A device for testing a blockchain network is provided. The device includes a processor. The processor is configured to calculate a matching degree between a configuration of the blockchain network and a template configuration corresponding to application scenario of the blockchain network; calculate performance of the blockchain network; calculate a health degree for the blockchain network; and determine whether the blockchain network is in a stable state based on the matching degree, the performance and the health degree.

Embodiments include methods for managing the processing of data and performing computing tasks of a wireless device using computing resources of a remote network computing device. A wireless device and a network computing device may dynamically track factors affecting a round-trip latency of a computing task. The wireless device and the network computing device may generate and send metadata including the factors and a latency budget for the computing task. The wireless device and the network computing device may adjust a processing time for processing data related to the computing task based the received metadata and the latency budget.

Systems and methods for decoupled searching and optimization for one or more data centers, including determining a network topology for one or more networks of interconnected computer systems embedded in the one or more data center, searching for routing candidates based on a network topology determined, and updating and applying one or more objective functions to the routing candidates to determine an optimal routing candidate to satisfy embedding goals based on tenant requests, and to embed the optimal routing candidate in the one or more data centers.

A system receives, from one or more subsystems, one or more predicted outcomes associated with a device. The system provides provide at least a subset of the predicted outcomes as input to a machine learning model trained to identify a set of resolution actions. The system receives, from the machine learning model, the set of resolution actions for the subset of the predicted outcomes, wherein each resolution action in the set of resolution actions is associated with a probability of resolving at least one of the predicted outcomes in the subset of predicted outcomes. The system identifies a first resolution action from the set of resolution actions, wherein the first resolution action has a highest probability of resolving the at least one of the predicted outcomes in the subset of predicted outcomes. The system provides a first instruction to execute the first resolution action.

A system receives, from one or more subsystems, one or more predicted outcomes associated with a device. The system provides provide at least a subset of the predicted outcomes as input to a machine learning model trained to identify a set of resolution actions. The system receives, from the machine learning model, the set of resolution actions for the subset of the predicted outcomes, wherein each resolution action in the set of resolution actions is associated with a probability of resolving at least one of the predicted outcomes in the subset of predicted outcomes. The system identifies a first resolution action from the set of resolution actions, wherein the first resolution action has a highest probability of resolving the at least one of the predicted outcomes in the subset of predicted outcomes. The system provides a first instruction to execute the first resolution action.

A wireless communication apparatus includes, a transceiver module performs data transmission; and a processing module selects a channel whose bandwidth is a to-be-detected bandwidth from a WLAN system. If there is no idle channel, detection is performed again after the to-be-detected bandwidth is reduced.

Some embodiments provide systems and methods for performing localized and real-time server-side network performance monitoring. These systems and methods leverage the distributed architecture of a content delivery network (CDN) so as to perform distributed monitoring with each Point-of-Presence of the CDN responsible for monitoring performance to a localized set of end users. These systems and methods also leverage existing traffic flows from a server to a particular end user in order to perform real-time server-side network performance monitoring without the injection of specialized monitoring packets and without active involvement of the end user in deriving the performance measurements. The performance measurements are then used to optimize delivery of existing and future traffic flows to the end user.

A terminal device and a base station apparatus use a broad band to improve a throughput. Provided is a terminal device that communicates with a base station apparatus, including a reception unit that receives signals of a first frame structure and a second frame structure, and a subcarrier spacing in the second frame structure is m times a subcarrier spacing in the first frame structure (m is a natural number). Provided is a base station apparatus which communicates with a terminal device, including a transmission unit that transmits signals of a first frame structure and a second frame structure, and a subcarrier spacing in the second frame structure is m times a subcarrier spacing in the first frame structure (m is a natural number).

A method and system provide an information centric network with a software defined network based on an information centric networking protocol on top of a physical network based on the internet protocol. There are forwarding elements in the physical network and a controller in the software defined network for controlling the forwarding elements. A publicly routable network address per domain for outside data object requests of named data objects is announced via the information centric network, and upon a first packet of an object request being received by an ingress element of the information centric network, the first packet is forwarded to the controller. The controller determines an object source for the requested named data object, encodes a message id into a header of the packet and establishes a forwarding path to forward the packet and further packets to the determined object source.

A machine-learning optimization of a plurality of networks is provided. The machine-learning optimization includes interconnecting an online platform providing a machine learning module, a core network of computers deploying novel software, and a plurality of Internet network service providers. The platform collects, via the software, performance data of the Internet networks, which the machine learning module utilizes to enhance performance and reduce the latency therein networks by taking into account thousands of real-time and historic latency and bandwidth metrics. Thereby the software continually selects an optimal path through the plurality of Internet networks.