New Hampshire "Ballot Selfie" Ban Is Unconstitutional, Appeals Court Rules

Mike Blake / Reuters

WASHINGTON — A New Hampshire law that forbids people from taking so-called “ballot selfies” is unconstitutional, a federal appeals court ruled on Wednesday.

“New Hampshire may not impose such a broad restriction on speech by banning ballot selfies in order to combat an unsubstantiated and hypothetical danger” of vote buying or voter intimidation, Judge Sandra Lynch wrote for the 1st Circuit Court of Appeals.

“We repeat the old adage: &;a picture is worth a thousand words.&039;”

The ACLU had brought a lawsuit challenging the law on behalf of three people investigated for alleged violations of the law during the 2014 election. At the appeals court, they were backed by the Reporters Committee for Freedom of the Press and Snapchat, among others.

In the key part of the ruling, Lynch wrote:

Read the opinion:

Quelle: <a href="New Hampshire "Ballot Selfie" Ban Is Unconstitutional, Appeals Court Rules“>BuzzFeed

How we made Kubernetes insanely easy to install

Editor’s note: Today’s post is by Luke Marsden, Head of Developer Experience, at Weaveworks, showing the Special Interest Group Cluster-Lifecycle’s recent work on kubeadm, a tool to make installing Kubernetes much simpler.Over at SIG-cluster-lifecycle, we’ve been hard at work the last few months on kubeadm, a tool that makes Kubernetes dramatically easier to install. We’ve heard from users that installing Kubernetes is harder than it should be, and we want folks to be focused on writing great distributed apps not wrangling with infrastructure!There are three stages in setting up a Kubernetes cluster, and we decided to focus on the second two (to begin with):Provisioning: getting some machinesBootstrapping: installing Kubernetes on them and configuring certificatesAdd-ons: installing necessary cluster add-ons like DNS and monitoring services, a pod network, etcWe realized early on that there’s enormous variety in the way that users want to provision their machines.They use lots of different cloud providers, private clouds, bare metal, or even Raspberry Pi’s, and almost always have their own preferred tools for automating provisioning machines: Terraform or CloudFormation, Chef, Puppet or Ansible, or even PXE booting bare metal. So we made an important decision: kubeadm would not provision machines. Instead, the only assumption it makes is that the user has some computers running Linux.Another important constraint was we didn’t want to just build another tool that “configures Kubernetes from the outside, by poking all the bits into place”. There are many external projects out there for doing this, but we wanted to aim higher. We chose to actually improve the Kubernetes core itself to make it easier to install. Luckily, a lot of the groundwork for making this happen had already been started.We realized that if we made Kubernetes insanely easy to install manually, it should be obvious to users how to automate that process using any tooling.So, enter kubeadm. It has no infrastructure dependencies, and satisfies the requirements above. It’s easy to use and should be easy to automate. It’s still in alpha, but it works like this:You install Docker and the official Kubernetes packages for you distribution.Select a master host, run kubeadm init.This sets up the control plane and outputs a kubeadm join […] command which includes a secure token.On each host selected to be a worker node, run the kubeadm join […] command from above.Install a pod network. Weave Net is a great place to start here. Install it using just kubectl apply -f https://git.io/weave-kubePresto! You have a working Kubernetes cluster! Try kubeadm today. For a video walkthrough, check this out:Follow the kubeadm getting started guide to try it yourself, and please give us feedback on GitHub, mentioning @kubernetes/sig-cluster-lifecycle!Finally, I want to give a huge shout-out to so many people in the SIG-cluster-lifecycle, without whom this wouldn’t have been possible. I’ll mention just a few here:Joe Beda kept us focused on keeping things simple for the user.Mike Danese at Google has been an incredible technical lead and always knows what’s happening. Mike also tirelessly kept up on the many code reviews necessary.Ilya Dmitrichenko, my colleague at Weaveworks, wrote most of the kubeadm code and also kindly helped other folks contribute.Lucas Käldström from Finland has got to be the youngest contributor in the group and was merging last-minute pull requests on the Sunday night before his school math exam.Brandon Philips and his team at CoreOS led the development of TLS bootstrapping, an essential component which we couldn’t have done without.Devan Goodwin from Red Hat built the JWS discovery service that Joe imagined and sorted out our RPMs.Paulo Pires from Portugal jumped in to help out with external etcd support and picked up lots of other bits of work.And many other contributors! This truly has been an excellent cross-company and cross-timezone achievement, with a lovely bunch of people. There’s lots more work to do in SIG-cluster-lifecycle, so if you’re interested in these challenges join our SIG. Looking forward to collaborating with you all!–Luke Marsden, Head of Developer Experience at WeaveworksTry kubeadm to install Kubernetes todayGet involved with the Kubernetes project on GitHub Post questions (or answer questions) on Stack Overflow Connect with the community on SlackFollow us on Twitter @Kubernetesio for latest updates
Quelle: kubernetes

