Abstract: The invention discloses an interface system for an interconnected die and an MPU and a communication method thereof. The system comprises a data interface, an interrupt interface, and a debugging interface; the data interface comprises an SPI interface, a DDR data interface, and a DMA control interface; the interrupt interface is used for receiving an interrupt data packet from the network and parsing the interrupt data packet to obtain a pulse interrupt input required by the MPU; the debugging interface comprises a JTAG-Core debugging interface, which is used for receiving a debugging data packet from the network and translating the debugging data packet into a JTAG protocol for MPU debugging. The invention realizes the expansion of the master device MPU in the high-performance information processing microsystem and the high-speed communication between the master device and the interconnected dies.

Abstract: A method and an apparatus for searching for a point of information. A client performs, based on a first search text entered by a user and first user data stored in the client, address extension on the first search text, to obtain a second search text that includes the first search text and includes first address information obtained based on the first user data. Because the first address information in the second search text is closely related to personal information of the user, it is highly probable that an address indicated by the first address information represents an address of a POI that the user expects to find. Therefore, it is highly probable that POI search results fed back by a server based on the second search text include a search result of the POI that the user is interested in or expects.

Abstract: Features are disclosed for correlating a workload type with particular volume characteristics for a block storage volume. The volume characteristics may include a durability or a performance consistency for a particular volume. A computing device can obtain a set of workload parameters indicating a workload associated with a particular block storage volume. Based on the set of workload parameters, the computing device can determine a workload classification that links the set of workload parameters to a set of volume characteristics. The computing device can further compare the set of volume characteristics with the current set of volume characteristics for the block storage volume. Based on comparing the sets of volume characteristics, the computing device may determine a recommendation for a user. The computing device can dynamically modify the block storage volume based on the recommendation.

Abstract: A word completion method and apparatus are described. The method is applied to a search scenario and is used to complete an incomplete word entered by a user. The method is based on an improved trie. Hot words are stored in some nodes of the trie. In the word completion method, a target node that matches a character string is searched in the trie, and at least one completed word is output to a user based on a hot word stored in the target node. Word completion efficiency is improved, and a case in which a word is recommended to a user when the user enters an excessively short character string is avoided.

Abstract: Features are disclosed for forecasting a usage of a block storage volume with a first configuration by a user. A computing device can forecast the usage of the block storage volume based on the historical usage of the block storage volume by the user. The computing device can determine additional potential configurations of the block storage volume. The computing device can further simulate the additional potential configurations of the block storage volume based on the forecasted usage of the block storage volume. The additional potential configurations may include a volume type, a volume size, or other volume characteristics. Based on the simulations of the additional potential configurations, the computing device may determine a recommended configuration. The computing device can dynamically modify the block storage volume based on the recommended configuration of the block storage volume.

Abstract: Techniques for an optimization service of a service provider network to help optimize the selection, configuration, and utilization, of virtual machine (VM) instance types to support workloads on behalf of users. The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources. For example, the optimization service may perform techniques to help new users select an optimized VM instance type on which to initially launch their workload. Further, the optimization service may monitor a workload for the life of the workload, and determine new VM instance types, and/or configuration modifications, that optimize the performance of the workload. The optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network.

Abstract: Techniques for an optimization service of a service provider network to help optimize the selection, configuration, and utilization, of virtual machine (VM) instance types to support workloads on behalf of users. The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources. For example, the optimization service may perform techniques to help new users select an optimized VM instance type on which to initially launch their workload. Further, the optimization service may monitor a workload for the life of the workload, and determine new VM instance types, and/or configuration modifications, that optimize the performance of the workload. The optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network.

Abstract: Techniques for a service provider network to allow users to quickly and easily establish a testing environment to test various virtual machine (VM) instance types for hosting their workloads. Rather than identifying and recommending optimized VM instance types for hosting workloads of users, the techniques allow for users to initially test the VM instance types and determine how well their workloads perform on the VM instance types. Users can quickly and easily (e.g., “one-click” input) request that a testing environment be established. The optimization service can then test one or more recommended VM instance types for the users' workloads in the testing environment. The optimization service can monitor the performance of the VM instance types while they host the “test workloads,” and provide the users with performance metrics to help them decide if they would like to migrate their workloads to the recommended VM instance types.

Abstract: Techniques for an optimization service of a service provider network to help optimize the selection, configuration, and utilization, of virtual machine (VM) instance types to support workloads on behalf of users. The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources. For example, the optimization service may perform techniques to help new users select an optimized VM instance type on which to initially launch their workload. Further, the optimization service may monitor a workload for the life of the workload, and determine new VM instance types, and/or configuration modifications, that optimize the performance of the workload. The optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network.

