基于移动Agent的分布式路由算法研究

移动Agent技术能较好地适应Internet分布式的特点，将其引入网络分布式路由计算领域是对Internet路由系统的革新。本文指出了传统路由算法存在的问题，提出基于移动Agent的分布式路由算法，就路由表的初始化、通信网络中Agent的数量控制、网络链路故障后路由表的更新、后向Agent的消亡等方面进行了讨论，并进行了仿真实验和性能分析。     

基于强化学习的多移动Agent学习算法

结合强化学习技术讨论了单移动Agent学习的过程,然后扩展到多移动Agent学习领域,提出一个多移动Agent学习算法MMAL(MultiMobileAgentLearning)。算法充分考虑了移动Agent学习的特点,使得移动Agent能够在不确定和有冲突目标的上下文中进行决策,解决在学习过程中Agent对移动时机的选择,并且能够大大降低计算代价。目的是使Agent能在随机动态的环境中进行自主、协作的学习。最后,通过仿真试验表明这种学习算法是一种高效、快速的学习方法。     

对抗环境下多Agent协商问题的研究

利用多Agent系统具有的自治性和实时反应性,探讨对抗环境下的多Agent协商决策问题,提出一种混合式的多Agent结构协商模型,给出以最大团队效益为前提的协商求解策略和协商角色交换算法。通过协商,对抗环境中的Agent成员能够很好地进行动作策略选择和移动,能更好地进行进攻和防守。仿真实验验证了算法的可行性和有效性,结果表明其在一定程度上解决了多Agent系统中实时动态和受限通信对抗环境下的多Agent决策与合作问题。     

一种基于移动Agent的网格资源发现方法

提出了一种基于移动Agent的非集中式网格资源发现方法,通过Agent共享服务请求环境中重叠部分的信息,减少整个信息索存储空间的数量,从而提高了查找效率。Agent并行处理查找任务,可扩展性提高了。同时提出了一种基于CE算法的资源查找算法ApRD,该算法是完全分布式的,由移动Agent来执行。实验结果表明该方法可获得较满意的资源定位性能,能适应网格资源的分布性、动态参与和良好的可扩展性。     

一种基于移动Agent的动态计算资源模型的研究

计算资源的动态部署为分布计算提供灵活、高效的解决方案，针对移动环境，当前的一些研究工作还存在局限性．以移动Agent技术为基础，考虑到移动环境的动态性、不确定性，提出了动态计算资源部署模型及自主性能优化算法，较好地解决了移动环境的资源部署和性能优化问题．结果表明其方法是通用的、可行的、有效的．     

基于移动Agent的集群企业资源管理技术的实现

针对分布计算环境下企业资源管理中的诸多问题，提出基于移动Agent的集群企业资源的全局整合与动态分配方法和机制，给出了系统的体系结构。提出了基于资源类的资源整合模式和资源类的信息模型，构建了企业集群资源的分级整合。提出了基于移动Agent寻优和基于市场原则的资源分配调度算法。最后给出了S2-ARIMS应用系统。     

动态网络环境下的多Agent移动合作系统

多Agent移动合作系统为分布式系统提供了灵活、高效的解决方案,但针对动态网络环境,当前的一些研究工作还存在局限性,以多Agent移动合作系统模型为基础,考虑到动态环境中Agent知识的不确定性、影响力和Agent间可能客观存在的矛盾,提出了Agent模糊知识表达与交换的思想、理论模型及算法,较好地考虑并解决了Agent的模糊信念合成及矛盾处理等关键问题,通过仿真研究,将所提出的理论与模型应用于解决动态网络拓扑发现问题,结果证明了该模型是可行、高效的。     

移动计算环境下支持移动节点迁移的故障检测算法

与有线的固定环境相比,移动环境中的无线网络连接更加脆弱,移动主机可靠性更差,为提高移动计算系统的容错性能,研究并提出一个移动计算环境下支持移动节点迁移的故障检测算法.在基于互测比较和概率诊断的区内故障感知算法基础上,针对移动节点自由迁移导致的区内故障感知算法中移动主机(MH)漏检或故障恢复不彻底问题,引入条件检查和故障信息发布机制,提出支持移动节点迁移的移动计算环境下的故障检测算法.仿真实验表明:算法满足故障检测要求,能够适应动态的移动计算网络环境.     

