Abstract: A programmable network technology-based multi-homed network routing and forwarding method includes a data packet performing address-to-port matching a forwarding flow table address matching method; classifying addresses to matched ports corresponding to a destination address group; selecting a link information table and a port selection function, an appropriate port and a corresponding destination address group; cropping destination address groups of the remaining ports using an address filtering function and previous hop information carried in data to further crop the remaining destination address groups and addresses in the destination address group, a single destination address can be determined using a single address selection method, taking the single destination address using a universal routing and forwarding method to forward a packet to the destination address; otherwise, packaging the destination address group, and using a single address selection method to determine the single destination address h

Abstract: A big data processing method based on a deep learning model satisfying K-degree sparse constraints comprises: step 1), constructing a deep learning model satisfying K-degree sparse constraints using an un-marked training sample via a gradient pruning method, wherein the K-degree sparse constraints comprise a node K-degree sparse constraint and a level K-degree sparse constraint; step 2), inputting an updated training sample into the deep learning model satisfying the K-degree sparse constraints, and optimizing a weight parameter of each layer of the model, so as to obtain an optimized deep learning model satisfying the K-degree sparse constraint; and step 3), inputting big data to be processed into the optimized deep learning model satisfying the K-degree sparse constraints for processing, and finally outputting a processing result. The method in the present invention can reduce the difficulty of big data processing and increase the speed of big data processing.

Abstract: A programmable network technology-based multi-homed network routing and forwarding method includes a data packet performing address-to-port matching a forwarding flow table address matching method; classifying addresses to matched ports corresponding to a destination address group; selecting a link information table and a port selection function, an appropriate port and a corresponding destination address group; cropping destination address groups of the remaining ports using an address filtering function and previous hop information carried in data to further crop the remaining destination address groups and addresses in the destination address group, a single destination address can be determined using a single address selection method, taking the single destination address using a universal routing and forwarding method to forward a packet to the destination address; otherwise, packaging the destination address group, and using a single address selection method to determine the single destination address h

Abstract: A distributed node intra-group task scheduling method includes: step 101) collecting resource state information and history service information about each node in a group; step 102) receiving a service request command and parsing the request command to obtain a request task; step 103) according to the resource state information and history service information about each node, calculating the resource utilization rate increment after each node in the group loads a task and the occupation condition of each kind of resource in the group; and according to a principle of making the resource utilization rate increment of each node in the group as low as possible and the occupation of each kind of resource in the group as balanced as possible, selecting service execution nodes in the group, and providing the request task by the selected service execution nodes.

Abstract: A big data processing method for a segment-based two-grade deep learning model. The method includes: step (1), constructing and training a segment-based two-grade deep learning model, wherein the model is divided into two grades in a longitudinal level: a first grade and a second grade, each layer of the first grade is divided into M segments in a horizontal direction, and the weight between neuron nodes of adjacent layers in different segments of the first grade is zero; step (2), dividing big data to be processed into M sub-sets according to the type of the data and respectively inputting same into M segments of a first layer of the segment-based two-grade deep learning model for processing; and step (3), outputting a big data processing result. The method of the present invention can increase the big data processing speed and shorten the processing time.

Abstract: A big data processing method based on a deep learning model satisfying K-degree sparse constraints comprises: step 1), constructing a deep learning model satisfying K-degree sparse constraints using an un-marked training sample via a gradient pruning method, wherein the K-degree sparse constraints comprise a node K-degree sparse constraint and a level K-degree sparse constraint; step 2), inputting an updated training sample into the deep learning model satisfying the K-degree sparse constraints, and optimizing a weight parameter of each layer of the model, so as to obtain an optimized deep learning model satisfying the K-degree sparse constraint; and step 3), inputting big data to be processed into the optimized deep learning model satisfying the K-degree sparse constraints for processing, and finally outputting a processing result. The method in the present invention can reduce the difficulty of big data processing and increase the speed of big data processing.