Abstract: Techniques for an optimization service of a service provider network to help optimize the selection, configuration, and utilization, of virtual machine (VM) instance types to support workloads on behalf of users. The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources. For example, the optimization service may perform techniques to help new users select an optimized VM instance type on which to initially launch their workload. Further, the optimization service may monitor a workload for the life of the workload, and determine new VM instance types, and/or configuration modifications, that optimize the performance of the workload. The optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network.

Abstract: Techniques for an optimization service of a service provider network to help optimize the selection, configuration, and utilization, of virtual machine (VM) instance types to support workloads on behalf of users. The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources. For example, the optimization service may perform techniques to help new users select an optimized VM instance type on which to initially launch their workload. Further, the optimization service may monitor a workload for the life of the workload, and determine new VM instance types, and/or configuration modifications, that optimize the performance of the workload. The optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network.

Abstract: Techniques for an optimization service of a service provider network to generate an architecture diagram that represents an architecture of a web-based application. The optimization service may use the architecture diagram to determine modifications or changes to make to the application. For example, the optimization service may compare the architecture diagram with optimized architecture diagrams that represent application best practices, and determine the modifications or change to make to the application to optimize the application and bring the application in-line with best practices. Further, the optimization service may use the architecture diagram to generate a visualization, and provide the user account with the visualization of the architecture diagram to show users their application architecture.

Abstract: Techniques for an optimization service of a service provider network to provide users with machine-generated narratives that include human-intelligible, credible, and transparent recommendations and rationales for recommended VM instance types. The optimization service may gather various information or data about the workload, such as utilization characteristics of the underlying computing resources, and decompose the workloads through a number of dimensions that can be used to describe the workload. Further, the optimization service may analyze the utilization characteristics and/or other data to determine more optimized VM instance types for the workloads that are to be recommended to the users, and also rationales that describes why each recommendation is an appropriate fit for the workload being assessed.

Abstract: Techniques for an optimization service of a service provider network to help optimize the selection, configuration, and utilization, of virtual machine (VM) instance types to support workloads on behalf of users. The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources. For example, the optimization service may perform techniques to help new users select an optimized VM instance type on which to initially launch their workload. Further, the optimization service may monitor a workload for the life of the workload, and determine new VM instance types, and/or configuration modifications, that optimize the performance of the workload. The optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network.

Abstract: Techniques for an optimization service of a service provider network to help optimize the selection, configuration, and utilization, of virtual machine (VM) instance types to support workloads on behalf of users. The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources. For example, the optimization service may perform techniques to help new users select an optimized VM instance type on which to initially launch their workload. Further, the optimization service may monitor a workload for the life of the workload, and determine new VM instance types, and/or configuration modifications, that optimize the performance of the workload. The optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network.

Abstract: Techniques for an optimization service of a service provider network to help optimize the selection, configuration, and utilization, of virtual machine (VM) instance types to support workloads on behalf of users. The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources. For example, the optimization service may perform techniques to help new users select an optimized VM instance type on which to initially launch their workload. Further, the optimization service may monitor a workload for the life of the workload, and determine new VM instance types, and/or configuration modifications, that optimize the performance of the workload. The optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network.

Abstract: Techniques for an optimization service of a service provider network to help optimize the selection, configuration, and utilization, of virtual machine (VM) instance types to support workloads on behalf of users. The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources. For example, the optimization service may perform techniques to help new users select an optimized VM instance type on which to initially launch their workload. Further, the optimization service may monitor a workload for the life of the workload, and determine new VM instance types, and/or configuration modifications, that optimize the performance of the workload. The optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network.

Abstract: Techniques for an optimization service of a service provider network to help optimize the selection, configuration, and utilization, of virtual machine (VM) instance types to support workloads on behalf of users. The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources. For example, the optimization service may perform techniques to help new users select an optimized VM instance type on which to initially launch their workload. Further, the optimization service may monitor a workload for the life of the workload, and determine new VM instance types, and/or configuration modifications, that optimize the performance of the workload. The optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network.

Abstract: Techniques for an optimization service of a service provider network to help optimize the selection, configuration, and utilization, of virtual machine (VM) instance types to support workloads on behalf of users. The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources. For example, the optimization service may perform techniques to help new users select an optimized VM instance type on which to initially launch their workload. Further, the optimization service may monitor a workload for the life of the workload, and determine new VM instance types, and/or configuration modifications, that optimize the performance of the workload. The optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network.

Abstract: Techniques for an optimization service of a service provider network to help optimize the selection, configuration, and utilization, of virtual machine (VM) instance types to support workloads on behalf of users. The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources. For example, the optimization service may perform techniques to help new users select an optimized VM instance type on which to initially launch their workload. Further, the optimization service may monitor a workload for the life of the workload, and determine new VM instance types, and/or configuration modifications, that optimize the performance of the workload. The optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network.