KMP模式匹配算法探讨

介绍了KMP算法并与朴素查找算法进行了比较,提出了前缀函数的概念,并利用改进的前缀函数改进KMP算法,最后结合KMP的改进算法给出了多次匹配的算法。     

一种基于WLAN指纹识别的无线定位中提高定位精度的方法

提出了一种在WLAN环境下利用RSSI值和距离之间关系进行定位的改进方法。提出了改进算法的基本流程,并对改进算法本身的特点进行了理论分析。分析表明该改进算法可以提高定位精度。     

基于改进的多目标进化算法的飞行控制系统优化*

针对在传统飞行控制系统控制器参数整定问题中单目标优化不能同时满足多个控制指标要求的缺点,提出了一种基于改进的NSGAII算法的多目标进化算法。在改进的NSGAII算法中,提出了改进的精英保留策略增强了算法收敛性;同时,使用改进的自适应模拟二进制（ASBX）算子提高了算法效率,提出使用改进的基于混沌序列的变异算子避免算法陷入局部最优解,以提高算法搜索精度。将改进的算法应用到飞机飞行控制系统设计中,仿真结果表明,该进化算法能够快速有效地进行飞行控制系统参数整定。     

k中心点聚类算法在层次数据的应用

探讨了近年来提出的聚类概念与聚类过程、k中心点聚类的算法,在此基础上提出了一种基于层次数据模型的k中心聚类的改进算法.该算法一方面针对层次变量提出了相关的中值点概念;另一方面对传统k中心点算法进行了改进.最后对改进算法的复杂度进行了分析,由分析结果得出改进算法要比传统k中心点算法每次迭代耗费时间略少,但在总耗费时间上远远小于k中心点算法,大幅度提高了算法的整体性能.     

一种Elgamal算法的改进算法

本文提出了Elgamal加密算法的一种改进算法,这种改进算法的特点是可以实现成泉序列密码一样逐比特地进行加解密,被加密消息可以任意比特长;本文还通过改进算法和原Elgamal算法的对比分析,讨论了改进算法的安全性,加解密速度、密文扩展、消息块长度的灵活性,同时对Elgamal加密算法及改进算法的随机数的位数对安全性的影响作了讨论,提出了算法中对随机数选取的安全性要求。     

基于REESSE3+算法的改进算法

REESSE3+算法是苏盛辉教授于2014年提出的一个8轮迭代的分组密码算法。本文在REESSE3+算法的基础上做出了一些改进,提出了一种新的改进算法。由于REESSE3+算法受到了来学嘉教授提出的IDEA算法的启发,采用了3个不相容的群运算来保证其安全性,因此采用来学嘉教授提出的马尔科夫密码模型来对REESSE3+(16)算法和16位输入的改进算法进行比较。通过实验发现,在面对差分攻击时,16位输入的改进算法比原REESSE3+(16)算法更加安全。     

典型Apriori改进算法的分析与比较

Apriori算法是一种最有影响的挖掘布尔关联规则频繁项集的算法。为了提高算法的效率,国内外数据挖掘领域的专家、提出了一系列的改进策略。文章首先分析了Apriori算法及当前主要的改进算法,其次比较了这些算法的改进策略及其局限性,最后在此基础上提出了Apriori算法的未来研究方向。     

基于项的协同过滤在推荐系统中的应用研究

分析基于项的协同过滤在推荐系统中应用及所存在的问题,提出了一个基于项的协同过滤改进算法,并给出了改进算法在标准数据集上的实验结果,对改进算法与原算法进行了相关性能的比较分析,证明了改进算法的有效性.最后,对研究进行了总结,指出存在的不足,提出了进一步研究的方向.     

基于蚁群算法的智能交通最优路径探究

对于汽车智能交通的最佳路线问题,业界学者提出了蚁群概念和算法,其具有实时规划的效能。基于此,笔者提出了改进蚁群算法,借助智能交通模型比较传统算法和改进算法的性能。通过试验发现,改进蚁群优化算法能够有效解决路径问题。     

一种改进的随机抽样算法

文章提出了一种改进的随机抽样算法,对其时间和空间复杂性进行了分析,结果表明改进的随机抽样算法总体性能优于现有随机抽样算法,最后,给出了改进算法在等距抽样中的应用.     

