Abstract:
Methods, systems, and storage media are described for Internet of Things (IoT) hubs and IoT cradles in mesh networks and/or fog computing systems while providing interoperability among IoT devices provided by various manufacturers, vendors, and service providers. IoT devices may be connected or attached to IoT cradles, and the IoT cradles may communicate data among themselves over a cradle network. The IoT cradles may also communicate IoT data with the IoT hub over a hub network. The IoT hub may communicate the IoT data with clients and/or servers over a wide area network using wired or wireless communication protocols. Clients may access resources and/or services provided by the IoT devices by accessing the IoT hub via a dedicated application. Other embodiments may be described and/or claimed.
Abstract:
Methods, systems, and storage media are described for Internet of Things (IoT) hubs and IoT cradles in mesh networks and/or fog computing systems while providing interoperability among IoT devices provided by various manufacturers, vendors, and service providers. IoT devices may be connected or attached to IoT cradles, and the IoT cradles may communicate data among themselves over a cradle network. The IoT cradles may also communicate IoT data with the IoT hub over a hub network. The IoT hub may communicate the IoT data with clients and/or servers over a wide area network using wired or wireless communication protocols. Clients may access resources and/or services provided by the IoT devices by accessing the IoT hub via a dedicated application. Other embodiments may be described and/or claimed.
Abstract:
Technologies for generating an analytical model for a workload of a data center include an analytics server to receive raw data from components of a data center. The analytics server retrieves a workbook that includes analytical algorithms from a workbook marketplace server, and uses the analytical algorithms to analyze the raw data to generate the analytical model for the workload based on the raw data. The analytics server further generates an optimization trigger to be transmitted to a controller component of the data center that may be based on the analytical model and one or more previously generated analytical models. The workbook marketplace server may include a plurality of workbooks, each of which may include one or more analytical algorithms from which to generate a different analytical model for the workload of the data center.
Abstract:
Technologies for datacenter management include one or more computing racks each including a rack controller. The rack controller may receive system, performance, or health metrics for the components of the computing rack. The rack controller generates regression models to predict component lifespan and may predict logical machine lifespans based on the lifespan of the included hardware components. The rack controller may generate notifications or schedule maintenance sessions based on remaining component or logical machine lifespans. The rack controller may compose logical machines using components having similar remaining lifespans. In some embodiments the rack controller may validate a service level agreement prior to executing an application based on the probability of component failure. A management interface may generate an interactive visualization of the system state and optimize the datacenter schedule based on optimization rules derived from human input in response to the visualization. Other embodiments are described and claimed.