Abstract: A PaaS cloud server receives a machine learning application resource request and determines an available logic circuit object accordingly. The PaaS cloud server creates a data compression and storing service object and pairs the data compression and storing service object and the logic circuit object. The PaaS cloud server binds the data compression and storing service object with a machine learning application, and receives a machine learning data from the machine learning application. The PaaS cloud server inputs the machine learning data into a part logic circuit of an FGPA circuit corresponding to the logic circuit object according to the pairing of the data compression and storing service object and the logic circuit object so that the part logic circuit compresses and stores the machine learning data. The PaaS cloud server returns information which relates to the compressed and stored data to the machine learning application.

Abstract: A device and method for failover are disclosed. The device receives a network topology data and a plurality of device metadata of an IoT system and converts them to a management topology data and a plurality of management metadata. After receiving a failure message related to a first apparatus of the IoT system, the device simulates a plurality of device connection relationships between the devices of the IoT system, calculates a plurality of failover costs according to the device connection relationships, chooses a second apparatus to be a failover target according to the failover costs, finds out at least one correctly verified management metadata according to a plurality of hash data and a hash function, converts the at least one verified management metadata into a format complying to the device metadata of the IoT system, delivers the converted management metadata to the second apparatus, and updates the management topology data.

Abstract: A PaaS cloud server and a multi-tenant operating method thereof are provided. The PaaS cloud server receives an application resource request and determines an available logic circuit object according to the application resource request. The PaaS cloud server creates an FPGA service object and pairs the FPGA service object and the logic circuit object. The PaaS cloud server binds the FPGA service object with an application of the tenant and receives an application data from the application. The PaaS cloud server inputs the application data into a part logic circuit of an FGPA circuit corresponding to the logic circuit object according to the pairing of the FPGA service object and the logic circuit object so that the part logic circuit computes a result data of the application data. The PaaS cloud server returns the result data to the application.

Abstract: A PaaS cloud server receives a machine learning application resource request and determines an available logic circuit object accordingly. The PaaS cloud server creates a data compression and storing service object and pairs the data compression and storing service object and the logic circuit object. The PaaS cloud server binds the data compression and storing service object with a machine learning application, and receives a machine learning data from the machine learning application. The PaaS cloud server inputs the machine learning data into a part logic circuit of an FGPA circuit corresponding to the logic circuit object according to the pairing of the data compression and storing service object and the logic circuit object so that the part logic circuit compresses and stores the machine learning data. The PaaS cloud server returns information which relates to the compressed and stored data to the machine learning application.

Abstract: The disclosure is related to a computer device and a method for facilitating a user to manage containers. The method includes: transforming container data including identification data and dependency data into graph data by a computer device; storing the graph data into a graph database by the computer device; and enquiring the graph data in the graph database by the computer device in response to a user input which is based on a graph manipulation language corresponding to the graph database so that the user manages the containers.

Abstract: A software test apparatus and a software test method and a computer readable medium thereof are provided. The software test apparatus stores a software testing program, an under-tested code, a plurality of basic test benches and a plurality of candidate test benches. The under-tested code includes a hard-to-detect code and the hard-to-detect code has at least one hard-to-detect section. The software test apparatus runs the software testing program to execute the following operations: parsing the hard-to-detect code to generate a condition-statement tree; based on the basic test benches and the condition-statement tree, using a support vector machine (SVM) to establish a support vector regression (SVR) predictor; and applying the SVR predictor to choose a best candidate test bench from the candidate test benches.

Abstract: A graphic processing unit (GPU) virtual apparatus, a GPU host apparatus and GPU program processing methods thereof are provided. The GPU virtual apparatus determines a priority of a GPU program, determines a processing order of the GPU program according to the priority, processes the GPU program according to the processing order, and transmits the processed GPU program to the GPU host apparatus. The GPU host apparatus receives the processed GPU program from the GPU virtual apparatus, determines a priority of the processed GPU program, determines a processing order of the processed GPU program according to the priority, further processes the processed GPU program according to the processing order, and transmits an operation result of the processed GPU program to the GPU virtual apparatus.