Abstract: A system and method for providing an on-site service includes a plurality of nodes, each containing: a neighborhood node set generation module, for generating a neighborhood node set on the basis of the bidirectional link bandwidth between a local node and a neighboring node; a neighborhood information index table generation module, for generating a neighborhood information index table of the local node; a candidate service point selection module, for selecting according to a selection function a candidate service node from the set of neighboring nodes; the definition of the selection function being: for a current service request, computing the difference between the QoS of the neighboring node i executing the service request and the QoS of the local node executing the service request; if the computed difference is smaller than a set threshold, the neighboring node i serving as the candidate service node; a service scheduling module, for receiving status information and feedback information provided in real t

Abstract: An SSH protocol-based session parsing method and system. The method includes: step 101) receiving an SSH protocol-based session data packet and respectively preprocessing the received data packet as follows: when one data packet contains several complete messages, extracting each complete message from the data packet; when the data packet contains an incomplete message, firstly caching the incomplete message contained in the current data packet, then receiving the subsequent data packet, and finally splicing the message contained in the subsequent data packet with content of the cached message until a complete message is spliced; and step 102) extracting a content associated with log generation from each obtained complete message, packaging the content associated with the log according to a set format, and then obtaining a parsed log and finishing the session parsing.

Abstract: The present invention relates to a content-centric network on-demand distance vector route method, comprising: a request source node of a required request route broadcasts a route request packet CCN_RREQ to other nodes in a network, and starts a route discovery process; after a target node receives the route request packet CCN_RREQ, replying with a route reply packet CCN_RREP; after the request source node receives the route reply packet CCN_RREP returned by the target node, establishing a path between the request source node and the target node according to the content of the route reply packet CCN_RREP.

Abstract: The present invention relates to a multi-protocol routing system, comprising a route determining module and a forwarding module which are located in network layer, wherein the route determining module is used for determining, based on the related information of applications and networks, the types of routable protocols and the next-hop path which are to be selected in the current routing phase; the forwarding module, including different routable protocols and corresponding routing information, is used for periodically calculating the newest remaining network resources and the state of link during a routing process and updating the forwarding table in current state. By integrating various characteristics and states of applications as well as different routable protocols, the present invention realizes real-time protocol selection and dynamic routing. Moreover, the present invention is easy to be expanded to other protocols, thus being adaptive to the continuous development and evolution of network services.

Abstract: A system includes a named data networking and a content delivery network, and a joint processing gateway. The named data networking is used for providing data content to a node in the content delivery network or sending a content request command to the content delivery network. The named data networking is a network formed by devices supporting a named data networking protocol. The content delivery network is used for providing data content to a node in the named data networking network or sending a content request command to the named data networking network. The content delivery network is a content delivery network set up over an IP network. The joint processing gateway is used for converting content data and transmitting the converted data from the named data networking to the content delivery network; and used for converting content data and transmitting the converted content data from the content delivery network to the named data networking.

Abstract: The present invention relates to a Layer 2 adapter selecting system, including a Layer 3 packet requirements extracting module, a destination Layer 2 address acquiring module, a Layer 2 adapter metric acquiring module, an appropriate Layer 2 adapter identifier selecting module, a packet caching module and a packet scheduling module; wherein the packet caching module is configured to cache a received Layer 3 packet; the Layer 3 packet requirements extracting module is configured to read the Layer 3 packet and extract the type and a parameter value of the requirements; the Layer 2 adapter metric acquiring module is configured to acquire instant metric parameter values of each of the adapters; the appropriate Layer 2 adapter identifier selecting module calculates an adapter identifier and a Layer 3 packet metric; the destination Layer 2 address acquiring module is configured to acquire a Layer 2 address of each of the adapters associated with a destination Layer 3 address; and the packet scheduling module calls