Synchronised execution on shared memory multiprocessors

Threads provides a mechanism for simulating the execution of parallel algorithms on a simplified model of a shared-memory multiprocessor. The algorithms can be expressed in a high-level block-structured language, which supports multiple threads of execution within a common body of program code. Results show an ability to achieve good speedup for small problems using algorithms derived by simple modifications of sequential algorithms. As well, a sibling thread synchronisation feature provides the basis for the synchronous execution of threads. k-parallel algorithms tailored to the machine size and implemented as synchronously executing iterations, can provide near linear speedup as the problem size is increased. The techniques described in this paper seem to promise an effective synchronous execution mode for shared-memory MIMD architectures.     

Strategies for Rescheduling Tightly-Coupled Parallel Applications in Multi-Cluster Grids

As computational Grids are increasingly used for executing long running multi-phase parallel applications, it is important to develop efficient rescheduling frameworks that adapt application execution in response to resource and application dynamics. In this paper, three strategies or algorithms have been developed for deciding when and where to reschedule parallel applications that execute on multi-cluster Grids. The algorithms derive rescheduling plans that consist of potential points in application execution for rescheduling and schedules of resources for application execution between two consecutive rescheduling points. Using large number of simulations, it is shown that the rescheduling plans developed by the algorithms can lead to large decrease in application execution times when compared to executions without rescheduling on dynamic Grid resources. The rescheduling plans generated by the algorithms are also shown to be competitive when compared to the near-optimal plans generated by brute-force methods. Of the algorithms, genetic algorithm yielded the most efficient rescheduling plans with 9–12% smaller average execution times than the other algorithms.     

Some Analysis Techniques for Asynchronous Multiprocessor Algorithms

Efficient algorithms for asynchronous multiprocessor systems must achieve a balance between low process communication and high adaptability to variations in process speed. Algorithms that employ problem decomposition may be classified as static (in which decomposition takes place before execution) and dynamic (in which decomposition takes place during execution). Static and dynamic algorithms are particularly suited for low process communication and high adaptability, respectively. For static algorithms the following analysis techniques are presented: finding the probability distribution of execution time, deriving bounds on mean execution time using order statistics, finding asymptotic mean speedup, and using approximations. For dynamic algorithms the technique of modeling using a queueing system is presented. For each technique, an example application to parallel sorting is given.     

数据流上的复合滑动窗口聚集算法

基于滑动窗口的聚集查询是数据流研究领域的一个热点问题。在已有的研究工作中,聚集算法都是针对立即执行的连续查询提出的,这些算法均是当数据流新到一个元组立即计算一次聚集结果。而在实际应用中,连续查询有时采取的是周期执行方式。论文针对周期执行的连续查询提出了复合滑动窗口聚集算法,即数据流新到一个元组,将它插入到基本窗口中,当基本窗口被插满时计算一次聚集结果。给出了非增量式和增量式两种算法。理论分析和实验结果表明增量式算法具有较好的性能。     

ElasticSim: A Toolkit for Simulating Workflows with Cloud Resource Runtime Auto-Scaling and Stochastic Task Execution Times

Resource provisioning and scheduling are crucial for cloud workflow applications. Simulation is one of the most promising evaluation methods for different resource provisioning and scheduling algorithms. However, existing simulators for Cloud workflow applications fail to provide support for resource runtime auto-scaling and stochastic task execution time modeling. In this paper, a workflow simulator ElasticSim is introduced, which is an extension of the popular used CloudSim simulator by adding support for resource runtime auto-scaling and stochastic task execution time modeling. Most of existing workflow scheduling algorithms are static and are based on deterministic task execution times. By the aid of ElasticSim, the practical performance of existing static algorithms, when they are put into practice with stochastic task execution times, is evaluated. Experimental results show that about 2.8 % to 20 % additional resource rental cost is incurred for different cases and workflow deadlines are violated for most cases because of stochastic task execution times. Therefore, ElasticSim is a promising platform for evaluating the practical performance of workflow resource provisioning and scheduling algorithms, which supports resource runtime auto-scaling and stochastic task execution time modeling.     

