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This runbook automates scheduled startup and shutdown of Azure virtual machines. You can implement multiple granular power schedules for your virtual machines using simple tag metadata in the Azure portal or through PowerShell. For example, you could tag a group of VMs to be shut down between the hours of 10:00 PM and 6:00 AM, all day on Saturdays and Sundays, and during specific days of the year, like December 25.
The runbook is intended to run on a schedule in an Azure Automation account, with a configured subscription and associated access credentials. For example, it can run once every hour, checking all the schedule tags it finds on your virtual machine resource groups. If the current time falls within a shutdown period you’ve defined, the runbook will stop the VM if it is running, preventing any compute charges. If the current time falls outside of any tagged shutdown period, this means the VM should be running, so the runbook starts any such VM that is stopped.
Once the runbook is in place and scheduled, the only configuration required can be done through simple tagging of resources, and the runbook will implement whatever power schedules it finds during its next scheduled run. Think of this as a quick and basic power management scheduling solution for your Azure virtual machines.
Tags are great for adding metadata to our resources in Azure for purposes like these, but as of this writing (May 2015), tags are not able to be applied to a Virtual Machine resource – only to Resource Groups. Therefore we can only schedule using this approach at the resource group level, not (yet) for individual VMs directly. Until this changes, you’ll need to make sure all Azure VMs which need a specific shutdown schedule are contained in a resource group where the schedule tag is assigned. To use different schedules for different VMs, you’d need to contain them within separate resource groups.
This site provides links to random videos hosted at YouTube, with the emphasis on random.
The original idea for this site actually stemmed from another idea to provide a way of benchmarking the popularity of a video against the general population of YouTube videos. There are probably sites that do this by now, but there wasn’t when we started out. Anyway, in order to figure out how popular any one video is, you need a pretty large sample of videos to rank it against. The challenge is that the sample needs to be very random in order to properly rank a video and YouTube doesn’t appear to provide a way to obtain large numbers of random video IDs.
Even if you search on YouTube for a random string, the set of results that will be returned will still be based on popularity, so if you’re using this approach to build up your sample, you’re already in trouble. It turns out there is a multitude of ways in which the YouTube search function makes it very difficult to retrieve truly random results.
So how can we provide truly random links to YouTube videos? It turns out that the YouTube programming interface (API) provides additional functions that allow the discovery of videos which, with the right approach, are much more random. Using a number of tricks, combined some subtle manipulation of the space-time fabric, we have managed to create a process that yields something very close to 100% random links to YouTube videos.
YouTube is an American video-sharing website headquartered in San Bruno, California. YouTube allows users to upload, view, rate, share, add to playlists, report, comment on videos, and subscribe to other users. It offers a wide variety of user-generated and corporate media videos. Available content includes video clips, TV show clips, music videos, short and documentary films, audio recordings, movie trailers, live streams, and other content such as video blogging, short original videos, and educational videos. Most content on YouTube is uploaded by individuals, but media corporations including CBS, the BBC, Vevo, and Hulu offer some of their material via YouTube as part of the YouTube partnership program. Unregistered users can only watch videos on the site, while registered users are permitted to upload an unlimited number of videos and add comments to videos. Videos deemed potentially inappropriate are available only to registered users affirming themselves to be at least 18 years old.
YouTube and selected creators earn advertising revenue from Google AdSense, a program which targets ads according to site content and audience. The vast majority of its videos are free to view, but there are exceptions, including subscription-based premium channels, film rentals, as well as YouTube Music and YouTube Premium, subscription services respectively offering premium and ad-free music streaming, and ad-free access to all content, including exclusive content commissioned from notable personalities. As of February 2017, there were more than 400 hours of content uploaded to YouTube each minute, and one billion hours of content being watched on YouTube every day. As of August 2018, the website is ranked as the second-most popular site in the world, according to Alexa Internet, just behind Google. As of May 2019, more than 500 hours of video content are uploaded to YouTube every minute.
YouTube has faced criticism over aspects of its operations, including its handling of copyrighted content contained within uploaded videos, its recommendation algorithms perpetuating videos that promote conspiracy theories and falsehoods, hosting videos ostensibly targeting children but containing violent and/or sexually suggestive content involving popular characters, videos of minors attracting pedophilic activities in their comment sections, and fluctuating policies on the types of content that is eligible to be monetized with advertising.