into practice in Microsoft cloud infrastructures. As a result of these improvements, modern Azure services can carry up to 1,400,000 SQL databases. Moreover, a typical Azure event hub sees as high as 1 trillion events per month.

  • On the Alphabet Google reorg...what Horace Dediu has to say on functional vs divisional organizations may provide insight. A functional organization, which is used by the Army and Apple, prevents cross divisional fights for resources and power. Those are the kind of internal politics that kill a company. Why not just sidestep all that?

  • Here's how Pinterest shards MySQL to scale: All data needed to be replicated to a slave machine for backup, with high availability and dumping to S3 for MapReduce...You never want to read/write to a slave in production...Slaves lag, which causes strange bugs; I still recommend startups avoid the fancy new stuff — try really hard to just use MySQL. Trust me. I have the scars to prove it...We created a 64 bit ID that contains the shard ID...To create a new Pin, we gather all the data and create a JSON blob...A mapping table links one object to another...there are three primary ways to add more capacity...more RAM...open up new ranges...move some shards to new machines...This system is best effort. It does not give you Atomicity, Isolation or Consistency in all cases...We stored the shard configuration table in ZooKeeper...This system has been in production at Pinterest for 3.5 years now and will likely be in there forever. 

  • Nobody expects the quadruple fault! Google loses data as lightning strikes: four successive lightning strikes on the local utilities grid that powers our European datacenter caused a brief loss of power to storage systems...only a very small number of disks remained affected, totalling less than 0.000001% of the space of allocated persistent disks...full recovery is not possible.

  • Flxone upgraded to Go version 1.5 and reduced their 95-percentile garbage collector from 279 milliseconds down to just 10 ms, a 96% decrease in garbage collection pause time. Average request latency dropped by 53%. I wonder now if they can reduce the number of nodes required to meet their SLA? And would the results hold if they wrote their app more natively, that is to generate garbage?

Don't miss all that the Internet has to say on Scalability, click below and become eventually consistent with all scalability knowledge (which means this post has many more items to read so please keep on reading)...

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Wednesday
Aug192015

The Microsoft Take on Containers and Docker

This is a guest repost by Mark Russinovich, CTO of Microsoft Azure (and novelist!). We all benefit from a vibrant competitive cloud market and Microsoft is part of that mix. Here's a good container overview along with Microsoft's plan of attack. Do you like their story? Is it interesting? Is it compelling?

You can’t have a discussion on cloud computing lately without talking about containers. Organizations across all business segments, from banks and major financial service firms to e-commerce sites, want to understand what containers are, what they mean for applications in the cloud, and how to best use them for their specific development and IT operations scenarios.

From the basics of what containers are and how they work, to the scenarios they’re being most widely used for today, to emerging trends supporting “containerization”, I thought I’d share my perspectives to better help you understand how to best embrace this important cloud computing development to more seamlessly build, test, deploy and manage your cloud applications.

Containers Overview

In abstract terms, all of computing is based upon running some “function” on a set of “physical” resources, like processor, memory, disk, network, etc., to accomplish a task, whether a simple math calculation, like 1+1, or a complex application spanning multiple machines, like Exchange. Over time, as the physical resources became more and more powerful, often the applications did not utilize even a fraction of the resources provided by the physical machine. Thus “virtual” resources were created to simulate underlying physical hardware, enabling multiple applications to run concurrently – each utilizing fractions of the physical resources of the same physical machine.

We commonly refer to these simulation techniques as virtualization. While many people immediately think virtual machines when they hear virtualization, that is only one implementation of virtualization. Virtual memory, a mechanism implemented by all general purpose operating systems (OSs), gives applications the illusion that a computer’s memory is dedicated to them and can even give an application the experience of having access to much more RAM than the computer has available.

Containers are another type of virtualization, also referred to as OS Virtualization. Today’s containers on Linux create the perception of a fully isolated and independent OS to the application. To the running container, the local disk looks like a pristine copy of the OS files, the memory appears only to hold files and data of a freshly-booted OS, and the only thing running is the OS. To accomplish this, the “host” machine that creates a container does some clever things.

The first technique is namespace isolation. Namespaces include all the resources that an application can interact with, including files, network ports and the list of running processes. Namespace isolation enables the host to give each container a virtualized namespace that includes only the resources that it should see. With this restricted view, a container can’t access files not included in its virtualized namespace regardless of their permissions because it simply can’t see them. Nor can it list or interact with applications that are not part of the container, which fools it into believing that it’s the only application running on the system when there may be dozens or hundreds of others.

