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Google Compute Engine enables users (utilising authentication based on OAuth 2.0) to launch virtual machines (VMs) on demand. VMs can be launched from the standard images or custom images created by users. Google Compute Engine can be accessed via the Developer Console, RESTful API or command-line interface (CLI).
The computation would have taken over a month on internal resources, but completed in 7 hours running on 70,000 cores in Amazon Web Services, at a cost of less than $6,000. [14] [15] In September 2015, Cycle Computing and the Broad Institute announced a 50,000 core cluster to run on Google Compute Engine. [16]
It also updated its App Engine hosting service with PHP runtime, calling it "the most requested feature," and launched Google Cloud Datastore to go up against AWS' cloud storage services.
Google Cloud Platform is a part [7] of Google Cloud, which includes the Google Cloud Platform public cloud infrastructure, as well as Google Workspace (G Suite), enterprise versions of Android and ChromeOS, and application programming interfaces (APIs) for machine learning and enterprise mapping services.
Autoscaling, also spelled auto scaling or auto-scaling, and sometimes also called automatic scaling, is a method used in cloud computing that dynamically adjusts the amount of computational resources in a server farm - typically measured by the number of active servers - automatically based on the load on the farm.
Bigtable development began in 2004. [1] It is now used by a number of Google applications, such as Google Analytics, [2] web indexing, [3] MapReduce, which is often used for generating and modifying data stored in Bigtable, [4] Google Maps, [5] Google Books search, "My Search History", Google Earth, Blogger.com, Google Code hosting, YouTube, [6] and Gmail. [7]
Software Linux FreeBSD Windows Bare Metal AppScale? ? ? Cloud Foundry: Yes No Yes Yes Cloud.com / CloudStack : Yes No Yes Yes Eucalyptus: Yes No No Yes [41]: Flexiant Limited
Commodity computing (also known as commodity cluster computing) involves the use of large numbers of already-available computing components for parallel computing, to get the greatest amount of useful computation at low cost. [1] This is a useful alternative to high-cost superminicomputers or boutique computers.
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