基于分治法求解对称三对角矩阵特征问题的混合并行实现

基于对称三对角矩阵特征求解的分而治之方法,提出了一种改进的使用MPI/Cilk模型求解的混合并行实现,结合节点间数据并行和节点内多任务并行,实现了对分治算法中分治阶段和合并阶段的多任务划分和动态调度.节点内利用Cilk任务并行模型解决了线程级并行的数据依赖和饥饿等待等问题,提高了并行性;节点间通过改进合并过程中的通信流程,使组内进程间只进行互补的数据交换,降低了通信开销.数值实验体现了该混合并行算法在计算效率和扩展性方面的优势.     

无线传感器网络中基于采样的查询处理方法研究

本文系统介绍了PROSPECTORPROOF算法。PROSPECTORPROOF算法在预先分配一定带宽的基础上,在传感器节点内部将感知数据排序,上传被各节点证明的感知数据的数量。在改进算法中引入了本地过滤策略,使得在查询过程中节点内部实现了感知数据的过滤。改进的算法利用了PROSPECTORPROOF算法中产生的数据作进一步的查询优化,能够缩小查询范围而且能够得到准确的感知数据。较好的实现了查询的基本功能,将系统能量消耗降低了11.5%。     

面向类对象的三维空间查询

针对流体实验模拟系统中的三维空间数据，采用了基于固定三维网格划分的线性八叉树宅间数据结构，并运用了层次分明的Morton编码方法，快速有效地存储和管理数目庞大的八叉树节点。为了便于缩小空间范围，快速地进行空间查询检索，引入了面向类对象的分级查询技术，有效地提高了空间查询的速度。     

链路感知的传感器网络空间范围查询处理算法

在无线传感器网络环境中,用户经常提交空间范围查询以获取网络某局部区域的统计信息,如最大温度、平均湿度等。现有的基于路线的空间范围查询处理算法假设节点通信模型为理想的圆盘模型,而实际的网络并不满足该假设,导致其能量消耗大且查询结果质量差。提出了一种链路感知的空间范围查询处理算法LSA,它根据网络拓扑和链路质量动态地将查询区域划分为若干个网格,依次收集各网格中节点的感知数据,以生成最终的查询结果。LSA算法通过遍历查询区域内的所有网格,保证了算法查询结果的质量。提出了启发式的网格划分方法以降低节点间数据通信的丢包率,给出链路感知的数据收集算法,以减少算法的能量消耗,提高查询结果的质量。通过仿真实验系统地分析和比较了LSA算法和现有的IWQE算法的能量消耗及查询结果质量,结果表明,在绝大多数情况下,LSA算法优于IWQE算法。     

结构化加密图数据的Top-H跳节点查询

为了保护外包数据的隐私,用户通常需要对数据加密后再存储到云服务器.但数据加密后,对密文数据的查询与处理变得极为困难. 2010年, Kamara等提出结构化加密的概念,可以实现各种类型数据的高效查询,包括文本、矩阵及图数据等.利用结构化加密的思想,本文提出第一个结构化加密图数据的top-H跳节点查询方法.现有的H跳查询方案主要通过2-Hop索引计算查询节点之间的跳数来判断它们之间的可达性,当节点数达到十万或百万级时,构建2-Hop索引的计算和存储开销都非常大.本文提出的方案在满足可达性判断的同时极大地降低了存储开销,同时提高了查询效率,还实现了更加丰富的H跳范围查询.本方案采用了结构化加密中可链接(chainable)的思想,实现邻居节点的迭代查询.同时,根据用户指定的跳数(H)获取满足条件的top-H跳节点.安全性分析表明本方案满足CQA2安全.在真实数据集上的测试结果表明,本方案比已有方案更加高效.     

