Zhe Wang’s scientific contributions

The network candidate routing data stream is restricted to reroute the data stream of same origin and same destination, which brings great limitation to the application of the algorithm. This paper proposes a multi-objective optimization oriented rerouting algorithm for congested data streams. The algorithm detects and aggregates data streams on congested links, and reroutes the detected data streams. When there is no data stream, the data streams are arranged from large to small in the congested links, and the appropriate number of data streams are selected as candidate data streams, and small streams are aggregated by source and destination addresses. In order to determine the maximum network traffic and the optimal routing of candidate data streams, multi-objective optimization is introduced to jointly optimize the network load data stream maximization and network load balancing. Finally, a non dominated genetic algorithm with elite strategy is designed to solve the multi-objective optimization problem.

Network operation and maintenance (O & M) activities of data centers focus mainly on checking the operating states of devices. O & M engineers determine how services are running and the bearing capacity of a data center by checking the operating states of devices. However, this method cannot reflect the real transmission status of business data; therefore, engineers cannot fully comprehensively perceive the overall running conditions of businesses. In this paper, ERSPAN (Encapsulated Remote Switch Port Analyzer) technology is applied to deliver stream matching rules in the forwarding path of TCP packets and mirror the TCP packets into the network O & M AI collector, which is used to conduct an in-depth analysis on the TCP packets, collect traffic statistics, recapture the forwarding path, carry out delayed computing, and identify applications. This enables O & M engineers to comprehensively perceive the service bearing status in a data center, and form a tightly coupled correlation model between networks and services through end-to-end visualized modeling, providing comprehensive technical support for data center optimization and early warning of network risks.

With the development of 5G, network attacking becomes more and more easy. Many system vulnerability are utilized to be attacked via 5G technology. It leads that the network attack frequency turn to high, and the network attack strength turns to strong. Among all network attack identification methods, outlier detection is one of the most important one. It aims to find data which is much different from most of the others. In this paper, we propose an outlier detection based framework to support network-attack identification. It first uses a novel algorithm to construct core point set so as support efficiently outlier detection. Next, it uses a novel index named ZB-Tree to manage these core points. Thirdly, we propose a predictive IP-table to handle and predict suspicious IP addresses. In this way, we could identify most suspicious IP addresses based on the position relationships among different base stations. Theoretical analysis and extensive experimental results demonstrate the effectiveness of the proposed algorithms.

With the rapid development of the mobile internet, the volume of data has grown exponentially, and the content of data become more complicated. It is hard for people to select useful information from such a large number of data. In this paper, we study the problem of approximate sub-graph matching over knowledge graph. We first propose two algorithms to reduce the scale of knowledge graph. Next, we use an efficient algorithm to find similarity sub-graphs. Thirdly, we use skyline technique to further select high quality sub-graphs from the matching results. Theoretical analysis and extensive experimental results demonstrate the effectiveness of the proposed algorithms.

This paper introduces various concepts that relate to information technology and the development of energy transmission and distribution. Key challenges need to be addressed in relation to energy consumption, such as the need to be responsive to current demand, which have been addressed through information technology systems. With the increased connectedness of energy systems, there has also been an increased need to ensure the information security of these systems. The Internet of Things (IoT) concept will be reviewed in relation to the connection of objects in energy systems as well as the concepts of Big Data and Cloud Computing. The former has developed in response to the need to predict energy usage more accurately and the latter offers the advantages of increased failover potential as well as much faster provisioning of enhanced capacity in IT systems to meet consumer demand.