A new forwarding address for next generation networks

Zhao, Aqun; Liang, Mangui

doi:10.1631/jzus.c1100096

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…Dispatcher's empirical method [21] Simple and fast Not well adapted to new power systems Probabilistic statistical method [22][23][24][25] Comprehensive and accurate calculations Insufficient mathematical description Data-driven method [26] Based on big data and higher credibility Time scales are too loose…”

Section: Source Vantage Drawbackmentioning

confidence: 99%

“…(1) The dispatcher's experiential method refers to the approach where the system operator, based on their extensive operational experience, directly determines the AGC reserve requirements of the system by considering the electric load levels and the periodic patterns of load consumption. Alternatively, based on their operational experience, the dispatcher may calculate the AGC reserve requirements using pre-defined computational formulas and actual operational data [21]. Although this method is simple and fast, it still has the disadvantage of being insufficiently objective to be generalized on a large scale, and it is not well adapted to new power systems.…”

Section: Introductionmentioning

confidence: 99%

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Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Li,

Liang

et al. 2024

Energies

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Load forecasting is a research hotspot in academia; in the context of new power systems, the prediction and determination of load reserve capacity is also important. In order to adapt to new forms of power systems, a day-ahead automatic generation control (AGC) reserve capacity demand prediction method based on the Fourier transform and the attention mechanism combined with a bidirectional long and short-term memory neural network model (Attention-BiLSTM) optimized by an improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the response time, Fourier transform is used to refine the distinction between various types of load reserve demand, and the power of the AGC reserve band is calculated using Parseval’s theorem to obtain the reserve capacity demand sequence. The maximum mutual information coefficient method is used to explore the relevant influencing factors of the AGC reserve sequence concerning the data characteristics of the AGC reserve sequence. Then, the historical daily AGC reserve demand sequences with relevant features are input into the Attention-BiLSTM prediction model, and the improved whale algorithm is used to automatically find the optimal hyperparameters to obtain better prediction results. Finally, the arithmetic simulation results show that the model proposed in this paper has the best prediction performance with the upper (0.8810) and lower (0.6651) bounds of the coefficient of determination (R2) higher than the other models, and it has the smallest mean absolute percentage error (MAPE) and root mean square error (RMSE).

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Section: Source Vantage Drawbackmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Li,

Liang

et al. 2024

Energies

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“…The vector address is a new type of coding technique[ 16 ][ 17 ]. It differs from the above-mentioned encoding types.…”

Section: The Design and Implementation Of The Scdcn Modelmentioning

confidence: 99%

A source-controlled data center network model

Liang

Wang

2017

PLoS ONE

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The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS.

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“…But with some new branches of applications in the network, the information communication requires a higher load. At this time, the conception of vector network is proposed by Beijing Jiao tong University and the research on vector-based exchange equipment also become necessity (Zhao and Liang, 2012;Liang, 2009a).…”

Section: Introductionmentioning

confidence: 99%

Research on Performance between IP and Vector Forwarding

Zhao¹,

Zhou²,

Hao³

2013

RJASET

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With the rapid development of network size and great increase of Internet traffic, it has become a crucial work to improve the forwarding performance of the core devices of Internet or the routers. In this study, the analysis and comparison of instruction cycles occupied by the machine in the process of route lookup in IP forwarding and vector forwarding were carried out which may determine the forwarding performance of a router. The simulation experiments were also made to study the forwarding efficiency of IP forwarding and vector forwarding. The theoretical and experimental results prove that the vector forwarding method is more efficient than IP forwarding which can provide powerful evidence to the application of vector network in the Internet.

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A new forwarding address for next generation networks

Cited by 9 publications

References 19 publications

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

A source-controlled data center network model

Research on Performance between IP and Vector Forwarding

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