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Deterministic and non-deterministic query optimization techniques in the cloud computing

机译:云计算中的确定性和非确定性查询优化技术

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Query optimization is considered as one of the main challenges of query processing phases inthe cloud environments. The query optimizer attempts to provide the most optimal executionplan by considering the possible query plans. Therefore, the execution cost of a query canbe affected by some factors, including communication costs, unavailability of resources, andaccess to large distributed data sets. In addition, it is known as NP-hard problem and manyresearchers are focused on this problem in recent years. Some techniques are proposed forsolving this problem. Deterministic and non-deterministic methods are two main categories tostudy these techniques. The deterministic and non-deterministic query optimization methodscan be further divided into three subcategories, cost-based query plan enumeration, multiplequery optimization, and adaptive query optimization methods. Moreover, this paper presentsthe advantages and disadvantages of the algorithms for solving the query optimization problemsin the cloud environments. Moreover, these techniques are compared in terms of optimization,time, cost, efficiency, and scalability. Finally, some key areas are offered to improve the cloudquery optimization mechanisms in the future.
机译:查询优化被视为查询处理阶段的主要挑战之一云环境。查询优化器尝试提供最佳的执行通过考虑可能的查询计划来计划。因此,查询的执行成本可以受到一些因素的影响,包括沟通成本,资源不可用,以及访问大分布式数据集。此外,它被称为NP难题和许多人近年来,研究人员专注于这个问题。提出了一些技术解决这个问题。确定性和非确定性方法是两个主要类别研究这些技术。确定性和非确定性查询优化方法可以进一步分为三个子类别,基于成本的查询计划枚举,多个查询优化和自适应查询优化方法。而且,本文呈现解决查询优化问题的算法的优点和缺点在云环境中。此外,这些技术在优化方面进行了比较,时间,成本,效率和可扩展性。最后,提供了一些关键领域来改善云查询未来优化机制。

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