For efficiency, many of the OS files, directories and running services are shared between containers and projected into each container’s namespace. Only when an application makes changes to its containers, for example by modifying an existing file or creating a new one, does the container get distinct copies from the underlying host OS – but only of those portions changed, using Docker’s “copy-on-write” optimization. This sharing is part of what makes deploying multiple containers on a single host extremely efficient.

Second, the host controls how much of the host’s resources can be used by a container. Governing resources like CPU, RAM and network bandwidth ensure that a container gets the resources it expects and that it doesn’t impact the performance of other containers running on the host. For example, a container can be constrained so that it cannot use more than 10% of the CPU. That means that even if the application within it tries, it can’t access to the other 90%, which the host can assign to other containers or for its own use. Linux implements such governance using a technology called “cgroups.” Resource governance isn’t required in cases where containers placed on the same host are cooperative, allowing for standard OS dynamic resource assignment that adapts to changing demands of application code.

The combination of instant startup that comes from OS virtualization and reliable execution that comes from namespace isolation and resource governance makes containers ideal for application development and testing. During the development process, developers can quickly iterate. Because its environment and resource usage are consistent across systems, a containerized application that works on a developer’s system will work the same way on a different production system. The instant-start and small footprint also benefits cloud scenarios, since applications can scale-out quickly and many more application instances can fit onto a machine than if they were each in a VM, maximizing resource utilization.

Comparing a similar scenario that uses virtual machines with one that uses containers highlights the efficiency gained by the sharing. In the example shown below, the host machine has three VMs. In order to provide the applications in the VMs complete isolation, they each have their own copies of OS files, libraries and application code, along with a full in-memory instance of an OS. Starting a new VM requires booting another instance of the OS, even if the host or existing VMs already have running instances of the same version, and loading the application libraries into memory. Each application VM pays the cost of the OS boot and the in-memory footprint for its own private copies, which also limits the number of application instances (VMs) that can run on the host.

App Instances on Host

The figure below shows the same scenario with containers. Here, containers simply share the host operating system, including the kernel and libraries, so they don’t need to boot an OS, load libraries or pay a private memory cost for those files. The only incremental space they take is any memory and disk space necessary for the application to run in the container. While the application’s environment feels like a dedicated OS, the application deploys just like it would onto a dedicated host. The containerized application starts in seconds and many more instances of the application can fit onto the machine than in the VM case.

Containers on Host

Docker’s Appeal

Click to read more ...

Tuesday
Aug182015

Sponsored Post: Surge, Redis Labs, Jut.io, VoltDB, Datadog, MongoDB, SignalFx, InMemory.Net, Couchbase, VividCortex, MemSQL, Scalyr, AiScaler, AppDynamics, ManageEngine, Site24x7

Who's Hiring?

  • VoltDB's in-memory SQL database combines streaming analytics with transaction processing in a single, horizontal scale-out platform. Customers use VoltDB to build applications that process streaming data the instant it arrives to make immediate, per-event, context-aware decisions. If you want to join our ground-breaking engineering team and make a real impact, apply here.  

  • At Scalyr, we're analyzing multi-gigabyte server logs in a fraction of a second. That requires serious innovation in every part of the technology stack, from frontend to backend. Help us push the envelope on low-latency browser applications, high-speed data processing, and reliable distributed systems. Help extract meaningful data from live servers and present it to users in meaningful ways. At Scalyr, you’ll learn new things, and invent a few of your own. Learn more and apply.

  • UI EngineerAppDynamics, founded in 2008 and lead by proven innovators, is looking for a passionate UI Engineer to design, architect, and develop our their user interface using the latest web and mobile technologies. Make the impossible possible and the hard easy. Apply here.

  • Software Engineer - Infrastructure & Big DataAppDynamics, leader in next generation solutions for managing modern, distributed, and extremely complex applications residing in both the cloud and the data center, is looking for a Software Engineers (All-Levels) to design and develop scalable software written in Java and MySQL for backend component of software that manages application architectures. Apply here.

Fun and Informative Events

  • Surge 2015. Want to mingle with some of the leading practitioners in the scalability, performance, and web operations space? Looking for a conference that isn't just about pitching you highly polished success stories, but that actually puts an emphasis on learning from real world experiences, including failures? Surge is the conference for you.

  • Your event could be here. How cool is that?

Cool Products and Services

  • MongoDB Management Made Easy. Gain confidence in your backup strategy. MongoDB Cloud Manager makes protecting your mission critical data easy, without the need for custom backup scripts and storage. Start your 30 day free trial today.

  • In a recent benchmark for NoSQL databases on the AWS cloud, Redis Labs Enterprise Cluster's performance had obliterated Couchbase, Cassandra and Aerospike in this real life, write-intensive use case. Full backstage pass and and all the juicy details are available in this downloadable report.

