Q-Tree: A Multi-Attribute Based Range Query Solution for Tele-immersive Framework

A. Arefin, Md Yusuf Sarwar Uddin, Indranil Gupta, K. Nahrstedt
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引用次数: 15

Abstract

Users and administrators of large distributed systems are frequently in need of monitoring and management of its various components, data items and resources. Though there exist several distributed query and aggregation systems, the clustered structure of tele-immersive interactive frameworks and their time-sensitive nature and application requirements represent a new class of systems which poses different challenges on this distributed search. Multi-attribute composite range queries are one of the key features in this class. Queries are given in high level descriptions and then transformed into multi-attribute composite range queries. Designing such a query engine with minimum traffic overhead, low service latency, and with static and dynamic nature of large datasets, is a challenging task. In this paper, we propose a general multi-attribute based range query framework, Q-Tree, that provides efficient support for this class of systems. In order to serve efficient queries, Q-Tree builds a single topology-aware tree overlay by connecting the participating nodes in a bottom-up approach, and assigns range intervals on each node in a hierarchical manner. We show the relative strength of Q-Tree by analytically comparing it against P-Tree, P-Ring, Skip-Graph and Chord. With fine-grained load balancing and overlay maintenance, our simulations with PlanetLab traces show that our approach can answer complex queries within a fraction of a second.
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Q-Tree:基于多属性的远程沉浸式框架范围查询解决方案
大型分布式系统的用户和管理员经常需要监视和管理其各种组件、数据项和资源。虽然目前已有几种分布式查询和聚合系统,但远程沉浸式交互框架的集群结构及其时间敏感性和应用需求代表了一类新的系统,这对分布式搜索提出了不同的挑战。多属性复合范围查询是该类的关键特性之一。查询以高级描述形式给出,然后转换为多属性复合范围查询。设计这样一个具有最小流量开销、低服务延迟以及大型数据集的静态和动态特性的查询引擎是一项具有挑战性的任务。在本文中,我们提出了一个通用的基于多属性的范围查询框架Q-Tree,为这类系统提供了有效的支持。为了提供高效的查询服务,Q-Tree以自底向上的方式连接参与节点,构建单个拓扑感知树覆盖,并以分层方式在每个节点上分配范围间隔。通过与P-Tree、P-Ring、Skip-Graph和Chord进行分析比较,证明了Q-Tree的相对强度。通过细粒度负载平衡和覆盖维护,我们使用PlanetLab跟踪进行的模拟表明,我们的方法可以在几分之一秒内回答复杂的查询。
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