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STUDY ON LOAD BALANCING INTERMITTENT ENERGY BIG DATA CLOLD PLATFORM IEEE PROJECT 2018





JAVA PROJECT

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With the development of construction on smart grid and the reasonable utilization of intermittent energy source, data processing on traditional platform cannot already satisfy the intermittent energy sources. It must be a great challenge for the whole platform. According to the superiority of cloud platform on processing data as well as the load balancing of the overall cluster on the cloud platform, this paper proposes a strategy that the cloud platform is integrated with the intermittent energy sources data and the load balancing of multi-factor predictive cloud platform. Firstly, deploying the overall process of the intermittent energy data processing on a new data processing platform, and then running the multi-factor predictive cloud platform load balancing on the processing platform. Finally, simulations and experiments prove that the data processing platform proposed provides better performance and will promote the construction of smart grids.




To build an optimization platform

According to the characteristics of wind power generation equipment monitoring data and application of requirements, the reasonable utilization o fcloud computing data technology to build a disttributed storage and processing platform with high reliability and high availability will bring great convenience for subsequent data mining and data analysis. Deploy intermittent energy big data processing services to the cloud platform.

In the experiment and production environment, an optimized processing platform eill be built in which the data processing flows collected by the wind power centralization and control center are transformed between the platforms.


Multifactor forecasting cloud platform load balancing algirithm

Optimization Methods

on the optimized platform, due to the intermittency of the wind power equipment monitoring data, there are several main factors that affect the storage load imbalance between the metadata storage nodes:(1) Uneven distribution of server nodes.(2)REsource storage is not balanced.(3)File access is uneven. (4)Different hardware configuration


Multi- factor forecast cloud platform load balancing strategy

In the design of big data cloud platform for gap energy, the central task scheduling method can be a choice. Select a server node to act as a scheduling node, and other servers act as task execution nodes. Each node has a corresponding task list, which dynamically allocates tasks for dynamic load balancing.

In the face of the above problems, dynamic load balancing is designed in the gap energy data cloud platform design, storage feedback and dynamic load will be engineered for data storage and cloud platform load balancing.


Existing Sytem

Cloud computing using cloud server to provide data storage, software applications adn other services for users. Hadoop is a distributed open source framework form apache open source organization. HDFS is the distributed storage system based on HADOOP- a big data platform developed by Aoache. It has high capacity of fault-tolerant and processing. HDFS has low demand on hardware and can operate on cheap clusters. The uper layser of HDFS is the MapReduce engine, which provides parallel computing power for massive data. HDFS,MapReduce, data warehousing tool Hive adn distributed database Hbase cover the core technology of Hadoop distributed platform.

THis method requires more manual intervention to achieve the desired load balancing, while it does not efficiently optimize overall system processing load balancing and performace optimization. In the cloud platform system, to cope with the heterogeneity of thse cloud nodes and the diversity and uncertainty of user needs, it is necessary to solve the load balancing of the big data cloud platform, ehich is the basic point of performance improvement


Proposed System

proposes a strategy that the cloud platform is integrated with the intermittent energy sources data and the load balancing of multi-factor predictive cloud platform.

Firstly, deploying the overall process of the intermittent energy data processing on a new data processing platform, and then running the multi-factor predictive cloud platform load balancing on the processing platform.


Conclusion




In this paper, we havemainly studied the data processing of intermittent energy and improved an intermittent energy big data cloud platform load balancing processing model.

we migrated the intermittent energy big data to a new data processing platform, and then implemented the strategy of multi-factor predictive cloud platform load balancing. the simulation and experiment show that our new data processing platform provide a better performance of data processing.



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