Image2Docker: A New Tool for Prototyping Windows VM Conversions

Docker is a great tool for building, shipping, and running your applications. Many companies are already moving their legacy applications to Docker containers and now with the introduction of the Microsoft Windows Server 2016, Docker Engine can not run containers  natively on Windows.To make it even easier, there’s a new prototyping tool for Windows VMs that shows you how to replicate a VM Image to a container.
Docker Captain Trevor Sullivan recently released the Image2Docker tool, an open source project we’re hosting on GitHub. Still in it’s early stages, Image2Docker is a Powershell module that you can point at a virtual hard disk image, scan for common Windows components and suggest a Dockerfile. And to make it even easier, we’re hosting it in the Powershell Gallery to make it easy to install and use.
In Powershell, just type:
Install-Module -Name Image2Docker
And you’ll have access to Get-WindowsArtifacts and ConvertTo-Dockerfile. You can even select which discovery artifacts to search for.

Currently Image2Docker supports VHD, VHDK, and WIM images. If you have a VMDK, Microsoft provides a great conversion tool to convert VMDK images to VHD images.
And as an open source project, lead by a Docker Captain, it’s easy to contribute. We welcome contributions to add more discovery objects and functionality.
More Resources:

Check out Image2Docker in the Powershell Gallery
Contribute to Image2Docker
Learn More: Docker and Windows Server
Get Started with Windows Server Containers with Docker

Introducing Image2Docker: A New Tool for Prototyping @Windows VM Conversions by @pcgeek86Click To Tweet

The post Image2Docker: A New Tool for Prototyping Windows VM Conversions appeared first on Docker Blog.
Quelle: https://blog.docker.com/feed/

Using Google’s cloud networking products: a guide to all the guides

Posted by Mike Truty, Cloud Solutions Architect

I’m a relative newcomer to Google Cloud Platform. After nine years working in Technical Infrastructure, I recently joined the team to work hand-in-hand with customers building out next-generation applications and services on the platform. In this role, I realized that my privileged understanding of how we build our systems can be hard to come by from outside the organization. That is, unless you know where to look.

I recently spent a bunch of time hopping around the Google Cloud Networking pages under the main GCP site, looking for materials that could help a customer better understand our approach.

What follows is a series of links for anyone who may want an introduction to Google Cloud Networking, presented in digestible pieces and ordered to build on previous content.

Getting started
First, for some quick 15-minute background, I recommend this Google Cloud Platform Overview. It’s a one-page survey of all the necessary concepts you need to work in Cloud Platform. Then, you may want to scan the related Cloud Platform Services doc, another one-pager that introduces the primary customer-facing services (including networking services) that you might need. It’s not obvious but Cloud Platform networking also lays the foundation for the newer managed services mentioned including Google Container Engine (Kubernetes) and Cloud Dataflow. After all that, you’ll have a good idea of the landscape and be ready to actually do something in GCP!

(click to enlarge)

Networking Codelabs
Google has an entire site devoted to Codelabs — my favorite way to learn nontrivial technical concepts. Within the Cloud Codelabs there are two really excellent networking Codelabs: Networking 101 and Networking 102. I recommend them highly for a few reasons. Each one only takes about 90 minutes end-to-end; each is a quick survey of a few of the most commonly used features in cloud networking; both include really helpful hints about performance and, most importantly, after completing these Codelabs, you’ll have a really good sandbox for experimenting in cloud networking on Google Cloud Platform.

Google Cloud Networking references
Another question you may have is what are the best Google Cloud Networking reference docs? The Google Cloud Networking feature docs are split between two main landing pages: the Cloud Networking Products page and the Compute Engine networking page. The products page introduces the main product feature areas: Cloud Virtual Network, Autoscaling and Load Balancing, Global DNS, Cloud Interconnect and Cloud CDN. Be sure to scroll down to the end, because there are some really valuable links to guides and resources at the very bottom of each page that a lot of people miss out on.

