基于蚁群算法的非结构化P2P搜索机制的研究

通过对P2P中资源搜索技术的研究,针对非结构化P2P网络中的传统洪泛搜索机制中的路由盲目性和产生过多冗余消息等缺点,提出一种基于蚁群算法的非结构化P2P搜索机制.蚁群算法是一种新型的优化算法,利用蚁群算法信息素的正反馈机制指导查询请求消息的转发,将查询请求消息尽量发往资源可能存在的节点上.实验结果表明,该算法在查询成功率和查询消息的传送次数方面都优于洪泛搜索算法,提高了算法的有效性.     

基于蚁群算法的多连接查询优化方法

介绍蚁群算法在多连接查询优化中的应用,在介绍蚁群算法的基本原理和工作流程的基础上,提出一种利用蚁群算法进行数据库多连接查询优化的方法,并建立基于蚁群算法的多连接查询优化模型。理论分析与试验结果表明,用蚁群算法解决多连接查询优化问题取得了满意的效果。     

基于MapReduce的Skyline查询处理算法

Skyline查询是一个典型的多目标优化查询,在多目标优化、数据挖掘等领域有着广泛的应用。现有的Skyline查询处理算法大都假定数据集存放在单一数据库服务器中,查询处理算法通常也被设计成针对单一服务器的串行算法。随着数据量的急剧增长,特别是在大数据背景下,传统的基于单机的串行Skyline算法已经远远不能满足用户的需求。基于流行的分布式并行编程框架MapReduce,研究了适用于大数据集的并行Skyline查询算法。针对影响MapReduce计算的因素,对现有基于角度的划分策略进行了改进,提出了Balanced Angular划分策略;同时,为了减少Reduce过程的计算量,提出了在Map端预先进行数据过滤的策略。实验结果显示所提出的Skyline查询算法能显著提升系统性能。     

On-Demand数据广播环境下实时有序查询处理

在On-Demand数据广播环境下,广播服务器基于用户发送的数据请求等信息进行调度决策来满足用户的数据访问需求。在很多实际应用中,用户的数据请求需要在一定时间段内得到满足,即数据请求是有截止期的。现有研究只考虑了具有截止期约束的单个数据请求的调度问题,而实时查询处理即用户以查询为单位依次发送多个数据请求的研究尚未得到足够的关注。本文重点研究了On-Demand数据广播环境下如何有效地处理实时有序查询这一问题。基于对该问题的分析,定义了一类新的调度问题ROBS并证明了ROBS的Off-Line版本是NP-Hard的;提出了一种新的考虑查询语义的On-Line调度算法OL-ROBS,该算法通过综合考虑数据请求个数、查询截止期和查询剩余数据请求个数来确定待广播数据项的优先级;为提高OL-ROBS的执行效率,设计了一种裁减算法,用以减少调度决策的搜索空间。模拟实验将OL-ROBS与目前最为有效的实时数据请求调度算法Sinθ-进行了比较,结果显示OL-ROBS具有更低的错过截止期比率。     

面向汽车产业链协同平台的配件库存信息动态集成技术

为实现对汽车产业链协同平台服务商、二级中心库售后配件库存信息的动态集成和实时查询,构建了面向协同平台的配件库存信息动态集成模型,提出了查询事件和协作关系驱动的信息动态集成算法。研究了分布式数据源并行查询优化技术、多源异构数据转换处理技术、基于API变化捕获的数据库同步方法,实现了对各服务商、二级中心库实时配件库存信息的Web封装及动态调用。提出的模型与算法在汽车产业链协同平台上实现了应用验证。     

面向物联网的分布式上下文敏感复杂事件处理方法

物联网产生的数据具有大数据特征,而这些数据难以用现有数据处理技术进行有效处理.作为物联网中间件的核心技术,复杂事件处理技术具备大数据的海量、复杂性等特征和实时处理的需求.上下文敏感是复杂事件处理引擎的重要特征.提出一种高效的面向物联网的分布式上下文敏感复杂事件处理架构和方法.该方法使用模糊本体进行上下文建模,以支持事件的不确定性及模糊事件查询问题.以基于模糊本体的查询和基于相似性的分布式推理为基础,生成复杂事件查询规划,并通过查询重写,把上下文相关查询转换为上下文无关子查询.根据不同的事件模型和上下文划分数据,并通过优化和多级并行来提高性能.实验结果表明该方法能够处理模糊事件上下文,对于面向物联网的分布式上下文敏感复杂事件处理具有比一般方法更好的性能和可伸缩性.     