Abstract: A system and method for providing an on-site service includes a plurality of nodes, each containing: a neighborhood node set generation module, for generating a neighborhood node set on the basis of the bidirectional link bandwidth between a local node and a neighboring node; a neighborhood information index table generation module, for generating a neighborhood information index table of the local node; a candidate service point selection module, for selecting according to a selection function a candidate service node from the set of neighboring nodes; the definition of the selection function being: for a current service request, computing the difference between the QoS of the neighboring node i executing the service request and the QoS of the local node executing the service request; if the computed difference is smaller than a set threshold, the neighboring node i serving as the candidate service node; a service scheduling module, for receiving status information and feedback information provided in real t

Abstract: The present invention relates to a Layer 2 adapter selecting system, including a Layer 3 packet requirements extracting module, a destination Layer 2 address acquiring module, a Layer 2 adapter metric acquiring module, an appropriate Layer 2 adapter identifier selecting module, a packet caching module and a packet scheduling module; wherein the packet caching module is configured to cache a received Layer 3 packet; the Layer 3 packet requirements extracting module is configured to read the Layer 3 packet and extract the type and a parameter value of the requirements; the Layer 2 adapter metric acquiring module is configured to acquire instant metric parameter values of each of the adapters; the appropriate Layer 2 adapter identifier selecting module calculates an adapter identifier and a Layer 3 packet metric; the destination Layer 2 address acquiring module is configured to acquire a Layer 2 address of each of the adapters associated with a destination Layer 3 address; and the packet scheduling module calls

Abstract: The present invention relates to a multi-protocol routing system, comprising a route determining module and a forwarding module which are located in network layer, wherein the route determining module is used for determining, based on the related information of applications and networks, the types of routable protocols and the next-hop path which are to be selected in the current routing phase; the forwarding module, including different routable protocols and corresponding routing information, is used for periodically calculating the newest remaining network resources and the state of link during a routing process and updating the forwarding table in current state. By integrating various characteristics and states of applications as well as different routable protocols, the present invention realizes real-time protocol selection and dynamic routing. Moreover, the present invention is easy to be expanded to other protocols, thus being adaptive to the continuous development and evolution of network services.

Abstract: A data storing method of a (d, k) Moore graph-based network storage structure is provided. The method arranges a number of formula(I) storing nodes in a wide area network (WAN) environment in accordance with a (d, k) Moore graph to form a strongly regular network structure, and utilizes implementation methods of different separate redundant array of independent disks (RAID) techniques of multiple degrees of reliability, thereby enabling data storing supported by network-RAIDs (NRAID) of multiple degrees of reliability in a network environment; said network structure of a strongly regular graph makes an arbitrarily accessed storing node as a controlling node, and uses other d+d(d?1) storing nodes as neighboring nodes of the controlling node, wherein d is the number of one-hop neighboring nodes; and d(d?1) are the number of two-hop neighboring nodes; the controlling node stores metadata of stored data, and sends information of accessing data; the neighboring nodes provide data storing services.

Abstract: A storage network structure comprises a primary distributed storage network (a cluster distributed storage network or a DHT (Distributed Hash Table)-based peer-to-peer storage network) and a Peterson graph-based storage network. The storage network structure provides different reliability classes for applications and combines the advantages of a peer-to-peer based distributed network storage with Peterson graph-based network storage, solve the problem of a large quantity of data migrating by utilizing the better media data migrating non-movable property from the Peterson graph-based network storage, and screen the single failure of the cluster storage structure and avoid performance degradation of peer-to-peer wide area storage by combining with the availability and robustness of Distributed Hash Table.

Abstract: The language of origin of a word or named entity is predicted using estimates of frequency of occurrence of the word or named entity in different languages. In one embodiment, the normalized frequency of occurrence of the word or named entity in a variety of different languages is estimated and the values are used as features in a feature vector which is scored and used to identify language of origin.

Abstract: The present invention provides a storage network structure based on Peterson graph and data read-write method thereof. The aforementioned storage network structure consists of a primary distributed storage network and a Peterson graph-based storage network, in which, the said primary distributed storage network may be a cluster distributed storage network or a DHT (Distributed Hash Table)-based peer-to-peer storage network consisting of n nodes; and the said Peterson graph-based storage network is formed by 10 nodes selected from aforementioned storage node set according to policies.