无线传感器网络中基于关联度的多查询优化

无线传感器网络是一种以数据为中心的网络,用户通过基站向网络提出查询请求获取所需数据。如何通过多查询的优化来减少传感器节点的能耗以延长网络生命期是无线传感器网络中需要解决的关键问题之一。提出了基于关联度的多查询优化算法,其基本思想是节点通过节点与候选父亲节点之间的关联度来选择父节点,从而被相同查询覆盖的节点聚集成一个组,多个查询间共享组中节点的数据,在网络中对查询数据进行有效的融合,充分减少了网络的数据传输量,延长了网络的生命期。理论分析和模拟实验表明该算法可以充分减少数据传输量,从而达到节能的目的。     

结构编码与簇集索引相结合的XML混合索引

提出了一种结构编码与簇集索引相结合的XML混合索引结构(H iSC)。引入簇集索引结构,将XML节点分类,尽量多地保存XML数据的结构信息,缩小查询范围,提高了查询效率并能支持关键字的查询。实验表明此索引结构可以高效并准确地查询XML数据中的结构信息。     

医疗应用服务中云计算的数据查询方法研究

针对医疗应用服务中的数据查询问题,提出了一种基于云计算的数据查询方法。该方法首先基于Random Walk方法找到查询请求的目标节点,然后通过定义服务节点的相似节点集和等价节点集来进行二次搜索,返回具有最大评价值和最低负载的节点和数据作为所需的目标节点及数据。通过两种查询方法的目标数据质量对比,发现随着服务节点数目的增加,文中方法对于提高查询质量的作用比Random Walk方法更好;通过两种方法查询目标节点的负载情况,文中的查询算法在大量服务节点间的负载情况比Random Walk方法更均衡;通过两种方法的查询路径长短比较,显示两种方法的结果较接近,但文中方法比Random Walk方法稍有优势。实验结果显示文中方法在查询之数据质量、服务节点之负载能力和查询之效率方法均好于传统策略。     

基于分类节点的P2P语义路由模型

对于P2P语义覆盖网络,语义信息的维护和智能路径的选择是实现的难点。根据小世界原理,提出了一种新的基于节点分类划分的P2P语义路由模型。通过建立节点本体来描述节点的网络结构和节点下的内容项,在此基础上创建了路由消息格式和节点分类划分的方法,然后创建了支持内容语义查询的节点内相关性内容查询算法和节点间消息路由算法。通过实验对比,该语义路由模型能够提高P2P系统下的内容查找速度并且能够显著降低占用的网络带宽。     

基于物联网的传感器节点易移动型监测区域定位算法

针对物联网监控区域中的节点由于地理位置、天气等各类原因,其位置易变且分布不均的特点,定义了一种改进的DV-Hop定位算法实现对节点的定位;首先在跳数获取阶段通过改变锚节点的数据包结构来降低节点数据存储量;在平均跳距计算阶段,引入权值对原有的平均跳距计算方式进行改进,同时节点与锚节点之间的距离计算依据参考锚节点的不同而不同;然后,在节点定位阶段,使用多边测量法及泰勒级数进行迭代求精;最后,对文中方法进行仿真实验,且与已有方法进行比较,结果证明文中方法,较好地解决物联网监测区域的节点定位问题。     

An efficient grid scheduling strategy for data parallel applications

Scheduling large-scale application in heterogeneous grid systems is a fundamental NP-complete problem that is critical to obtain good performance and execution cost. To achieve high performance in a grid system it requires effective task partitioning, resource management and load balancing. The heterogeneous and dynamic nature of a grid, as well as the diverse demands of applications running on the grid, makes grid scheduling a major task. Existing schedulers in wide-area heterogeneous systems require a large amount of information about the application and the grid environment to produce reasonable schedules. However, this required information may not be available, may be too expensive to collect, or may increase the runtime overhead of the scheduler such that the scheduler is rendered ineffective. We believe that no one scheduler is appropriate for all grid systems and applications. This is because while data parallel applications in which further data partitioning is possible can be further improved by efficient management of resources, smart selection of resources and load balancing can be possible, in functional/not-dividable-task parallel applications such partitioning is either not possible or difficult or expensive in term of performance. In this paper, we propose a scheduler for data parallel applications (SDPA) which offers an efficient task partitioning and load balancing strategy for data parallel applications in grid environment. The proposed SDPA offers two major features: maintaining job priority even if insufficient number of free resources is available and pre-task assignment to cut the idle time of nodes. The SDPA selects nodes smartly according to the nature of task and the nodes’ resources availability. Simulation results conducted reveal that SDPA achieves performance improvement over reported strategies in the reviewed literature in terms of execution time, throughput and waiting time.     