The Compute Engine networking page is a treasure trove of all kinds of interesting details that you won’t find anywhere else. It includes the picture I hold in my mind for how networks and subnetworks are related to regions and zones, details about quotas, default IP ranges, default routes, firewall rules, details about internal DNS, and some simple command line examples using gcloud.

An example of the kind of gem you’ll find on this page is a little blurb on measuring network throughput that links to the PerfKitBenchMarker tool, an open-source benchmark tool for comparing cloud providers (more on that below). I return to this page frequently and find things explained that previously confused me.

For future reference, the Google Cloud Platform documentation also maintains a list of networking tutorials and solutions documents with some really interesting integration topics. And you should definitely check out Google Cloud Platform for AWS Professionals: Networking, an excellent, comprehensive digest of networking features.

Price and performance
Before you do too much, you might want to get a sense for how much of your free quota it will cost you to run through more networking experiments. Get yourself acquainted with the Cloud Platform Pricing page as a reference (notice the “Free credits” link at the bottom of the page). Then, you can find the rest of what you need under Compute Engine Pricing. There, you can see rates for the standard machine types used in the Codelabs, and also a link to General network pricing. A little further down, you’ll find the IP address pricing numbers. Finally, you may find it useful to click through the link at the very bottom to the estimated billing charges invoice page for a summary of what you spent on the codelabs.

Once you’ve done that, you can start thinking about the simple performance and latency tests you completed in the Codelabs. There’s a very helpful discussion on egress throughput caps buried in the Networking and Firewalls doc and you can run your own throughput experiments with PerfKitBenchMarker (sources). This tool does all the heavy lifting with respect to spinning up instances, and understands how different cloud providers define regions, making for relevant comparisons. Also, with PerfKitBenchmaker, someone else has already done the hard work of identifying the accepted benchmarks in various areas.

Real world use cases
Now that you understand the main concepts and features behind Google Cloud Networking, you might want to see how others put them all together. A common first question is how to set things up securely. Securely Connecting to VM Instances is a really good walkthrough that includes more overviews of key topics (firewalls, HTTPS/SSL, VPN, NAT, serial console), some useful gcloud examples and a nice picture that reflects the jumphost setup in the codelab.

Next you should watch two excellent videos from GCP Next 2016: Seamlessly Migrating your Networks to GCP and Load Balancing, Autoscaling & Optimizing Your App Around the Globe. What I like about these videos is that they hit all the high points for how people talk about public cloud virtual networking, and offer examples of common approaches used by large early adopters.

A common question about cloud networking technologies is how to distribute your services around the globe. The Regions and Zones document explains specifically where GCP resources reside, and Google’s research paper Software Defined Networking at Scale (more below) has pretty map-based pictures of Google’s Global CDN and inter-Datacenter WAN that I really like. This Google infrastructure page has zoomable maps with Google’s data centers around the world marked and you can read how Google uses its four undersea cables, with more ‘under’ the horizon, to connect them here.

Finally, you may want to check out this sneaky-useful collection of articles discussing approaches to geographic management of data. I plan to go through the solutions referenced at the bottom of this page to get more good ideas on how to use multiple regions effectively.

Another thing that resonated with me from both GCP Next 2016 videos was the discussion about how easy it is to setup and manage services in GCP to serve from closest, low-latency instances using a single global Anycast VIP. For more on this, the Load Balancing and Scaling concept doc offers a really nice overview of the topic. Then, for some initial exploration of load balancing, check out Setting Up Network Load Balancing.

And in case you were wondering from exactly where Google peers and serves CDN content, visit the Google Edge Network/Peering site and PeeringDB for more details. The peering infrastructure page has zoomable maps where you can see Google’s Edge PoPs and nodes.

Best practices
There’s also a wealth of documents about best practices for Google Cloud Networking. I really like the Best Practices for Networking and Security within the Best Practices for Enterprise Organizations document, and DDoS Best Practices doc provides more useful ways to think about building a global service.

Another key concept to wrap your head around is Cloud Identity & Access Management (IAM). In particular, check out the Understanding Roles doc for its introduction to network- and security-specific roles. Service accounts play a key role here. Understanding Service Accounts walks you through the considerations, and Using IAM Securely offers some best practices checklists. Also, for some insight into where this all leads, check out Access Control for Organizations using IAM [Beta].

