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Advanced Replica-Based Data Access Prediction and Optimization Approach in Distributed Environment

机译:分布式环境中基于高级副本的数据访问预测和优化方法

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The main purpose of the distributed system is to coordinate the use of shared resources or provide communication services to the users. In order to achieve high performances in distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. The data can be access in distributed manner within organizations allows more redundancy and high flexibility in structure for system behaviour. In this system applies many strategies for supporting the online prediction of system behaviour using PSO technique. The main aspect to accessing data is finding the system behaviour and checks the operation conducting on the system through the reducing the iteration process in migration and replication with support strategies models to designed for schedulers. If procedure a high throughput strategies models in a data access optimization behaviour for a mapreduce framework and also to predicate system behaviour. The data access operation finds to automatic and online prediction of readandwrite operations performed by optimization processes and dynamically to predict CPU performances to accessing the resources in efficient way. Data can be process in PSO technique based on scheduler by observe the metadata that placed in the data centres.
机译:分布式系统的主要目的是协调共享资源的使用或向用户提供通信服务。为了在分布式存储系统中实现高性能,一直在考虑数据复制,迁移,分发和访问并行性的技术。可以在组织内以分布式方式访问数据,从而为系统行为提供更多的冗余和结构上的高度灵活性。在该系统中,应用了许多策略来支持使用PSO技术进行系统行为的在线预测。访问数据的主要方面是查找系统行为,并通过使用针对调度程序设计的支持策略模型来减少迁移和复制中的迭代过程,从而检查系统上进行的操作。如果执行此过程,则高吞吐量策略将为mapreduce框架的数据访问优化行为建模,并预测系统行为。数据访问操作可发现对优化过程执行的读写操作的自动和在线预测,并可以动态地预测CPU性能以有效地访问资源。通过观察放置在数据中心中的元数据,可以使用基于调度程序的PSO技术处理数据。

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