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A HISTORY-BASED HEURISTIC TO OPTIMIZE DATA ACCESS IN DISTRIBUTED ENVIRONMENTS

机译：基于历史的启发式优化分布式环境中的数据访问

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摘要

Data Grid, a class of Grid Computing, aims at providing services and infrastructure to data-intensive distributed applications which need to access, transfer and modify large data storages. A common issue on Data Grids is the data access optimization, which has been addressed through different approaches such as: bio-inspired and replication (LRU, LFU, Economic Model) strategies. However, few of these approaches consider application features to optimize data access operations (read-and-write). These features define the application behavior, which can support the optimization of operations and, consequently, improve the global system performance. Motivated by the need of efficient data access in large scale distributed environments and by the use of application characteristics, this paper proposes a new heuristic to optimize data accesses (read-and-write operations) based on application historical behavior. Simulation results confirm that the heuristic reduces application execution time when compared to other approaches commonly considered.
机译：数据网格是网格计算的一类，旨在为需要访问，传输和修改大型数据存储的数据密集型分布式应用程序提供服务和基础架构。数据网格的一个常见问题是数据访问优化，已通过不同方法解决了这些问题，例如：生物启发和复制（LRU，LFU，经济模型）策略。但是，这些方法中很少有考虑使用应用程序功能来优化数据访问操作（读写）。这些功能定义了应用程序行为，它可以支持操作的优化，从而提高全局系统的性能。出于在大规模分布式环境中进行有效数据访问的需求以及应用程序特征的使用，本文提出了一种新的启发式方法，可以基于应用程序的历史行为来优化数据访问（读写操作）。仿真结果证实，与通常考虑的其他方法相比，该启发式方法减少了应用程序的执行时间。

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