32 results about "Dependence network" patented technology

The invention discloses a mobile edge computing unloading method based on multi-agent reinforcement learning, belongs to the field of edge computing and wireless networks, and provides an intelligenttask unloading method for a multi-user multi-edge node complex scene. According to the method, a multi-agent reinforcement learning algorithm is adopted, each piece of user equipment establishes an Actor and Critic deep learning network locally, action selection and action scoring are carried out according to states and actions of the user equipment and other equipment, spectrum resources, computing resources and energy resources are comprehensively considered, and an unloading and resource allocation strategy is formulated by taking task delay optimization as a target. The method does not depend on a specific network model, each equipment can autonomously and intelligently make an optimal strategy through a learning process of exploring feedback, and the method can adapt to dynamic changeof a network environment.

The invention discloses an intrusion process layering online risk assessment system and a method, which are used for assessing the risk condition produced on three layers of the service, the host and the network of an occurrent intrusion process in real time. In the invention, on the layer of the service, an evidence theory is used for fusing multi vectors in an alarm thread to compute a risk index, wherein the vectors can response risk change conditions, the objective condition of intrusion risks is reflected by the risk index, and simultaneously, with target risk distribution reflected by subjective safety awareness, the risk condition of a target is comprehensively assessed; on the layer of the host, a risk assessment method based on a cask principle is provided; and on the layer of the network, a safety dependence network concept is provided, and an improved risk spreading algorithm is utilized so as to complete the risk assessment of the layer of the network. According to the invention, the alarm processes of alarm verification, aggregation and correlation as well as alarm confidence learning are closely combined with the risk assessment, so that the subjectivity, the fuzziness, the uncertainty and other problems in the risk assessment are better processed.

According to an embodiment of the invention, a method is provided for allocating load to processing nodes in a processing cluster arranged as an object dependency network. The method includes establishing at a load allocation function a plurality of load allocation function attributes whose values are dependant on the values of attributes of the processing nodes, and receiving requests for processing. The method further includes determining to which of the processing nodes to allocate the requests for processing based on the load allocation function attributes, and allocating requests for processing to the processing nodes in accordance with that determination.

The invention relates to a self-learning user habit control system and method for an intelligent home. The system comprises a data receiving module, a data storage module, an algorithm module, an execution triggering module and a logic judging module. The control system is arranged in the home, user data is stored in a local system, and the system can autonomously work and does not depend on network transmission; the system is internally installed with an algorithm system, and the algorithm system can work at regular intervals according to the behavior data of a user so as to learn the habitsof the user and update a logic judging control system of the system, so that a large amount of manual configuration operations of the user are avoided, and the problem that the convenience of an intelligent management and control system cannot be enjoyed because aged people and children may not able to use a manual configuration system or crowds who are not able to operate cannot use the manual configuration system is solved.

The invention provides a failure prediction method for software development. The failure prediction method comprises the steps that a source code dependence network and a developer social network are set up, and the dependence relationship between source code files and the communication and cooperation relationship between developers are recorded respectively; the source code dependence network and the developer social network are compared, and the difference between the source code dependence network and the developer social network is used as an indication of lack of communication. By the adoption of the failure prediction method for software development, the number of lacked developer communication links associated with each source code file can be detected and used as the indication of inconsistency of file networks, and the developer communication links lacked by the files can be provided; meanwhile, the reason of each lacked link can also be provided; namely, two corresponding source files depend on each other, however the developers of the two source files do not communicate and cooperate with each other. The developers can organize corresponding communication for making up according to the guide of the method, and failures are found and repaired in a targeted mode.

The invention discloses a container network flow limiting method and system for a cloud edge collaborative virtualization scene. The method comprises the following steps: sending flow limiting information to an edge platform through a cloud end; according to the flow limiting information and a network card address of the edge platform, establishing a label for a network packet, wherein the network card comprises a virtual interface of a container; and carrying out network flow limiting on the network packet through a flow controller based on the label and the flow limiting information. In the cloud edge collaborative virtualization scene, the network packets are classified according to the virtual interface addresses of the containers, the virtual interface addresses point to the containers, and the classified network packets are subjected to flow limiting, so that a native container network is subjected to flow limiting, a network plug-in does not need to be relied on, and the network bandwidth limit operation for the container granularity in the container arrangement framework is realized.