HD Tree: A novel data structure to support multi-dimensional range query for P2P networks

There are two basic concerns for supporting multi-dimensional range query in P2P overlay networks. The first is to preserve data locality in the process of data space partitioning, and the second is the maintenance of data locality among data ranges with an exponentially expanding and extending rate. The first problem has been well addressed by using recursive decomposition schemes, such as Quad-tree, K-d tree, Z-order, and Hilbert curve. On the other hand, the second problem has been recently identified by our novel data structure: HD Tree. In this paper, we explore how data locality can be easily maintained, and how range query can be efficiently supported in HD Tree. This is done by introducing two basic routing strategies: hierarchical routing and distributed routing. Although hierarchical routing can be applied to any two nodes in the P2P system, it generates high volume traffic toward nodes near the root, and has very limited options to cope with node failure. On the other hand, distributed routing concerns source and destination pairs only at the same depth, but traffic load is bound to some nodes at two neighboring depths, and multiple options can be found to redirect a routing request. Because HD Tree supports multiple routes between any two nodes in the P2P system, routing in HD Tree is very flexible; it can be designed for many purposes, like fault tolerance, or dynamic load balancing. Distributed routing oriented combined routing (DROCR) algorithm is one such routing strategy implemented so far. It is a hybrid algorithm combining advantages from both hierarchical routing and distributed routing. The experimental results show that DROCR algorithm achieves considerable performance gain over the equivalent tree routing at the highest depth examined. For supporting multi-dimensional range query, the experimental results indicate that the exponentially expanding and extending rate have been effectively controlled and minimized by HD Tree overlay structure and DROCR routing.     

Semi-static operator graphs for accelerated query execution on FPGAs

This paper introduces the concept of Semi-static Operator Graphs (SOG) to provide a runtime reconfigurable accelerator for query execution based on a Field Programmable Gate Array (FPGA). Instead of generating an FPGA configuration for a given arbitrary query during system runtime, we deploy a general query structure on the FPGA consisting of multiple small reconfigurable partitions (RP). During deployment of the hybrid database system, for each RP various query operators are prepared as reconfigurable modules (RM). At system runtime, the proposed approach dynamically chooses and reconfigures RMs into the RPs regarding a given query. As a result the reconfiguration overhead during system runtime is significantly reduced and enables the utilization of our hybrid architecture in real-world scenarios.     

一种无线传感器网络环境下的查询路由与负载均衡机制

针对无线传感器网络节点数量多、通信距离短、能量有限的特点,提出一种查询增益路由算法以及基于路由的负载均衡机制。查询增益路由算法通过查询增益矩阵维护路由信息,并依据历史查询成功记录来选取路由节点;而基于路由的负载均衡机制可以在查询路由过程中记录节点的能量信息,转移负载,使得查询路径中各节点的能量消耗得到均衡。仿真实验结果表明,查询增益路由算法可以在降低节点能量消耗的前提下提高查询成功率,而基于路由的负载均衡机制可以进一步降低查询增益路由算法的能量消耗。     

Experiences from integrating algorithmic and systemic load balancing strategies

Load balancing increases the efficient use of existing resources for parallel and distributed applications. At a coarse level of granularity, advances in runtime systems for parallel programs have been proposed in order to control available resources as efficiently as possible by utilizing idle resources and using task migration. Simultaneously, at a finer granularity level, advances in algorithmic strategies for dynamically balancing computational loads by data redistribution have been proposed in order to respond to variations in processor performance during the execution of a given parallel application. Combining strategies from each level of granularity can result in a system which delivers advantages of both. The resulting integration is systemic in nature and transfers the responsibility of efficient resource utilization from the application programmer to the runtime system. This paper presents the design and implementation of a system that combines an algorithmic fine-grained data parallel load balancing strategy with a systemic coarse-grained task-parallel load balancing strategy, and reports on recent experimental results of running a computationally intensive scientific application under this integrated system. The experimental results indicate that a distributed runtime environment which combines both task and data migration can provide performance advantages with little overhead. It also presents proposals for performance enhancements of the implementation, as well as future explorations for effective resource management. Copyright © 2001 John Wiley & Sons, Ltd.     

Probabilistic cost model for nearest neighbor search in image retrieval

We present a probabilistic cost model to analyze the performance of the kd-tree for nearest neighbor search in the context of content-based image retrieval. Our cost model measures the expected number of kd-tree nodes traversed during the search query. We show that our cost model has high correlations with both the observed number of traversed nodes and the runtime performance of search queries used in image retrieval. Furthermore, we prove that, if the query points follow the distribution of data used to construct the kd-trees, the median-based partitioning method as well as PCA-based partitioning technique can produce near-optimal kd-trees in terms of minimizing our cost model. The probabilistic cost model is validated through experiments in SIFT-based image retrieval.     

