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OPC: A Distributed Computing and Memory Computing-Based Effective Solution of Big Data

机译:OPC:基于分布式计算和内存计算的大数据解决方案

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The Big Data computing is one of hot spots of the internet of things and cloud computing. How to compute efficiently on the Big Data is the key of improving performance. By means of distributed computing or memory computing, many companies and institutions provide some technologies and produces. But they are invalid in the scene in which there are real-time demands in the low-configure cluster. To deal with the problem, this paper provides a distributed computing and memory computing-based effective solution (Objectification Parallel Computing, OPC). In the solution, the data can be formatted into object. Then the objects are distributed stored in the computer memories and parallel compute to complete tasks. The OPC is applied to the Electric Asset Quality Supervision Manage System (EAQSMS) of State Grid of China, the result shows that with PCs the system is efficiently available, reliable, and flexible expansible.
机译:大数据计算是物联网和云计算的热点之一。如何在大数据上有效地计算是提高性能的关键。通过分布式计算或记忆计算,许多公司和机构提供了一些技术并产生。但它们在场景中无效,其中在低配置群集中存在实时需求。要解决该问题,本文提供了一种分布式计算和基于内存计算的有效解决方案(ObjectIfication Parallic Computing,OPC)。在解决方案中,数据可以格式化为对象。然后,对象被分发存储在计算机存储器中并并行计算到完整任务。 OPC应用于中国国家电网的电力资产质量监督管理系统(EAQSMS),结果表明,使用PCS,系统有效可用,可靠,可携带灵活。

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