谈局域网的性能优化

在局域网运行和维护的过程中,网络管理员不仅要确保网络能够提供正常的服务,还要采取各种措施保证网络能够可靠、安全、高效地运行,从整个网络的角度提高网络的性能。     

常用内排序算法的分析与比较

该文在对常用内排序算法基本思想分析的基础上,从算法的稳定性;算法在最好情况下、最坏情况下的交换次数和移动次数;算法的时间复杂度等方面进行了详细的比较分析。     

探讨数据库加密技术

从数据库安全需求着手,提出数据库加密要实现的目标,分析了数据库加密技术中几项关键技术并进行比较,最后对加密后系统产生的影响进行总结。     

Expansion of a matrix in terms of external products of configuration vectors

The paper proposes a neural-net iterative algorithm that allows us to represent any random symmetrical N×N matrix as a weighted Hebbian series of configuration vectors with a given accuracy. The iterative algorithm is shown to demonstrate the fastest convergence when the vectors of expansion are stable nods of the N-dimensional space corresponding to the extremums of the neural-net energy functional. It so proves that all conclusions about neural networks and optimization algorithms that are based on Hebbian matrices are true for any other type of matrix. The text was submitted by the author in English.     

知识获取技术及其在网络智能故障诊断中的应用

基于遗传算法提出了一种新型的机器学习方法,它利用遗传算法的全局寻优手段,为解决知识获取这一瓶颈问题另辟捷径。论述了该方法的原理及其实现过程,并且给出了网络故障诊断应用实例,证明了它的有效性。     

The election problem in asynchronous distributed systems with bounded faulty processes

Determining the “weakest” failure detectors is a central topic in solving many agreement problems such as Consensus, Non-Blocking Atomic Commit and Election in asynchronous distributed systems. So far, this has been studied extensively for several such fundamental problems. It is stated that Perfect Failure Detector P is the weakest failure detector to solve the Election problem with any number of faulty processes. In this paper, we introduce Modal failure detector M and show that to solve Election, M is the weakest failure detector to solve election when the number of faulty processes is less than ⌈n/2⌉. We also show that it is strictly weaker than P.

Adaptive channel allocation for large-scale streaming content delivery systems

Scheduling algorithms are used in content delivery systems to control the resource allocation rate. They not only improve system efficiency, but also increase user satisfaction. Lower renege rate and less waiting time for users are the main goals for a scheduling algorithm. Among existing algorithms, On-Demand strategy does not perform well, while rate control channel allocation policies performs much better. Pure-Rate-Control (PRC) and Multiple-Service-Class (MSC) belong to the rate control algorithms. MSC performs well, but a drawback is that it uses the Hot Index, which is hard to decide and has significant effects on the performance. In order to solve this problem and to improve the overall system performance, two new algorithms, Modified MSC(MMSC) and Adaptive Algorithm (AA), are proposed in this paper. Both of them solved the problem of MSC very well and improved the overall performance. For example, the renege rate of AA is about 5.4% less than that of MMSC, and about 9.8% less than that of MSC.

An LMI approach to constrained optimization with homogeneous forms

This paper considers the problem of determining the minimum Euclidean distance of a point from a polynomial surface in . It is well known that this problem is in general non-convex. The main purpose of the paper is to investigate to what extent linear matrix inequality (LMI) techniques can be exploited for solving this problem. The first result of the paper shows that a lower bound to the global minimum can be achieved via the solution of a one-parameter family of linear matrix inequalities (LMIs). It is also pointed out that for some classes of problems the solution of a single LMI problem provides the lower bound. The second result concerns the tightness of the bound. It is shown that optimality of the lower bound amounts to solving a system of linear equations. An application example is finally presented to show the features of the approach.     

Parallel Implementation of a Class of Adaptive Signal Processing Applications

Recently, High Performance Computing (HPC) platforms have been employed to realize many computationally demanding applications in signal and image processing. These applications require real-time performance constraints to be met. These constraints include latency as well as throughput. In order to meet these performance requirements, efficient parallel algorithms are needed. These algorithms must be engineered to exploit the computational characteristics of such applications. In this paper we present a methodology for mapping a class of adaptive signal processing applications onto HPC platforms such that the throughput performance is optimized. We first define a new task model using the salient computational characteristics of a class of adaptive signal processing applications. Based on this task model, we propose a new execution model. In the earlier linear pipelined execution model, the task mapping choices were restricted. The new model permits flexible task mapping choices, leading to improved throughput performance compared with the previous model. Using the new model, a three-step task mapping methodology is developed. It consists of (1) a data remapping step, (2) a coarse resource allocation step, and (3) a fine performance tuning step. The methodology is demonstrated by designing parallel algorithms for modern radar and sonar signal processing applications. These are implemented on IBM SP2 and Cray T3E, state-of-the-art HPC platforms, to show the effectiveness of our approach. Experimental results show significant performance improvement over those obtained by previous approaches. Our code is written using C and the Message Passing Interface (MPI). Thus, it is portable across various HPC platforms. Received April 8, 1998; revised February 2, 1999.     

On the analysis of stochastic divide and conquer algorithms

This paper develops general tools for the analysis of stochastic divide and conquer algorithms. We concentrate on the average performance and the distribution of the running time of the algorithm. As a special example we analyse the average performance and the running time distribution of the (2k + 1)-median version of Quicksort. Online publication October 13, 2000.