, pp.11-15 http://dx.doi.org/10.14257/astl.2014.53.03 UPS battery remote monitoring system in cloud computing Shiwei Li, Haiying Wang, Qi Fan School of Automation, Harbin University of Science and Technology Harbin 150080, China Email: wanghy@hrbust.edu.cn Abstract. This paper introduced the structure and operational status of UPS Battery Remote Monitoring System, analyzed UPS battery monitoring data and UPS battery computational features. In allusion to the UPS battery remote monitoring system of long-term monitoring to the huge amounts of data, putting forward the application of cloud computing technology to satisfy huge amounts of UPS battery data access to reliable and high-performance computing, realizing Stability, extensibility and the diversification of applications of UPS battery remote monitoring system. Through the comparative analysis of cloud computing technology selection, putting forward the UPS battery remote monitoring system based on cloud computing architecture, in the end, the preliminary design scheme was presented. Keywords: Cloud computing, UPS battery monitoring data, High performance computing, Reliable access 1 Introduction As an important energy storage unit of UPS, battery performance and quality directly affect the safety and stable operation of machine room equipment. Today, as the scale of the enterprise to further expand, production area is more and more widely, the number of UPS increased, the dispersion problems of UPS should be solved as soon as possible. At the same time, the practicability and importance of UPS battery remote monitoring system is increasingly prominent. UPS battery monitoring data and its analysis computation showed the geometric level growth. The user puts forward high demand for remote monitoring system of UPS battery, puts forward more and more high requirements for reliable access to mass data, high performance computing, the stability and scalability of application system and the diversification of application mode. In this paper, using cloud computing technology [1-3], building the new UPS battery remote monitoring system: Through the cloud data storage [4] to solve the problem of reliable access to huge amounts of data [5]; Through analyzing cloud computing to solve the problem of high performance computing; Through cloud Business applications to solve the problem of stability and scalability of the ISSN: 2287-1233 ASTL Copyright 2014 SERSC
application system; Through cloud terminal [6] to achieve the diversification of application mode. 2 The overall architecture design The cloud computing technology based on massive UPS battery monitoring data as far as possible according to open source software technology. Through the research, Hadoop will be used as the basis for realization. Hadoop have the two parts distributed File system (HDFS) and distributed computing MapReduce. HDFS is a master/slave structure. MapReduce is a programming model, which is used to calculate the large amount of data. Based on Hadoop, you can write distributed parallel programs of huge amounts of data can be processed and runs on a large-scale computer clusters composed of hundreds or thousands of nodes. This article refers to cloud computing architecture, combined with the actual needs of UPS battery remote monitoring, taking the cloud computing technology into the UPS battery remote monitoring platform, we designed a Remote Monitoring System based on Cloud Computing, Its architecture shown in Figure 1.In the figure: SAN is storage area network; NAS is network-attached storage. Cross-Platform Business Terminal Business Desktop Analysis and Calculation Server Group Server Group Computing Resources Pool Management Unified Storage Support SAN Storage Arrays Distributed File Storage NAS storage arrays Fig.1. The architecture of UPS battery remote monitoring system in cloud computing 3 Cloud data storage layer Remote Monitoring System Stored data not only include real-time monitoring data, also including the equipment running status data. UPS battery status the raw data belong to unsustainable data, so ensure reliable access of huge amounts of UPS battery status data have important significance in UPS battery research. Cloud data storage of Remote Monitoring System can be used Hadoop technical architecture to expand, Using HBase as the massive data management system, Hadoop HDFS as a distributed storage system, Xen as a virtual machine. The data is split by Hadoop HDFS, divided into multiple data block, stored 12 Copyright 2014 SERSC
on different storage nodes. Cluster consists of a master server and a certain number of data servers: Master server is responsible for managing file system namespace and customer access to the file, data server is responsible for managing storage of the node. Building a master server in UPS battery monitoring laboratory of the head office center, and can be equipped with a standby master server, Other branch database server as data server, Thus building a unified database has good fault tolerant performance of UPS battery. 4 Analysis and calculation of cloud layer The Analysis and calculation of massive UPS battery monitoring data mainly to complete real-time data processing, multiple dimensions of time and space on statistics and data mining, etc. Existing analysis and calculation of massive UPS battery monitoring data are carried out in their respective central station, the efficiency is very low. Using analysis and calculation of cloud, collection analysis and calculation server s performance of each branch to analyze and calculate data, greatly improved the performance of the system. In the selection of Hadoop technology to realize data storage on the basis of the clouds, Hadoop technology roadmap s MapReduce can be used as parallel programming model. MapReduce-based data parallel processing system can provide high performance parallel computing ability and general parallel algorithm development environment for forecasts and real-time processing and multidimensional space-time analysis of massive data calculation of sudden huge amounts of data, mainly consists of algorithm calls and task management. 5 Cloud business application layer Adoption of cloud business applications, it will integrate central station server resources of each system. In the process of implementation, using a service-based architecture, UPS battery applications are separated into application service layer and application layer, application service layer provides business data application services, application layer can be displayed in various ways, such as Web browser, PC applications and mobile applications, as shown in figure 2. Copyright 2014 SERSC 13
The Layer The Web Browser PC Mobile Service Layer The Business Process Layer Data Integration Layer Accident Diagnosis UPS Battery Query Failure Analysis Data Information statistics Target Data Maintenance Information Statistics pretreatment Target Based Data UPS Battery Alarm Target Alarm Information Equipment State Diagnosis Equipment Operating Status Inquiry UPS battery warning analysis UPS battery predictive analysis UPS battery alarm analysis Target Fault Data Equipment State Related Data Information Security System Fig.2. System architecture based on the service system 6 Conclusions In this paper, using the latest cloud computing technology, on the basis of existing systems, proposed Remote Monitoring System in Cloud Computing. Follow-up work will use Hadoop technology roadmap to establish a UPS battery remote monitoring platform prototype system, realizing the business layer of stratification and development of diversified lightweight terminal system, experimental test on the prototype system, from testing and comparing the performance and scalability to verify the validity of the proposed method. Acknowledgments. This paper is partially supported by Technological Innovation Foundation for Leaders of Disciplines in Science of Harbin (2014RFXXJ032). References 1. Yang, G., Sui, Y. L.: Adaptive approach to monitor resource for cloud computing platform [J]. Computer Engineering and s, 2009, 45(29):14-19. 2. Wang, D. W., Song, Y. Q. and Zhu, Y. L.: Information platform of smart grid based on cloud computing [J]. Automation of Electric Power Systems, 2010, 34(22):7-12. 3. Deng, Z. L.: Network topology design of cloud computing and Hadoop Platform research [D]. University of Science and Technology of China, 2009. 4. Zhao, J. H., Wen, F. S. and Xue, Y. S.; Cloud computing: implementing an essential computing platform for future power systems [J]. Automation of Electric Power Systems, 2010, 34(15):1-8. 14 Copyright 2014 SERSC
5. Chen, Y.: Design and implementation of communication data distributed query algorithm that based on Hadoop platform [D], Beijing Jiaotong University, 2009. 6. Wang, R. H.: Research of distributed log analysis system that based on Hadoop cluster [J], Science and technology information, 2009, 15(60). Copyright 2014 SERSC 15