矩阵乘法在斐波那契数列计算中的应用

本文介绍了斐波那契数列的一些算法思路,对递归算法、自底向上、比内公式等算法的时间复杂度进行了分 析,给出了利用矩阵乘法升维计算降低时间复杂度的方法,对比测试了各算法实现在不同计算量下的执行时间。针对数据溢 出,将long数据类型改进为BigInteger的数据类型,给出了大数计算下的执行时间对比。     

A graph-theoretic model for optimizing queries involving methods

Traditional algorithms for optimizing the execution order of joins are no more valid when selections and projections involve methods and become very expensive operations. Selections and projections could be even more costly than joins such that they are pulled above joins, rather than pushed down in a query tree. In this paper, we take a fundamental look at how to approach query optimization from a top-down design perspective, rather than trying to force one model to fit into another. We present a graph model which is designed to characterize execution plans. Each edge and each vertex of the graph is assigned a weight to model execution plans. We also design algorithms that use these weights to optimize the execution order of operations. A cost model of these algorithms is developed. Experiments are conducted on the basis of this cost model. The results show that our algorithms are superior to similar work proposed in the literature. Received 20 April 1999 / Accepted 9 August 2000 Published online 20 April 2001     

基于Shared-Nothing的并行Hash连接算法效率分析

该文研究了基于Shared-Nothing结构的几种常用并行连接算法,分析了影响查询响应时间的各种因素.在此基础上,以多种硬件成分作为参数建立一个代价分析模型.使用该模型计算并行Hash算法在每个处理机上的平均任务执行时间和总的查询响应时间,并比较了几种算法在不同硬件配置下的执行效率.所提出的模型和分析方法为评价和选取并行连接算法提供了一种可行的途径.     

Toward High-Performance Delta-Based Iterative Processing with a Group-Based Approach

Many systems have been built to employ the delta-based iterative execution model to support iterative algorithms on distributed platforms by exploiting the sparse computational dependencies between data items of these iterative algorithms in a synchronous or asynchronous approach. However, for large-scale iterative algorithms, existing synchronous solutions suffer from slow convergence speed and load imbalance, because of the strict barrier between iterations; while existing asynchronous approaches induce excessive redundant communication and computation cost as a result of being barrier-free. In view of the performance trade-off between these two approaches, this paper designs an efficient execution manager, called Aiter-R, which can be integrated into existing delta-based iterative processing systems to efficiently support the execution of delta-based iterative algorithms, by using our proposed group-based iterative execution approach. It can efficiently and correctly explore the middle ground of the two extremes. A heuristic scheduling algorithm is further proposed to allow an iterative algorithm to adaptively choose its trade-off point so as to achieve the maximum efficiency. Experimental results show that Aiter-R strikes a good balance between the synchronous and asynchronous policies and outperforms state-of-the-art solutions. It reduces the execution time by up to 54.1% and 84.6% in comparison with existing asynchronous and the synchronous models, respectively.

     

Pro-active failure handling mechanisms for scheduling in grid computing environments

In this paper, we consider designing pro-active failure handling strategies for grid environments. These strategies estimate the availability of resources in the Grid, and also preemptively calculate the expected long term capacity of the Grid. Using these strategies, we create modified versions of the backfill and replication algorithms to include all three pro-active strategies to ascertain each of their effectiveness in the prevention of job failures during execution. Also, we extend our earlier work on a co-ordinate based allocation strategy. The extended algorithm also shows continual improvement when operating under the same execution environment. In our experiments, we compare these enhanced algorithms to their original forms, and show that pro-active failure handling is able to, in some cases, avoid all job failures during execution. Also, we show that NSA provides the best balance of enhanced throughput and job failures during execution of the algorithms we have considered.