Espresso makes Google cloud faster, more available and cost effective by extending SDN to the public internet
Today, we gave a keynote presentation at the Open Networking Summit, where we shared details about Espresso, Google’s peering edge architecture—the latest offering in our Software Defined Networking (SDN) strategy. Espresso has been in production for over two years and routes 20 percent of our total traffic to the internet—and growing. It’s changing the way traffic is directed at the peering edge,
A Look Inside Google’s Data Center Networks
1) Google has been developing their own data center network architectures using merchant silicon switches and centralized network control since 2005 to keep up with increasing bandwidth demands. 2) Their network designs have evolved from Firehose and Watchtower to the current Saturn and Jupiter networks, increasing port speeds from 1/10Gbps to 40/100Gbps and aggregate bandwidth from terabits to pe
Mesos, Omega, Borg: A Survey : umbrant
Google recently unveiled one of their crown jewels of system infrastructure: Borg, their cluster scheduler. This prompted me to re-read the Mesos and Omega papers, which deal with the same topic. I thought it'd be interested to do a compare and contrast of these systems. Mesos gets credit for the groundbreaking idea of two-level scheduling, Omega improved upon this with an analogy from databases,
dl.google.com: Powered by Go
dl.google.com: Powered by Go 26 July 2013 Brad Fitzpatrick Gopher, Google Overview / tl;dw: dl.google.com serves Google downloads Was written in C++ Now in Go Now much better Extensive, idiomatic use of Go's standard library ... which is all open source composition of interfaces is fun groupcache, now Open Source, handles group-aware caching and cache-filling 2 too long... 3 me Brad Fitzpatrick br
How Google Backs Up the Internet Along With Exabytes of Other Data - High Scalability -
« Sponsored Post: Logentries, Booking, Apple, MongoDB, BlueStripe, AiScaler, Aerospike, LogicMonitor, AppDynamics, ManageEngine, Site24x7 | Main | Stuff The Internet Says On Scalability For January 31st, 2014 » Raymond Blum leads a team of Site Reliability Engineers charged with keeping Google's data secret and keeping it safe. Of course Google would never say how much data this actually is, but f
The Tail at Scale
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Reddit - Dive into anything
We are the Google Site Reliability team. We make Google’s websites work. Ask us Anything! Hello, reddit! We are the Google Site Reliability (SRE) team. We’re responsible for the 24x7 operation of Google.com, as well as the technical infrastructure behind many other Google products such as GMail, Maps, G+ and other stuff you know and love. We’ve been traditionally invisible and behind-the-scenes bu
C is for Compute - Google Compute Engine (GCE) - High Scalability -
After poking around the Google Compute Engine (GCE) documentation I had some trouble creating a mental model of how GCE works. Is it like AWS, GAE, Rackspace, just what is it? After watching Google I/O 2012 - Introducing Google Compute Engine and Google Compute Engine -- Technical Details, it turns out my initial impression, that GCE is disarmingly straightforward, turns out to be the point. The f
Google on Latency Tolerant Systems: Making a Predictable Whole Out of Unpredictable Parts - High Scalability -
In Taming The Long Latency Tail we covered Luiz Barroso’s exploration of the long tail latency (some operations are really slow) problems generated by large fanout architectures (a request is composed of potentially thousands of other requests). You may have noticed there weren’t a lot of solutions. That’s where a talk I attended, Achieving Rapid Response Times in Large Online Services (slide deck
Building Software Systems at Google and Lessons Learned
Building Software Systems at Google and Lessons Learned Jeff Dean jeff@google.com Plan for Today • Evolution of various systems at Google –computing hardware –core search systems –infrastructure software • Techniques for building large-scale systems –decomposition into services –design patterns for performance & reliability –Joint work with many, many people • # docs: tens of millions to tens of b
Google: Taming the Long Latency Tail - When More Machines Equals Worse Results - High Scalability -
« Sponsored Post: Nokia, Oracle, Percona Live, AiCache, ElasticHosts, Logic Monitor, Attribution Modeling, New Relic, AppDynamics, CloudSigma, ManageEngine, Site24x | Main | Stuff The Internet Says On Scalability For March 9, 2012 » Likewise the current belief that, in the case of artificial machines the very large and the very small are equally feasible and lasting is a manifest error. Thus, for
The Great Disk Drive in the Sky: How Web giants store big—and we mean big—data
Google technicians test hard drives at their data center in Moncks Corner, South Carolina Consider the tech it takes to back the search box on Google's home page: behind the algorithms, the cached search terms, and the other features that spring to life as you type in a query sits a data store that essentially contains a full-text snapshot of most of the Web. While you and thousands of other peopl
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Google's Transition from Single Datacenter, to Failover, to a Native Multihomed Architecture - High Scalability -
http://conferences.sigcomm.org/sigcomm/2015/pdf/papers/p183.pdf
Dataflow | Google Cloud
Large-scale cluster management at {Google} with {Borg}
How Search Works – Inside Search – Google
HackerKevin - Computer Kevin: Eng: Great Engineering Comes From Great Infrastructure
Google Spannerまとめ
Videolectures