Abstract: Techniques for host resource group sharing in a provider network are described. A resource share is made of a host resource group made up of dedicated hosts in the provider network. The share provides access to the host resource group of a primary account with one or more other principals—e.g., user accounts, an organization, or a group of users. The other principals can launch compute instances into the host resource group, which may cause the automatic provisioning of new hosts into the host resource group, of the primary account, as needed.

Abstract: Techniques for automated defragmentation of licensed resources in a provider network are described. A defragmentation service in a provider network can detect the existence of intra-host fragmentation and/or inter-host fragmentation among host computing devices belonging to a host resource group of hosts dedicated to particular users. The defragmentation service can cause instances to be migrated to new locations within the host resource group to reduce or eliminate the fragmentation, allowing for hosts to beneficially be removed from the host resource group. The defragmentation service may determine which instances to be moved based on considerations involving software licenses associated with the host computing devices.

Abstract: A pair of records is tokenized to form a normalized representation of an entity represented by each record. The tokens are correlated to a machine learning system by determining whether a learned resolution already exists for the two entities. If not, the normalized records are compared to generate a comparison measure to determine whether the records match. The normalized records can also be used to perform a web search and web search results can be normalized and used as additional records for matching. When a match is found, the records are updated to indicate that they match, and the match is provided to the machine learning system to update the learned resolutions.

Abstract: A pair of records is tokenized to form a normalized representation of an entity represented by each record. The tokens are correlated to a machine learning system by determining whether a learned resolution already exists for the two entities. If not, the normalized records are compared to generate a comparison measure to determine whether the records match. The normalized records can also be used to perform a web search and web search results can be normalized and used as additional records for matching. When a match is found, the records are updated to indicate that they match, and the match is provided to the machine learning system to update the learned resolutions.

Abstract: Techniques are described for improving the boot performance of an operating system (OS) used to launch a virtual machine. In embodiments, a request is received that identifies an OS image and that includes information indicative of when a boot-up process of the OS is complete. A boot-up process of the OS is then performed until complete, as indicated by the information, which includes loading a portion of the OS image from a virtual hard drive. During performance of the process, data is obtained that identifies logical units in the virtual hard drive that are accessed to obtain the portion of the OS image. A copy of the virtual hard drive that include the OS image and the data is then stored so that it can be used to facilitate launching a virtual machine through selective pre-fetching of only the identified logical units from the copy of the virtual hard drive.

Abstract: Explicit and implicit feedback information, that is indicative of an issue in a deployed computing system, is collected. Information identifying attempted resolutions for the issue is collected as well. A knowledge base is generated that identifies issues and successful resolutions for those issues. During runtime, issues are detected, either explicitly or implicitly, and the knowledge base is accessed to determine whether a resolution has already been identified. If so, it can be proactively provided to the computing system to address the issue.

Abstract: Aspects extend to methods, systems, and computer program products for clustering streamed or batch data. Aspects of the invention include dynamic clustering and labeling of streamed data and/or batch data, including failures and error logs (user, platform, etc.), latency logs, warning logs, information logs, Virtual Machine (VM) creation data logs, template logs, etc., for use in analysis (e.g., error log analysis). A clustering system can learn from previously identified patterns and use that information to group newer information dynamically as it gets generated. The clustering system can leverage streamed data and/or batch data domain knowledge for preprocessing. In one aspect, a clustering system uses a similarity measure. Based on (e.g., users' configuration of) a similarity threshold, the cluster system (e.g., automatically) assigns/clusters streamed data and/or batch data into groups.

Abstract: Explicit and implicit feedback information, that is indicative of an issue in a deployed computing system, is collected. Information identifying attempted resolutions for the issue is collected as well. A knowledge base is generated that identifies issues and successful resolutions for those issues. During runtime, issues are detected, either explicitly or implicitly, and the knowledge base is accessed to determine whether a resolution has already been identified. If so, it can be proactively provided to the computing system to address the issue.

Abstract: Techniques are described for improving the boot performance of an operating system (OS) used to launch a virtual machine. In embodiments, a request is received that identifies an OS image and that includes information indicative of when a boot-up process of the OS is complete. A boot-up process of the OS is then performed until complete, as indicated by the information, which includes loading a portion of the OS image from a virtual hard drive. During performance of the process, data is obtained that identifies logical units in the virtual hard drive that are accessed to obtain the portion of the OS image. A copy of the virtual hard drive that include the OS image and the data is then stored so that it can be used to facilitate launching a virtual machine through selective pre-fetching of only the identified logical units from the copy of the virtual hard drive.