  • Real-time correlation across your logs, metrics and events.  Jut.io just released its operations data hub into beta and we are already streaming in billions of log, metric and event data points each day. Using our streaming analytics platform, you can get real-time monitoring of your application performance, deep troubleshooting, and even product analytics. We allow you to easily aggregate logs and metrics by micro-service, calculate percentiles and moving window averages, forecast anomalies, and create interactive views for your whole organization. Try it for free, at any scale.

  • In a recent benchmark conducted on Google Compute Engine, Couchbase Server 3.0 outperformed Cassandra by 6x in resource efficiency and price/performance. The benchmark sustained over 1 million writes per second using only one-sixth as many nodes and one-third as many cores as Cassandra, resulting in 83% lower cost than Cassandra. Download Now.

  • Datadog is a monitoring service for scaling cloud infrastructures that bridges together data from servers, databases, apps and other tools. Datadog provides Dev and Ops teams with insights from their cloud environments that keep applications running smoothly. Datadog is available for a 14 day free trial at datadoghq.com.

  • Turn chaotic logs and metrics into actionable data. Scalyr replaces all your tools for monitoring and analyzing logs and system metrics. Imagine being able to pinpoint and resolve operations issues without juggling multiple tools and tabs. Get visibility into your production systems: log aggregation, server metrics, monitoring, intelligent alerting, dashboards, and more. Trusted by companies like Codecademy and InsideSales. Learn more and get started with an easy 2-minute setup. Or see how Scalyr is different if you're looking for a Splunk alternative or Sumo Logic alternative.

  • SignalFx: just launched an advanced monitoring platform for modern applications that's already processing 10s of billions of data points per day. SignalFx lets you create custom analytics pipelines on metrics data collected from thousands or more sources to create meaningful aggregations--such as percentiles, moving averages and growth rates--within seconds of receiving data. Start a free 30-day trial!

  • InMemory.Net provides a Dot Net native in memory database for analysing large amounts of data. It runs natively on .Net, and provides a native .Net, COM & ODBC apis for integration. It also has an easy to use language for importing data, and supports standard SQL for querying data. http://InMemory.Net

  • VividCortex goes beyond monitoring and measures the system's work on your MySQL and PostgreSQL servers, providing unparalleled insight and query-level analysis. This unique approach ultimately enables your team to work more effectively, ship more often, and delight more customers.

  • MemSQL provides a distributed in-memory database for high value data. It's designed to handle extreme data ingest and store the data for real-time, streaming and historical analysis using SQL. MemSQL also cost effectively supports both application and ad-hoc queries concurrently across all data. Start a free 30 day trial here: http://www.memsql.com/

  • aiScaler, aiProtect, aiMobile Application Delivery Controller with integrated Dynamic Site Acceleration, Denial of Service Protection and Mobile Content Management. Also available on Amazon Web Services. Free instant trial, 2 hours of FREE deployment support, no sign-up required. http://aiscaler.com

  • ManageEngine Applications Manager : Monitor physical, virtual and Cloud Applications.

  • www.site24x7.com : Monitor End User Experience from a global monitoring network.

If any of these items interest you there's a full description of each sponsor below. Please click to read more...

Click to read more ...

Monday
Aug172015

How Autodesk Implemented Scalable Eventing over Mesos

This is a guest post by Olivier Paugam, SW Architect for the Autodesk Cloud. I really like this post because it shows how bits of infrastructure--Mesos, Kafka, RabbitMQ, Akka, Splunk, Librato, EC2--can be combined together to solve real problems. It's truly amazing how much can get done these days by a small team.

I was tasked a few months ago to come up with a central eventing system, something that would allow our various backends to communicate with each other. We are talking about activity streaming backends, rendering, data translation, BIM, identity, log reporting, analytics, etc.  So something really generic with varying load, usage patterns and scaling profile.  And oh, also something that our engineering teams could interface with easily.  Of course every piece of the system should be able to scale on its own.

I obviously didn't have time to write too much code and picked up Kafka as our storage core as it's stable, widely used and works okay (please note I'm not bound to using it and could switch over to something else).  Now I of course could not expose it directly and had to front-end it with some API. Without thinking much I also rejected the idea of having my backend manage the offsets as it places too much constraint on how one deals with failures for instance.

So what did I end up with?