基于移动agent的信息协查业务仿真模型研究

根据移动Agent在分布式数据查询中的优势，该文提出了一个基于移动代理的分布式信息协作查询仿真模型，有效的解决了服务端和节点端因为频繁的交互产生的带宽浪费问题，减少了系统资源的消耗，同时通过一个实时监控Agent模块提高了系统运行的安全性。     

基于移动Agent的分布式数据挖掘平台的设计与实现

结合移动Agent应用开发方法,设计一个基于移动Agent的分布式计算应用模型.针对分布式数据计算现有方法,分析其不足,通过研究移动Agent体系结构及关键技术,从理论上探索将Agent技术与分布式数据挖掘相结合的可行性和技术优势,采用Agent技术解决分布式数据挖掘时所遇到的问题,全面、系统提出了解决方案.在此基础上,实现了一个Intranet环境下的基于移动Agent的分布式计算平台.     

Dynamic partner identification in mobile agent-based distributed job workflow execution

This paper is concerned with the design, implementation, and evaluation of algorithms for communication partner identification in mobile agent-based distributed job workflow execution. We first describe a framework for distributed job workflow execution over the Grid: the Mobile Code Collaboration Framework (MCCF). Based on the study of agent communications during a job workflow execution on MCCF, we identify the unnecessary agent communications that degrade the system performance. Then, we design a novel subjob grouping algorithm for preprocessing the job workflow's static specification in MCCF. The obtained information is used in both static and dynamic algorithms to identify partners for agent communication. The mobile agent dynamic location and communication based on this approach is expected to reduce the agent communication overhead by removing unnecessary communication partners during the dynamic job workflow execution. The proof of the dynamic algorithm's correctness and effectiveness are elaborated. Finally, the algorithms are evaluated through a comparison study using simulated job workflows executed on a prototype implementation of the MCCF on a LAN environment and an emulated WAN setup. The results show the scalability and efficiency of the algorithms as well as the advantages of the dynamic algorithm over the static one.     

Methods of resource management in problem-oriented computing environment

One of the important classes of computational problems is problem-oriented workflow applications executed in distributed computing environment. A problem-oriented workflow application can be represented by a directed graph whose vertices are tasks and arcs are data flows. For a problem-oriented workflow application, we can get a priori estimates of the task execution time and the amount of data to be transferred between the tasks. A distributed computing environment designed for the execution of such tasks in a certain subject domain is called problem-oriented environment. To efficiently use resources of the distributed computing environment, special scheduling algorithms are applied. Nowadays, a great number of such algorithms have been proposed. Some of them (like the DSC algorithm) take into account specific features of problem-oriented workflow applications. Others (like Min–Min algorithm) take into account many-core structure of nodes of the computational network. However, none of them takes into account both factors. In this paper, a mathematical model of problem-oriented computing environment is constructed, and a new problem-oriented scheduling (POS) algorithm is proposed. The POS algorithm takes into account both specifics of the problem-oriented jobs and multi-core structure of the computing system nodes. Results of computational experiments comparing the POS algorithm with other known scheduling algorithms are presented.     

A Direct Execution Approach to Simulating Mobile Agent Algorithms

Mobile agent technology has been applied to develop the solutions for various kinds of parallel and distributed computing problems. However, performance evaluation of mobile agent algorithms remains a difficult task, mainly due to the characteristics of mobile agents such as distributed and asynchronous execution, autonomy and mobility. This paper proposes a general approach based on direct execution simulation for evaluating the performance of mobile agent algorithms by collecting and analyzing the information about the agents during their execution. We describe the proposed generic simulation model, named MADES, the architecture of a software environment based on MADES, and a prototype implementation. A mobile agent-based distributed load balancing algorithm has been used for experiments with the prototype.     

基于多事务间序列模式的动态数据分布算法

用户通信代价是分布式系统性能的一个重要方面。特别是当今越来越普及的移动计算环境下,降低通信代价的意义尤其重大。通过对用户数据采用更为灵活的数据分布算法,可以有效地降低用户付出的通信代价。论文给出一种新的数据分布算法———基于多事务间序列模式数据分布算法,并从理论和实验上对其性能进行了分析。     

Execution coordination in mobile agent-based distributed job workflow execution

Mobile agent-based distributed job workflow execution requires the use of execution coordination techniques to ensure that an agent executing a subjob can locate its predecessors’ execution results. This paper describes the classification, implementation, and evaluation of execution coordination techniques in the mobile agent-based distributed job workflow execution system. First, a classification of the existing execution coordination techniques is developed for mobile agent systems. Second, to put the discussion into perspective, our framework for mobile agent-based distributed job workflow execution over the Grid (that is, MCCF: Mobile Code Collaboration Framework) is described. How the existing coordination techniques can be applied in the MCCF is also discussed. Finally, a performance study has been conducted to evaluate three coordination techniques using real and simulated job workflows. The results are presented and discussed in the paper.     