The invention relates to the technical field of monitoring of a cloud platform, and particularly relates to a method for checking connection states of virtual machines on the cloud platform. The method disclosed by the invention comprises the steps of: firstly, acquiring a list of virtual machines which run currently; then acquiring network information of each virtual machine; then checking a virtual machine operation system type; acquiring the operation system type; checking an external network connection state of each virtual machine; acquiring an external network connecting result; checking a virtual gateway connection state of each virtual machine; and finally, acquiring a virtual gateway connecting result. The method disclosed by the invention solves the problems that checking on the connection states of the virtual machines is not in real time and depends a network, network types of the virtual machines need to be distinguished, a checking method is not universal and the like; and the method can be applied to real-time monitoring of the cloud platform.

The invention discloses an invading infection region positioning method based on a computer time sequence dependence network. The method includes the steps of constructing the complete object time sequence dependence network according to an access record, collected in a computer system, of all processes to computer objects; pruning the complete time sequence dependence network, and extracting an object time sequence dependence sub-network related to invading; constructing a Bayesian network according to the object time sequence dependence sub-network; carrying out probabilistic reasoning through the Bayesian network, calculating and quantizing the possibility that other unknown objects in the system suffer from invading infection, and obtaining an invading infection region of the computer system. The method has the advantages that the time-information-combined object dependence network is constructed, and the time sequence dependence relation among the objects and the characteristic that the object infection state is changed along with the time change can be more accurately described; a probabilistic reasoning method is provided for positioning the invading infection region.

The embodiment of the invention provides a trusted execution environment activation method and device, the trusted execution environment is configured in equipment, and the equipment is further configured with a trusted application program running in the trusted execution environment, a rich execution environment and a client program running in the rich execution environment; the method comprisesthe following steps of enabling the trusted execution environment to acquire an activation file which is sent by the client program and is preset in the equipment; enabling the trusted execution environment to verify whether the activation file is legal or not; if the activation file is legal, activating the trusted execution environment and allowing the trusted application program to run normally; and after the trusted execution environment is activated, storing the activation file into a trusted storage space of the equipment. In the embodiment of the invention, the equipment can be activated without depending on a network.

The invention discloses an application service monitoring method and device, electronic equipment and a readable storage medium. The method comprises the steps of, installing a communication device which interacts with a host machine and does not depend on a network in a cloud host of a cloud platform in advance, deploying a data collection agent in the host machine, and meanwhile integrating a data collection program matched with the data collection agent and data collection templates of various application services in an application service installation package; when it is detected that the target application service is generated, generating a data acquisition configuration file according to the attribute information of the target application service and a target data acquisition template matched with the type of the target application service; and when the target application service is successfully deployed in the cloud host, acquiring the monitoring performance data acquired by the cloud host based on the data acquisition configuration file, thereby safely and efficiently monitoring various types of application services of the cloud platform.

The invention discloses a multi-path routing method for a high-speed interconnected dragonfly + network. Implementation steps are that based on dragonfly and various information in a network, enablinga switch node to determine whether the data uses the intermediate group or not through the shortest path weight; selecting an intermediate group; and finally, selecting an output port on the basis, thereby achieving multi-path routing of QP granularity data in a high-speed interconnected dragonfly + network. According to the invention, the method has the advantages that real-time monitoring is carried out without depending on network flow, and the disorder of a receiving end is avoided; the deadlock is avoided through distribution of virtual channels, the relieving of link congestion among network groups serves as an optimization target of a multi-path routing model, path resources in the dragonfly + network are fully utilized, and the method can be used for deploying an actual large high-speed internet.

The embodiment of the invention discloses a multi-machine interaction method, device and system, a storage medium and a program product, and belongs to the technical field of interaction. In the embodiment of the invention, the multi-machine interaction system comprises local storage nodes of a plurality of terminals, and a shared area in the storage nodes is used for realizing local interaction of the plurality of terminals. In this way, interaction among the multiple terminals does not need to depend on a server, and the interaction efficiency can be guaranteed. In addition, if a plurality of terminals realize interaction locally, the terminals can also perform interaction without depending on the network condition and without the network condition.