A little history of Google Cloud Networking
All this research about Google Cloud Networking may leave you wanting to know more about its history. I checked out the research papers referenced in the previously mentioned video Seamlessly Migrating your Networks to GCP and — warning — they’re deep, but they’ll help you understand the fundamentals of how Google Cloud Networking has evolved over the past decade, and how its highly distributed services deliver the performance and competitive pricing for which it’s known.

Google’s network-related research papers fall into two categories:

Cloud Networking fundamentals

Enter the Andromeda zone – Google Cloud Platform’s latest networking stack, a 2014 blog that details the fundamentals of network virtualization.
Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google’s Datacenter Network. This 2015 paper provides an excellent description of the evolution of datacenter networking at Google. It even comes with a video.
Maglev: A Fast and Reliable Software Network Load Balancer, a 2016 paper that presents an overview of distributed load balancing.

Networking background

A Guided Tour of Data-Center Networking. This 2012 article provides a high-level system overview.
B4: Experience with a Globally Deployed Software Defined WAN. Read this 2013 paper for a detailed look at Google’s quest for simpler and more efficient WAN.
Software Defined Networking at Scale, slides from 2014 about SDN models.
A look inside Google’s Data Center Networks. “Jupiter fabrics…can deliver more than 1 Petabit/sec…enough for 100,000 servers to exchange information at 10Gb/s each, enough to read the entire…Library of Congress in less than 1/10th of a second.”

The Andromeda network architecture (source)

I hope this post is useful, and that these resources help you better understand the ins and outs of Google Cloud Networking. If you have any other good resources, be sure to share them in the comments.

Quelle: Google Cloud Platform

What Killed The Blackberry?

Today, Blackberry announced it will no longer make hardware. Here&;s the definitive history of the once-dominant smartphone&8217;s downfall.

This is the original iPhone, a leading smartphone.

Apple

This is the iPhone 3G, which added 3G capabilities to the original iPhone smartphone.

Apple

Here is the iPhone 3GS, which added more speed to the iPhone 3G.

Apple

This is an image of the iPhone 4, the next in the series of the iPhone smartphone line. It had a new, better screen and was faster than its predecessor, the iPhone 3GS.

Apple


View Entire List ›

Quelle: <a href="What Killed The Blackberry?“>BuzzFeed

Hybrid cloud through the IBM Edge kaleidoscope

IBM Edge 2016 — a global conference that provides a platform for IT leaders to design, build and deliver infrastructure in the cloud — came, mesmerized, and conquered.
The hybrid cloud team engaged with clients, talking with them about new technologies and inspiring them in thought leadership sessions with our subject matter experts, demos, client stories and more.
The event kicked off with general sessions full of stories about how hybrid cloud is helping organizations. In one session, Tom Rosamilia, Senior Vice President of IBM Systems, spoke about how organizations lead by example with hybrid cloud. Red Bull Racing, the F1 Double World Champions, took the stage and told us how they roared and rose high over the competitors using cutting-edge technology from IBM.
The IBM Cloud Integration booth was where all the action was at IBM Edge, where visitors could experience the technology that makes hybrid cloud a reality. This is where the IBM Cloud Orchestrator team showed clients exactly how Cloud Orchestrator allows them to manage public, private, and hybrid clouds with a rapid configuration, provisioning and deployment process. Cloud Orchestrator enables organizations to go live sooner as they develop and test applications, integrating various tools with cloud services.
Here’s a quick look at what it can do:

Rapidly accelerate delivery times. It exponentially improves service delivery times and cuts provisioning times from weeks to minutes.
Increase profits by reducing costs. Cloud Orchestrator gets rid of process-heavy management tools and automates manual workloads.
Innovate and lead with confidence. Users can leverage public cloud services to innovate while keeping business policies intact within IT services.

IBM Cloud Orchestrator transforms the IT services wing into a self-service organization. One example of a customer who saw a massive decrease in delivery time was Bob Hunt, Enterprise Systems Management Manager at American Greetings. He said his company “cut server provisioning times from two weeks to 20 minutes.”
Likewise, Paul Lu, CEO of Wuxi Lake Cloud, said, “Previously, our quickest deployment was two weeks, but some took as long as two months. Now we’re doing it in a week, and we’re anticipating three-day deployments in the future. We’ve reduced system recovery times by about 75 percent.”
IBM Cloud Orchestrator has become a leader in hybrid cloud management by automating cloud services and rapidly speeding up agile IT Service delivery.
Learn more about IBM Cloud Orchestrator.
 
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Quelle: Thoughts on Cloud