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Friday
Aug142015

Stuff The Internet Says On Scalability For August 14th, 2015

Hey, it's HighScalability time:


Being Google CEO: Nice. Becoming Tony Stark: Priceless (Alphabet)

 

  • $7: WeChat's revenue per user and there are 549 million of them; 60%: Etsy users using mobile; 10: times per second a self-driving car makes a decision; 900: calories in a litre of blood, vampires have very efficient metabolisms; 5 billion: the largest feature in the universe in light years

  • Quotable Quotes:
    • @sbeam: they finally had the Enigma machine. They opened the case. A card fell out. Turing picked it up. "Damn. They included a EULA." #oraclefanfic
    • kordless: compute and storage continue to track with Moore's Law but bandwidth doesn't. I keep wondering if this isn't some sort of universal limitation on this reality that will force high decentralization.
    • @SciencePorn: If you were to remove all of the empty space from the atoms that make up every human on earth, all humans would fit into an apple.
    • @adrianco: Commodity server with 1.4TB of RAM running a mix of 16GB regular DRAM and 128GB Memory1 modules.
    • @JudithNursalim: "One of the most scalable structure in history was the Roman army. Its unit: eight guys; the number of guys that fits in a tent" - Chris Fry
    • GauntletWizard: Google RPCs are fast. The RPC trace depth of many requests is >20 in miliseconds. Google RPCs are free - Nobody budgets for intradatacenter traffic. Google RPCs are reliable - Most teams hold themselves to a SLA of 4 9s, as measured by their customers, and many see >5 as the usual state of affairs.
    • @rzidane360: I am a Java library and I will start 50 threads and allocate a billion objects  on your behalf.
    • @codinghorror: From Sandy Bridge in Jan 2011 to Skylake in Aug 2015, x86 CPU perf increased ~25%. Same time for ARM mobile CPUs: ~800%.
    • @raistolo: "The cloud is not a cloud at all, it's a limited number of companies that have control over a large part of the Internet" @granick
    • Benedict Evans: since 1999 there are now roughly 10x more people online, US online revenues from ecommerce and advertising have risen 15x, and the cost of creating software companies has fallen by roughly 10x. 

  • App constellations aren't working. Is this another idea the West will borrow from the East? When One App Rules Them All: The Case of WeChat and Mobile in China: Chinese apps tend to combine as many features as possible into one application. This is in stark contrast to Western apps, which lean towards “app constellations”.

  • It doesn't get much more direct than this. Labellio: Scalable Cloud Architecture for Efficient Multi-GPU Deep Learning: The Labellio architecture is based on the modern distributed computing architectural concept of microservices, with some modification to achieve maximal utilization of GPU resources. At the core of Labellio is a messaging bus for deep learning training and classification tasks, which launches GPU cloud instances on demand. Running behind the web interface and API layer are a number of components including data collectors, dataset builders, model trainer controllers, metadata databases, image preprocessors, online classifiers and GPU­-based trainers and predictors. These components all run inside docker containers. Each component communicates with the others mainly through the messaging bus to maximize the computing resources of CPU, GPU and network, and share data using object storage as well as RDBMS.

  • How do might your application architecture change using Lambda? Here's a nice example of Building Scalable and Responsive Big Data Interfaces with AWS Lambda. A traditional master-slave or job server model is not used, instead Lambda is used to connect streams or processes in a pipeline. Work is broken down into smaller, parallel operations on small chunks with Lambda functions doing the heavy lifting. The pipeling consists of a S3 key lister, AWS Lambda invoker/result aggregator, Web client response handle. 

  • The Indie Web folks have put together a really big list of Site Deaths, that is sites that have had their plugs pulled, bits blackened, dreams dashed. Take some time, look through, and say a little something for those that have gone before.

Don't miss all that the Internet has to say on Scalability, click below and become eventually consistent with all scalability knowledge (which means this post has many more items to read so please keep on reading)...

Click to read more ...

Wednesday
Aug122015

Why My Water Droplet Is Better Than Your Hadoop Cluster

We’ve had computation using slime mold and soap film, now we have computation using water droplets. Stanford bioengineers have built a “fully functioning computer that runs like clockwork - but instead of electrons, it operates using the movement of tiny magnetised water droplets.”

 

By changing the layout of the bars on the chip it's possible to make all the universal logic gates. And any Boolean logic circuit can be built by moving the little magnetic droplets around. Currently the chips are about half the size of a postage stamp and the droplets are smaller than poppy seeds.

What all this means I'm not sure, but pavo6503 has a comment that helps understand what's going on:

Logic gates pass high and low states. Since they plan to use drops of water as carriers and the substances in those drops to determine what the high/low state is they could hypothetically make a filter that sorts drops of water containing 1 to many chemicals. Pure water passes through unchanged. water with say, oil in it, passes to another container, water with alcohol to another. A "chip" with this setup could be used to purify water where there are many contaminants you want separated.

