Research on Optimized Scheduling for Cost Control Service in the Smart Grid System

被引：0

作者：

Shi Y.-L. ^{[1
,2
]}

Zhang K. ^{[1
,2
,3
]}

Rong Y.-P. ^{[4
]}

Zhu W.-Y. ^{[4
]}

Chen Z.-Y. ^{[1
]}

机构：

[1] School of Software, Shandong University, Jinan

[2] Dareway Software Co., Ltd., Jinan

[3] School of Information Science and Engineering, University of Jinan, Jinan

[4] State Grid Shandong Electric Power Company, Jinan

来源：

Jisuanji Xuebao/Chinese Journal of Computers | 2020年 / 43卷 / 02期

关键词：

Cost control; Intelligent service; Resource management; Scheduling optimization; Smart grid;

D O I：

10.11897/SP.J.1016.2020.00272

中图分类号：

学科分类号：

摘要：

Cost control instruction delivery is one of the services offered by the smart grid system, which is composed of application server cluster, front-end server cluster and smart electric equipment. The cost control instructions are generated and processed in the application server cluster, and then are sent to the front-end server cluster according to the specified policy. The front-end server clusters receive the cost control instructions and then send them to the specific terminals. After receiving these cost control instructions, the terminals send them to the specific smart electricity meter. The status information of smart electricity meters could also be collected and processed in the application server cluster. In this cost control instruction delivery process, the load imbalance issue becomes the influencing factor which affects the success rate and efficiency of cost control service delivery. The load imbalance caused by available resource inequality between application server cluster and front-end server cluster could affect the cost control instruction delivery process. It could lower the execution success rate and efficiency of cost control service delivery. In addition, the stationary mapping between front-end servers and terminals is not appropriate for the dynamic cost control instruction delivery environments. It could give rise to the imbalance in front-end server cluster. The cost control instructions would be sent to the front-end server with heavy load, in which could extend the execution time of cost control service and lower the cost control instruction delivery efficiency. For the above issues in the cost control instruction delivery process, this paper deeply examines and studies the process of cost control instruction delivery, analyzes the problems and bottleneck of the delivery process. For the load imbalance appeared in the cost control instruction delivery process, this paper proposes an optimized scheduling model at cost control service in smart grid system based on server loads prediction and load balancing, which could alleviate the load imbalance issue and improve the execution success rate and efficiency of cost control service. In this model, a load prediction approach based on time-series data is proposed to predict the server load of application server and front-end server. The accurate prediction of server load is the basis and premise of optimized scheduling for cost control instruction delivery service. Then cost control instruction balancing sending algorithm is proposed based on the predicted server load. It could select the appropriate set of cost control instructions and send them to the appropriate server according to their predicted load. This could improve the execution success rate of cost control service and the delivery efficiency. Considering the imbalance issue in front-end server cluster, a terminal balance distribution algorithm based on graph clustering is proposed. Combining the cost control instruction history and the present cost control instructions, the associations between terminals are analyzed. These terminals are then clustered. Then the association between front-end servers and terminals are dynamically adjusted to improve the success rate and efficiency of cost control instruction delivery. Experiments demonstrate that the proposed optimized scheduling model not only makes balance between sever cluster and terminals, but also improve the success rate and efficiency of cost control service. © 2020, Science Press. All right reserved.

引用

页码：272 / 285

页数：13

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