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Leveraging a Compound Graph-Based DHT for Multi-Attribute Range Queries with Performance Analysis

机译：利用基于复合图的DHT进行性能分析的多属性范围查询

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摘要

Resource discovery is critical to the usability and accessibility of grid computing systems. Distributed Hash Table (DHT) has been applied to grid systems as a distributed mechanism for providing scalable range-query and multi-attribute resource discovery. Multi-DHT-based approaches depend on multiple DHT networks with each network responsible for a single attribute. Single-DHT-based approaches keep the resource information of all attributes in a single node. Both classes of approaches lead to high overhead. In this paper, we propose a Low-Overhead Range-query Multi-attribute (LORM) DHT-based resource discovery approach. Unlike other DHT-based approaches, LORM relies on a single compound graph-based DHT network and distributes resource information among nodes in balance by taking advantage of the compound graph structure. Moreover, it has high capability to handle the large-scale and dynamic characteristics of resources in grids. Experimental results demonstrate the efficiency of LORM in comparison with other resource discovery approaches. LORM dramatically reduces maintenance and resource discovery overhead. In addition, it yields significant improvements in resource location efficiency. We also analyze the performance of the LORM approach rigorously by comparing it with other multi-DHT-based and single-DHT-based approaches with respect to their overhead and efficiency. The analytical results are consistent with experimental results, and prove the superiority of the LORM approach in theory.
机译：资源发现对于网格计算系统的可用性和可访问性至关重要。分布式哈希表（DHT）已作为一种分布式机制应用于网格系统，用于提供可伸缩的范围查询和多属性资源发现。基于多DHT的方法依赖于多个DHT网络，每个网络负责一个属性。基于单DHT的方法将所有属性的资源信息保留在单个节点中。两种方法都导致高开销。在本文中，我们提出了一种基于低开销范围查询多属性（LORM）DHT的资源发现方法。与其他基于DHT的方法不同，LORM依赖于单个基于复合图的DHT网络，并通过利用复合图结构在平衡的节点之间分配资源信息。而且，它具有处理网格中资源的大规模和动态特性的高能力。实验结果表明，与其他资源发现方法相比，LORM的效率更高。 LORM大大减少了维护和资源发现开销。此外，它还可以显着提高资源定位效率。我们还通过将LORM方法与其他基于多DHT和基于单DHT的方法进行比较，就其开销和效率进行了严格的分析。分析结果与实验结果一致，从理论上证明了LORM方法的优越性。

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