求解多维背包问题的MapReduce蚁群优化算法

应用MapReduce编程模式实现蚁群优化算法的并行化计算,提出基于MapReduce的改进背包问题蚁群算法.通过改进概率计算时机、轮盘赌、交叉、变异等技术,降低蚁群算法的计算复杂度.在云计算环境中应用该算法分布式并行地求解大规模多维背包问题,仿真实验结果表明,该算法能改善蚁群算法搜索时间长的缺陷,增强对大规模问题的处理能力.     

分布式数据库查询优化处理——基于关系代数等价变换的查询优化处理

在进行分布式数据库应用时，快速而准确的得到查询结果一直是分布式数据库得以应用的关键问题。本文阐述了分布式查询优化的一种策略和算法——基于关系代数等价变换的查询优化处理。     

一种减少长尾延迟的分布式实时约束传播方法

提出了一种在数据中心环境下用于减少长尾延迟的分布式实时约束传播方法,该方法能够使当前节点感知请求的全局响应时间约束信息,并能够将请求的实时约束信息传播到整个处理路径;节点可以利用请求的实时约束信息进行请求调度或加速请求执行时间,以此来减少长尾延迟现象.同时,针对划分/聚合模式和串行/依赖模式2种数据中心应用,提出了阶段服务模型和并行单元模型,并基于这2种模型实现了分布式实时约束传播框架.最后,在分布式实时约束传播框架上实现了实时约束感知调度算法,通过实验进行了简单的验证,初步的实验结果显示了分布式实时约束传播方法能够在一定程度上减少长尾延迟.     

分布式数据库系统中的查询优化处理

分布式查询处理是用户与分布式数据库的接口,也是分布式数据库研究的主要问题之一。在分布式查询处理中基于不同的目标有不同的查询优化算法,文章主要讨论基于最小传输代价原则的半连接算法,分析了半连接算法的原理并给出了多关系半连接查询优化算法思考。     

Efficient Processing of Distributed Twig Queries Based on Node Distribution

Massive XML data are increasingly generated for the representation, storage and exchange of web information. Twig query processing over massive XML data has become a research focus. However, most traditional algorithms cannot be directly implemented in a distributed manner. Some of the existing distributed algorithms generate a lot of useless intermediate results and execute many join operations of partial results in most cases; others require the priori knowledge of query pattern before XML partition, storage and query processing, which is impractical in the cases of large-scale data or frequent incoming new queries. To improve efficiency and scalability, in this paper, we propose a 3-phase distributed algorithm DisT3 based on node distribution mechanism to avoid unnecessary intermediate results. Furthermore, we propose a lightweight local index ReP with an enhanced XML partitioning approach using arbitrary partitioning strategy, and based on ReP we propose an improved 2-phase distributed algorithm DisT2ReP to further reduce the communication cost. After the performance guarantees are analyzed, extensive experiments are conducted to verify the efficiency and scalability of our proposed algorithms in distributed twig query applications.     

Swarm Intelligence Approaches for Grid Load Balancing

With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. The huge amount of computations a Grid can fulfill in a specific amount of time cannot be performed by the best supercomputers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized optimally using a good load balancing algorithm. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One algorithm is based on ant colony optimization and the other algorithm is based on particle swarm optimization. A simulation of the proposed approaches using a Grid simulation toolkit (GridSim) is conducted. The performance of the algorithms are evaluated using performance criteria such as makespan and load balancing level. A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches is provided. Experimental results show the proposed algorithms perform very well in a Grid environment. Especially the application of particle swarm optimization, can yield better performance results in many scenarios than the ant colony approach.     

基于量子蚁群算法的数据库连接查询优化

连接查询优化是提高数据库性能的关键技术,针对数据库连接查询优化效率低的难题,提出一种量子蚁群算法的数据库连接查询优化方法（QACA）.首先,将数据库连接查询计划左深树看作一个蚂蚁,然后,利用量子旋转门更新各路径信息素,并利用混沌变异策略保持种群多样性,通过蚂蚁之间的信息交流找到数据库连接查询最优计划,最后,进行数据库连接查询优化实例分析.结果表明,QACA是解决数据库连接查询优化的有效途径,获得理想的数据库连接查询计划,具有实际意义.     

