Abstract: A scalable Internet of Things (IoT) system may include multiple instances of an IoT manager, each instance respectively configured to connect to a respective edge system of multiple edge systems. The IoT system may further include a containerized system configured to allow any instance of the IoT manager to deploy data pipelines to any edge system of the multiple edge systems in delta communications. Any instance of the IoT manager may send a change message to any edge system via a publish/subscribe notification method. In some examples, a centralized IoT manager may form a secure communication with an edge system, synchronize an object model with an edge object model for the edge system, and maintain the edge system using delta change communications. The IoT system may facilitate any instance of the IoT manager to subscribe a communication channel with an associated edge system for receiving update notification.

Abstract: A scalable Internet of Things (IoT) system may include multiple instances of an IoT manager, each instance respectively configured to connect to a respective edge system of multiple edge systems. The IoT system may further include a containerized system configured to allow any instance of the IoT manager to deploy data pipelines to any edge system of the multiple edge systems in delta communications. Any instance of the IoT manager may send a change message to any edge system via a publish/subscribe notification method. In some examples, a centralized IoT manager may form a secure communication with an edge system, synchronize an object model with an edge object model for the edge system, and maintain the edge system using delta change communications. The IoT system may facilitate any instance of the IoT manager to subscribe a communication channel with an associated edge system for receiving update notification.

Abstract: Systems and methods described herein generally relate to compute node resource scheduling. AI inference services described herein may receive a request to execute a machine learning model in a clustered edge system. To determine which hardware resource comprising computing nodes of the clustered edge system on which to execute the machine learning model, AI inference services may compare the computational workload of the machine learning model, with the computational abilities and functions of the hardware resources. In examples, the comparison is based on a scheduling algorithm, including an identification stage to identify candidate hardware resources capable of executing the machine learning model, and a scoring stage to select the best candidate hardware resource for executing the machine learning model. A scheduler may assign the machine learning model to the selected hardware resource for execution by the AI inference services.

Abstract: A centralized Internet of Things (IoT) manager of an IoT system is configured generate and deploy a data pipeline application to an edge system. The centralized IoT manager is configured to receive a request for a data category and identification of a data transformation function, identify a data source of the IoT system belonging to the data category, and identify an edge system of the IoT system associated with the data source. The centralized IoT manager is further configured to generate a containerized data pipeline application based on a configuration of the edge system that is configured apply the data transformation function to input data to provide transformed data, and provide the containerized data pipeline application to the edge system.

Abstract: A centralized Internet of Things (IoT) manager of an IoT system is configured generate and deploy a data pipeline application to an edge system. The centralized IoT manager is configured to receive a request for a data category and identification of a data transformation function, identify a data source of the IoT system belonging to the data category, and identify an edge system of the IoT system associated with the data source. The centralized IoT manger is further configured to generate a containerized data pipeline application based on a configuration of the edge system that is configured apply the data transformation function to input data to provide transformed data, and provide the containerized data pipeline application to the edge system.

Abstract: A centralized Internet of Things (IoT) manager of an IoT system is configured generate and deploy a data pipeline application to an edge system. The centralized IoT manager is configured to receive a request for a data category and identification of a data transformation function, identify a data source of the IoT system belonging to the data category, and identify an edge system of the IoT system associated with the data source. The centralized IoT manger is further configured to generate a containerized data pipeline application based on a configuration of the edge system that is configured apply the data transformation function to input data to provide transformed data, and provide the containerized data pipeline application to the edge system.

Abstract: A scalable Internet of Things (IoT) system may include multiple instances of an IoT manager, each instance respectively configured to connect to a respective edge system of multiple edge systems, The IoT system may further include a containerized system configured to allow any instance of the IoT manager to deploy data pipelines to any edge system of the multiple edge systems in delta communications. Any instance of the IoT manager may send a change message to any edge system via a publish/subscribe notification method. In some examples, a centralized IoT manager may form a secure communication with an edge system, synchronize an object model with an edge object model for the edge system, and maintain the edge system using delta change communications. The IoT system may facilitate any instance of the IoT manager to subscribe a communication channel with an associated edge system for receiving update notification.

Abstract: An example edge system of an Internet of Things system may include a memory configured to store a machine learning (ML) model application having a ML model a machine, and a processor configured to cause a ML inference service to receive a request for an inference from a ML model application having a ML model, and load the ML model application from the memory into an inference engine in response to the request. The processor is further configured to cause the MT inference service to select a runtime environment from the ML model application to execute the ML model based on a hardware configuration of the edge system, and execute the ML model using the selected to provide inference results. The inference results are provided at an output, such as to a data plane or to be stored in the memory.

Abstract: Performance of a virtual machine system is improved by avoiding and/or eliminating bottlenecks in read and write operations. The system analyzes current virtualized workloads and provides working set estimates for individual VMs, hosts, and clusters. The working set estimate data is then utilized to make specific recommendations for different types of backend storage technologies. After procuring a storage device, the system provides a variety of information to aid in the operation of the system. From this information, the system can detect various scenarios and proactively make recommendations to the user about ways in which to improve storage performance at a host level and at a per-VM level. In some embodiments, these recommendations may be implemented automatically without user involvement.

Abstract: The present invention provides an apparatus and process to share digital images and videos of a user wearing virtual apparel. The invention comprises of a camera 201 for capturing images and videos; a central processing unit (CPU) that obtains camera media feed and processes it to augment digital imagery of apparel; the CPU being configured to track a user in the media feed; a display screen 203 that displays the processed media feed; an interne adapter 204 capable of connecting to the interne; the CPU configured to upload the processed media feed online to a server 205 and further send the web location of the uploaded image or video, preferably in a text message using a cellular network 206 to the user's mobile phone 207. The user 251 can share the text message with others enabling them to view the uploaded content on an interne enabled device through a private link or through a social networking platform.