Declustering and load-balancing methods for parallelizinggeographic information systems

Declustering and load balancing are important issues in designing a high performance geographic information system (HPGIS), which is a central component of many interactive applications such as real time terrain visualization. The current literature provides efficient methods for declustering spatial point data. However, there has been little work toward developing efficient declustering methods for collections of extended objects, like chains of line segments and polygons. We focus on the data partitioning approach to parallelizing GIS operations. We provide a framework for declustering collections of extended spatial objects by identifying the following key issues: (1) work load metric; (2) spatial extent of the work load; (3) distribution of the work load over the spatial extent; and (4) declustering method. We identify and experimentally evaluate alternatives for each of these issues. In addition, we also provide a framework for dynamically balancing the load between different processors. We experimentally evaluate the proposed declustering and load balancing methods on a distributed memory MIMD machine (Cray T3D). Experimental results show that the spatial extent and the work load metric are important issues in developing a declustering method. Experiments also show that the replication of data is usually needed to facilitate dynamic load balancing, since the cost of local processing is often less than the cost of data transfer for extended spatial objects. In addition, we also show that the effectiveness of dynamic load balancing techniques can be improved by using declustering methods to determine the subsets of spatial objects to be transferred during runtime     

An adaptive threshold based hybrid load balancing scheme with sender and receiver initiated approach using random information exchange

The primary objective of load balancing for distributed systems is to minimize the job execution time while maximizing the resource utilization. Load balancing on decentralized systems need effective information exchange policy so that with minimum amount of communication the nodes have up to date information about other nodes in the system. Periodic, event‐based and on‐demand information exchange are some important policies used for the same. All these approaches involve a lot of overhead and even sometime leading toward obsolete data with the nodes if there is a delay in the updation. This work presents an adaptive threshold‐based hybrid load balancing scheme with sender and receiver initiated approach (HLBWSR) using random information exchange (RIE). RIE ensures that the information is exchanged in such a way that each node in the system has up‐to‐date state of the other nodes with much reduced communication overhead. Further, the adaptive threshold ensures that almost an average numbers of jobs are executed by all the nodes in the system. The study of the effect of the use of RIE on sender initiated, receiver initiated and hybrid of sender and receiver initiated load balancing approach establishes the superior performance of HLBWSR among its RIE‐based peers. A comparative analysis of HLBWSR, with periodic information exchange strategy, modified estimated load information scheduling algorithm and load balancing on arrival reveals its effectiveness under various test conditions. Copyright © 2016 John Wiley & Sons, Ltd.     

基于SkipNet支持多属性范围查询的云资源共享设计

在云资源共享服务模式中,针对云资源多属性范围查询的问题,提出一种改进的E-SkipNet网络。首先,E-SkipNet在传统分布式哈希表(DHT)网络SkipNet的基础上将数据属性引入到节点NameID的设置中,将物理节点加入到单个属性域中,以支持多属性范围查询;其次,在原E-SkipNet网络的基础上,将物理节点同时映射成多个逻辑节点;同时加入多个属性域,并将资源按照不同的属性发布到不同逻辑节点上;最后,采用均匀位置保留哈希函数对资源进行映射存储,从而在各个属性域中保留属性值的顺序关系,从而支持范围查询。仿真结果表明,改进后的E-SkipNet网络与改进前的E-SkipNet和多属性可寻址网络(MAAN)相比,在路由效率方面分别提高了18.09%和20.47%。结果表明,改进后的E-SkipNet网络能支持更加高效的云资源多属性范围查询,在异构环境中能较好地实现负载均衡。     

一种基于Chord优化的空间数据存储方法

针对空间数据访问的局部性和位置相关性,在Chord协议的基础上进行扩展,提出了一种基于P2P的分布式空间数据存储方法。该方法对存储节点进行分组,并可根据节点的负载状态对分组进行动态调整,以保持系统的负载均衡。理论分析及仿真实验都显示该方法在执行空间范围查询操作时较Chord协议具有更高的效率。