Abstract: Explicit and implicit feedback information, that is indicative of an issue in a deployed computing system, is collected. Information identifying attempted resolutions for the issue is collected as well. A knowledge base is generated that identifies issues and successful resolutions for those issues. During runtime, issues are detected, either explicitly or implicitly, and the knowledge base is accessed to determine whether a resolution has already been identified. If so, it can be proactively provided to the computing system to address the issue.

Abstract: Techniques are described for improving the boot performance of an operating system (OS) used to launch a virtual machine. In embodiments, a request is received that identifies an OS image and that includes information indicative of when a boot-up process of the OS is complete. A boot-up process of the OS is then performed until complete, as indicated by the information, which includes loading a portion of the OS image from a virtual hard drive. During performance of the process, data is obtained that identifies logical units in the virtual hard drive that are accessed to obtain the portion of the OS image. A copy of the virtual hard drive that include the OS image and the data is then stored so that it can be used to facilitate launching a virtual machine through selective pre-fetching of only the identified logical units from the copy of the virtual hard drive.

Abstract: Aspects extend to methods, systems, and computer program products for clustering streamed or batch data. Aspects of the invention include dynamic clustering and labeling of streamed data and/or batch data, including failures and error logs (user, platform, etc.), latency logs, warning logs, information logs, Virtual Machine (VM) creation data logs, template logs, etc., for use in analysis (e.g., error log analysis). A clustering system can learn from previously identified patterns and use that information to group newer information dynamically as it gets generated. The clustering system can leverage streamed data and/or batch data domain knowledge for preprocessing. In one aspect, a clustering system uses a similarity measure. Based on (e.g., users' configuration of) a similarity threshold, the cluster system (e.g., automatically) assigns/clusters streamed data and/or batch data into groups.

Abstract: Techniques are described for improving the boot performance of an operating system (OS) used to launch a virtual machine. In embodiments, a request is received that identifies an OS image and that includes information indicative of when a boot-up process of the OS is complete. A boot-up process of the OS is then performed until complete, as indicated by the information, which includes loading a portion of the OS image from a virtual hard drive. During performance of the process, data is obtained that identifies logical units in the virtual hard drive that are accessed to obtain the portion of the OS image. A copy of the virtual hard drive that include the OS image and the data is then stored so that it can be used to facilitate launching a virtual machine through selective pre-fetching of only the identified logical units from the copy of the virtual hard drive.

Abstract: A pair of records is tokenized to form a normalized representation of an entity represented by each record. The tokens are correlated to a machine learning system by determining whether a learned resolution already exists for the two entities. If not, the normalized records are compared to generate a comparison measure to determine whether the records match. The normalized records can also be used to perform a web search and web search results can be normalized and used as additional records for matching. When a match is found, the records are updated to indicate that they match, and the match is provided to the machine learning system to update the learned resolutions.

Abstract: A system is configured to boot an image of an operating system for creating a virtual machine to run the operating system, and to eliminate a reboot from occurring, before the image completes booting and before the created virtual machine begins running the operating system, by provisioning the image with configuration settings associated with the virtual machine during the booting of the image.

Abstract: A pair of records is tokenized to form a normalized representation of an entity represented by each record. The tokens are correlated to a machine learning system by determining whether a learned resolution already exists for the two entities. If not, the normalized records are compared to generate a comparison measure to determine whether the records match. The normalized records can also be used to perform a web search and web search results can be normalized and used as additional records for matching. When a match is found, the records are updated to indicate that they match, and the match is provided to the machine learning system to update the learned resolutions.

Abstract: Explicit and implicit feedback information, that is indicative of an issue in a deployed computing system, is collected. Information identifying attempted resolutions for the issue is collected as well. A knowledge base is generated that identifies issues and successful resolutions for those issues. During runtime, issues are detected, either explicitly or implicitly, and the knowledge base is accessed to determine whether a resolution has already been identified. If so, it can be proactively provided to the computing system to address the issue.

Abstract: Crawlers crawl disparate sources of information and build a graph that identifies relationships between entities. The graph can be manually updated by users. Where two or more users attempt to make competing updates to the same information in the graph, a prevailing update is identified based upon the user's proximity to the entity being changed.

Abstract: A pair of records is tokenized to form a normalized representation of an entity represented by each record. The tokens are correlated to a machine learning system by determining whether a learned resolution already exists for the two entities. If not, the normalized records are compared to generate a comparison measure to determine whether the records match. The normalized records can also be used to perform a web search and web search results can be normalized and used as additional records for matching. When a match is found, the records are updated to indicate that they match, and the match is provided to the machine learning system to update the learned resolutions.