A multi-colony ant algorithm for optimizing join queries in distributed database systems

Distributed database systems provide a new data processing and storage technology for decentralized organizations of today. Query optimization, the process to generate an optimal execution plan for the posed query, is more challenging in such systems due to the huge search space of alternative plans incurred by distribution. As finding an optimal execution plan is computationally intractable, using stochastic-based algorithms has drawn the attention of most researchers. In this paper, for the first time, a multi-colony ant algorithm is proposed for optimizing join queries in a distributed environment where relations can be replicated but not fragmented. In the proposed algorithm, four types of ants collaborate to create an execution plan. Hence, there are four ant colonies in each iteration. Each type of ant makes an important decision to find the optimal plan. In order to evaluate the quality of the generated plan, two cost models are used—one based on the total time and the other on the response time. The proposed algorithm is compared with two previous genetic-based algorithms on chain, tree and cyclic queries. The experimental results show that the proposed algorithm saves up to about 80 % of optimization time with no significant difference in the quality of generated plans compared with the best existing genetic-based algorithm.     

一种改进的分布式查询优化算法

为了提高分布式查询优化算法的性能,在遗传模拟退火混合算法中融入小生境技术,并对混合算法的相应要素进行改进,基于该混合算法,提出了一种改进的分布式查询优化算法。利用小生境技术扩展遗传模拟退火混合算法的探索区域,防止早熟现象发生,简化算法中的Meteopolis规则,以消除混合算法中引入新技术后产生的功能冗余,将混合算法应用到分布式查询优化算法中。实验结果表明,改进的分布式查询优化算法可以稳定地得到最优解,减少分布式数据库查询的代价,提高查询效率。     

Joint Algorithm of Message Fragmentation and No-Wait Scheduling for Time-Sensitive Networks

Time-sensitive networks(TSNs)support not only traditional best-effort communications but also deterministic communications,which send each packet at a deterministic time so that the data transmissions of networked control systems can be precisely scheduled to guarantee hard real-time constraints.No-wait scheduling is suitable for such TSNs and generates the schedules of deterministic communications with the minimal network resources so that all of the remaining resources can be used to improve the throughput of best-effort communications.However,due to inappropriate message fragmentation,the realtime performance of no-wait scheduling algorithms is reduced.Therefore,in this paper,joint algorithms of message fragmentation and no-wait scheduling are proposed.First,a specification for the joint problem based on optimization modulo theories is proposed so that off-the-shelf solvers can be used to find optimal solutions.Second,to improve the scalability of our algorithm,the worst-case delay of messages is analyzed,and then,based on the analysis,a heuristic algorithm is proposed to construct low-delay schedules.Finally,we conduct extensive test cases to evaluate our proposed algorithms.The evaluation results indicate that,compared to existing algorithms,the proposed joint algorithm improves schedulability by up to 50%.     

基于蚁群优化的极限学习机选择性集成学习算法

针对现有极限学习机集成学习算法分类精度低、泛化能力差等缺点,提出了一种基于蚁群优化思想的极限学习机选择性集成学习算法。该算法首先通过随机分配隐层输入权重和偏置的方法生成大量差异的极限学习机分类器,然后利用一个二叉蚁群优化搜索算法迭代地搜寻最优分类器组合,最终使用该组合分类测试样本。通过12个标准数据集对该算法进行了测试,该算法在9个数据集上获得了最优结果,在另3个数据集上获得了次优结果。采用该算法可显著提高分类精度与泛化性能。     

A path selection-based algorithm for real-time data staging in Grid applications

Efficient data scheduling is becoming an important issue in distributed real-time applications that produce huge data sets. The Grid environment on which these applications may run seeks to harness the geographically distributed resources for the applications. Scheduling components should account for real-time measures of the applications and reduce communication overhead due to enormous data size experienced, especially in dissemination applications. In this study, we consider the data staging scheme to provide the dissemination of large-scale data sets for the distributed real-time applications. We propose a new path selection-based algorithm for optimizing a criterion that reflects the general satisfiability of the system. The algorithm adopts a blocking-time analysis method combined with a simple heuristic to explore the most likely regions of a search space. Two heuristics are provided for the algorithm to explore these regions of the search space. Simulation results show that the proposed algorithm together with either of the heuristic has higher performance compared to other algorithms in the literature. We also show by simulation that a new optimization criterion we proposed in this study is successful in improving the performance of the individual applications.     

混合蚁群算法求解分布式系统任务分配问题

任务分配问题是被公认的NP-hard问题,应用广泛。在对分布式系统任务分配问题进行分析的基础上,将蚂蚁寻求任务分配方案的过程用一种新的图形表示方式来实现。针对蚁群优化算法易陷入局部最优的固有缺陷,提出了一种新的混合算法,该算法将蚁群优化算法与简单禁忌搜索算法相结合,增强了算法的局部搜索能力,提高了任务分配问题解的质量。实验结果表明混合算法的求解性能较优。