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Monday
Aug102015

How Google Invented an Amazing Datacenter Network Only They Could Create

 

Google with justly earned pride recently announced:

Today at the 2015 Open Network Summit, we are revealing for the first time the details of five generations of our in-house network technology. From Firehose, our first in-house datacenter network, ten years ago to our latest-generation Jupiter network, we’ve increased the capacity of a single datacenter network more than 100x. Our current generation — Jupiter fabrics — can deliver more than 1 Petabit/sec of total bisection bandwidth. To put this in perspective, such capacity would be enough for 100,000 servers to exchange information at 10Gb/s each, enough to read the entire scanned contents of the Library of Congress in less than 1/10th of a second.

Google’s datacenter network is the magic behind what makes much of Google really work. But what is “bisectional bandwidth” and why does it matter? We talked about bisectional bandwidth a while back in Changing Architectures: New Datacenter Networks Will Set Your Code And Data Free. In short, bisectional bandwidth refers to the networks Google servers use to talk to each other.

Historically datacenter networks were oriented around talking to users. Let’s say a request for a web page came in from a browser. The request would go to a server and a reply was crafted by talking to just a few other servers, or perhaps even none at all, and the reply would be sent back to the client. This style of network is called a North/South oriented network. Very little internal communication was needed to implement a request.

That all changed as website and API services grew richer over time. Now literally thousands of backend requests can be made to create a single web page. Mind blowing. This meant communication shifted from being dominated by talking to users to talking to other machines within a datacenter. So these are called East/West oriented networks.

The shift to East/West dominate communication patterns meant a different topology was needed for datacenter networks. The old traditional fat tree network designs were out and something new needed to take its place.

Google has been on the forefront of developing new rich service supportive network designs largely because of their guiding vision of seeing The Datacenter as a Computer. Once your datacenter is the computer then your network is equivalent to a backplane on a single computer, so it must be as fast and reliable as possible so remote disk and remote storage can be accessed as if they were local.

Google’s efforts revolve around a three pronged plan of attack: use a Clos topology, use SDN (Software Defined Networking), and build their own custom gear in their own Googlish way.

Until now we’ve had a limited exposure to Google’s network designs. While we don’t exactly have an all access pass, Amin Vahdat, Google Fellow and Technical Lead for networking at Google, shared a lot of juicy details in a great talk: ONS [Open Networking Summit] 2015: Wednesday Keynote. There’s also a paper: Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google’s Datacenter Network.

Why release details earlier than they usually do? Google has some real competition with Amazon and they need to find compelling points of differentiation. Google hopes their datacenter network is one such point.

So what makes Google different? The overall message:

  • The end of Moore’s Law means how programs are built is changing.

  • Google has figured it out. Google knows how to build great networks and achieve proper datacenter balance.

  • You can prosper by taking advantage of the innovations and capabilities of Google’s Cloud Platform, the very same platform that powers Google Search.

  • So climb on board, the network is fine! 

Is that enough? Perhaps it's not a message with mass appeal, but it may find a home with the discriminating buyer. 

Some key points from the talk for me:

  • We don’t know how to build big networks that deliver lots of bandwidth. Google says their network provides 1 Pb/sec of total bisection bandwidth, but it turns out that’s not nearly enough. To support a datacenter’s worth of large compute servers you’ll need 5 Pb/sec networks. Keep in mind the entire internet today is probably near 200Tb/s.

  • It’s more efficient to schedule jobs over huge clusters. Otherwise you have leftover CPU in one place and leftover memory in another. So if you can build your system correctly, a datacenter scale computer gives you a decided economy of scale.

  • Google built their datacenter network system using lessons they learned from the server and storage world: scale out, logically centralize, use commodity components, and never ever manage singlets of anything. Manage all your servers, storage, and networks as a unified whole.

  • The I/O gap is huge. Amin says it has to get solved, if it doesn’t then we’ll stop innovating. Storage capacity has increased through disaggregation. The opportunity is to access global datacenter storage as if it were local. This will get harder and harder with flash and NVM. A new tier of flash and NWM will completely change programming models. Note: unfortunately he didn’t expand on this notion, I dearly wished he had. Amin, can we talk?

What you look for in a good story are characters that act from a core identity. Here we see Google operating from a unique vision that grew organically from their deep experience building scalable software systems. Probably only Google would have had the guts to follow their vision through and build a datacenter network so completely different from accepted wisdom. That takes huge huevos. And it makes for a good story.

Here’s my hopelessly inadequate gloss on the talk:

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