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MR-CloudSim: Designing and implementing MapReduce computing model on CloudSim

机译:MR-CloudSim:在CloudSim上设计和实现MapReduce计算模型

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Nowadays, we can hear the word “cloud” everywhere. Cloud web storage, cloud-based web applications, and cloud networks, they all indicate that cloud has become more important than it has ever been. Internet service providers (ISP) and internet contents providers (ICP) are providing more and more services with cloud computing, and also in mobile computing environments, a cloud service is drawing much attention from the community. The biggest advantage of cloud computing is clear. As far as the user is connected to the cloud, (s)he can have all the extra computing power and storage no matter what his device is. However, empirical validation of various research projects on a cloud system seems to be ineffective in both cost and time since it requires involving to a huge amount of computing facilities and resources. Even with its importance as commerce and inconvenience of researching, there are not many simulators that can provide a simulation environment for cloud computing and datacenters. Thus, we propose a simulation tool that supports the MapReduce model, the most widely used programming model for big data processing, implemented on CloudSim, an open source simulator for cloud computing and datacenters.
机译:如今,我们到处都能听到“云”这个词。云Web存储,基于云的Web应用程序和云网络,都表明云比以往任何时候都更加重要。互联网服务提供商(ISP)和互联网内容提供商(ICP)通过云计算提供越来越多的服务,并且在移动计算环境中,云服务也引起了社区的广泛关注。云计算的最大优势是显而易见的。只要用户连接到云,无论他的设备是什么,他都可以拥有所有额外的计算能力和存储。但是,对云系统上各种研究项目的经验验证似乎在成本和时间上都是无效的,因为它需要涉及大量的计算设施和资源。尽管它作为商务上的重要性和研究的不便,但没有多少模拟器可以为云计算和数据中心提供模拟环境。因此,我们提出了一种支持MapReduce模型的仿真工具,该模型是用于大数据处理的最广泛使用的编程模型,并在CloudSim(云计算和数据中心的开源模拟器)上实现。

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