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Kalman Filtering-based Traffic Prediction for Software Defined Intra-data Center Networks
( Jacques Mbous ),( Tao Jiang ),( Ming Tang ),( Songnian Fu ),( Deming Liu ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.6
Global data center IP traffic is expected to reach 20.6 zettabytes (ZB) by the end of 2021. Intra-data center networks (Intra-DCN) will account for 71.5% of the data center traffic flow and will be the largest portion of the traffic. The understanding of traffic distribution in Intra-DCN is still sketchy. It causes significant amount of bandwidth to go unutilized, and creates avoidable choke points. Conventional transport protocols such as Optical Packet Switching (OPS) and Optical Burst Switching (OBS) allow a one-sided view of the traffic flow in the network. This therefore causes disjointed and uncoordinated decision-making at each node. For effective resource planning, there is the need to consider joining the distributed with centralized management which anticipates the system’s needs and regulates the entire network. Methods derived from Kalman filters have proved effective in planning road networks. Considering the network available bandwidth as data transport highways, we propose an intelligent enhanced SDN concept applied to OBS architecture. A management plane (MP) is added to conventional control (CP) and data planes (DP). The MP assembles the traffic spatio-temporal parameters from ingress nodes, uses Kalman filtering prediction-based algorithm to estimate traffic demand. Prior to packets arrival at edges nodes, it regularly forwards updates of resources allocation to CPs. Simulations were done on a hybrid scheme (1+1) and on the centralized OBS. The results demonstrated that the proposition decreases the packet loss ratio. It also improves network latency and throughput―up to 84 and 51%, respectively, versus the traditional scheme.
Shengcan Lu,Zhan Wang,Jianrong Pan,Tulong Yin,Deming Liu 대한토목학회 2022 KSCE Journal of Civil Engineering Vol.26 No.12
The post-earthquake reports show that failure mode control design is of great significance to improve the seismic performance of structures. To realize the optimal design of failure mode in semi-rigid steel frame, a failure mode optimisation design method based on an elite retained genetic algorithm was developed in this study. Firstly, a database of 96 extended end-plate connections was established using the ABAQUS finite element software to obtain the initial rotational stiffness and yield moment under failure criteria. Secondly, with section size as the optimisation variable, failure mode as the constraint condition, and total steel consumptionas the objective function, elite gene transfer was realized through gene sequence replication, hybridisation and mutation. With a 10-story, 3-span, semi-rigid steel frame as an example is proposed to demonstrate the application of the proposed semi-rigid design method, and pushover and time history analyses were conducted on the optimized results to verify the accuracy of the proposed method. The analysis results showed that the stable and reliable optimal design results were obtained through 1317 generation genetic iterative calculations, and the energy consumption ratio of the semi-rigid connection (including the beam ends) was 95.82% and that of the column was 4.18%; in addition, 76% of the beam ends had plastic hinges to dissipate energy in the pushover analysis. This finding indicates that the optimized frame can achieve the global failure mode during earthquakes, and the design method can effectively improve the seismic performance of the frame.