The MPE toolkit for supporting distributed applications

This paper presents a toolkit for supporting the execution of coarse-grain, parallel (distributed) applications under the MOSIX multicomputer operating system. These tools use standard UNIX System V process control and message-passing facilities, as well as the dynamic process migration mechanisms of MOSIX. The MPE tools can be used to modify sequential applications that were originally written for execution in a uniprocessor environment, to run efficiently in a distributed environment, consisting of several loosely coupled independent computers that communicate by messages. After presenting the MPE tools, the paper gives examples of several sequential algorithms that have been modified for execution in such a distributed multicomputer, as well as the resulting execution speed-ups that were obtained.     

Precise execution offloading for applications with dynamic behavior in mobile cloud computing

In order to accommodate the high demand for performance in smartphones, mobile cloud computing techniques, which aim to enhance a smartphone’s performance through utilizing powerful cloud servers, were suggested. Among such techniques, execution offloading, which migrates a thread between a mobile device and a server, is often employed. In such execution offloading techniques, it is typical to dynamically decide what code part is to be offloaded through decision making algorithms. In order to achieve optimal offloading performance, however, the gain and cost of offloading must be predicted accurately for such algorithms. Previous works did not try hard to do this because it is usually expensive to make an accurate prediction. Thus in this paper, we introduce novel techniques to automatically generate accurate and efficient method-wise performance predictors for mobile applications and empirically show they enhance the performance of offloading.     

A comparative study of some concurrency control algorithms for cluster-based communication networks

This paper presents a comparative study of some concurrency control algorithms for distributed databases of computer clusters which emphasize high availability and high performance requirements. For this purpose, we have analyzed some concurrency control algorithms which are used in commercial DBMSs, such as the pessimistic locking algorithm as it verifies transaction conflicts early in their execution phase, and the optimistic algorithm which investigates the presence of conflicts after the execution phase. A new algorithm is proposed and implemented by a simulation program. The three algorithms were tested using different configurations. Simulation results showed that the locking algorithm performed better than the optimistic method in presence of conflicts between transactions, while the optimistic algorithm provided better results in the absence of conflicts. Furthermore, in a distributed database with a certain probability of conflicts, the locking algorithm can be used to guarantee strong consistency and an acceptable level of performance. However, if this probability is negligible, the system performance can be improved by using the optimistic algorithm. The proposed algorithm offers improved performance in numerous cases. As a result, it can be used in a distributed database to guarantee a satisfactory level of performance in the presence of conflicts.     

Stochastic Performance Prediction for Iterative Algorithms in Distributed Environments

The parallelization of iterative algorithms is an important issue for efficient solution of large numerical problems. Several theoretical results concerning sufficient conditions for, and speed of, convergence of parallel iterative algorithms are available. However, those results usually do not take into account the processor workloads and network communications at the application level. The approach in this paper develops a Markov chain based on random variables which describe aspects of the multiuser, distributed-memory environment and the phases of the algorithm. The performance characterization addresses stochastic characteristics of the algorithmic execution time such as mean values and standard deviations. We present simulation results as well as experimental results over different time periods. The results provide information about the impact of distributed environment and implementation style on long-run, expected execution time characteristics.     

Heterogeneous Computing and Parallel Genetic Algorithms

This paper analyzes some technical and practical issues concerning the heterogeneous execution of parallel genetic algorithms (PGAs). In order to cope with a plethora of different operating systems, security restrictions, and other problems associated to multi-platform execution, we use Java to implement a distributed PGA model. The distributed PGA runs at the same time on different machines linked by different kinds of communication networks. This algorithm benefits from the computational resources offered by modern LANs and by Internet, therefore allowing researchers to solve more difficult problems by using a large set of available machines. We analyze the way in which such heterogeneous systems affect the genetic search for two problems. Our conclusion is that super-linear performance can be achieved not only in homogeneous but also in heterogeneous clusters of machines. In addition, we study some special features of the running platforms for PGAs, and basically find out that heterogeneous computing can be as efficient or even more efficient than homogeneous computing for parallel heuristics.