基于遗传算法的资源结点选择策略

网格系统中由于采用资源备份策略,资源存在多个副本.用户请求资源时,如何从拥有相同资源的多个结点中选择一部分结点获取资源,达到以相同的代价获得最优质的服务,是一个迫切需要研究的问题.文中综合用户和资源拥有者双方的利益因素,将该问题归结为一个多目标规划问题,并提出了资源动态复制的图模型,进而运用遗传算法,提出了解决该问题的策略,分析显示该算法可以达到满意的结果.     

蚂蚁算法在数据网格副本选择中的应用研究

数据网格中由于采用复制备份策略,文件存在多个副本.用户访问文件时,如何从拥有相同文件的多个结点中选择某一结点获取文件,达到以相同的代价获得最优质的服务,是一个迫切需要研究的问题.深入研究了蚂蚁算法的原理,分析了影响副本选择性能的主要因素,利用这些参考因素设计了基于蚂蚁算法的副本选择策略;并对这个新算法进行了分析和实现.经仿真平台实验,表明该算法可有效地减少数据访问延迟及带宽消耗,实现网格中存储节点间的负载平衡,提高数据的访问速度.     

基于Hilbert曲线的双层覆盖网模型

资源定位和负栽均衡是P2P网络系统中急需解决的核心问题。本文给出的模型将连续相邻的结点组成一个无结构的结点组，结点组之间利用Chord协议连接成为一个全局结构化的环形拓扑结构，并在该拓扑结构中引入HBLS资源查找算法和动态负载均衡双层环状模型调度策略。整个方案不仅具有很好的负载平衡特性，而且为查找提供了充分的灵活性性。仿真实验表明，该模型在资源定位和负载平衡两个方面都优于原Chord。     

基于DSOF的网格资源发现模型研究

为了解决传统资源发现机制不能很好地适应网格资源环境的问题,在有效组建网格资源虚拟组织的基础上,提出了资源虚拟组织中联系结点的选取原则和选取算法,通过该算法从具有相同资源类型的联系结点中选出管理结点.联系结点和管理结点之间采用类似二部图的方式进行连接,构成基于动态自组织覆盖架构的网格资源发现模型.从资源发现能力、资源发现效率和系统可扩展性等方面对该模型进行了综合评价,结果显示该模型适合网格资源的特征,在大规模网格系统中能够有效提高资源发现性能,仿真实验验证了该模型的有效性.     

一种用于多信道Ad Hoc网络MAC协议的匹配策略

在ad hoc网络中,使用多个正交信道,并行地传输数据是一种提高网络吞吐率,降低数据时延的有效手段.目前多广播域类协议,由于不需要额外的硬件,同时也不需要在网络结点之间建立时间同步机制,比其他多信道MAC协议具备更好的灵活性.经过研究发现,传递广播数据包,将加重多广播域协议解决死锁、发送等待和匹配效率等问题的协议开销,从而制约ad hoc网络的实际性能.采用支持广播/多播的匹配策略MAMR(Matching Algo-rithm for Multiple Rendezvous),将有助于解决广播问题.MAMR根据ad hoc网络的数据传送需求,将任一网络收敛至一个无冲突,无死锁的匹配状态.在该策略的收敛状态下所有非广播结点的入度不大于1,并且任两个相邻的广播结点间不会有匹配的边存在,从而避免了广播结点间可能出现的数据发送冲突.进一步证明,该策略可以在O(4m)步内收敛,并且可以在限定条件下达到极大匹配状态;在无广播结点时,该策略与Hsu和Huang提出的分布式网络中的极大匹配策略等价.仿真显示,在具有5%的广播数据需求时,该策略可以使MAXM、BTMC协议性能提高10%.     

并行XML数据库系统中数据分片策略的研究

数据分片策略是影响并行数据库系统性能的重要因素之一．着重探讨并行XML数据库系统中大规模XML文档的数据分片问题，提出与传统数据库分片策略不同的两种新的分片方法：基于路径模式的路径实例平衡法(PSPIB)和基于结点模式的结点轮循法(NSNRR)．前者的思想是析散DOM树中具有相同路径模式的路径实例，将其分配到不同站点；后者的思想是将DOM树中具有不同结点模式的元素结点以轮循方式析散到不同站点，而将具有相同结点模式的元素结点聚簇到同一站点．还介绍了这两种分片策略的实现，并给出了相应的基于RPE查询的性能测试、分析和评价．     

异构云计算体系结构及其多资源联合公平分配策略

资源分配策略是当前云计算研究领域中的一个重要研究热点,异构云计算体系结构下的复杂应用问题研究中,最基本的问题在于如何将总体有限的资源分配给多个租户或应用,以达到效率或收效最大化.但是,在经典的资源分配问题中,任务或者用户往往是“贪婪”的;因此,在总体资源有限的前提下,资源分配的公平性就显得尤为重要.为了满足不同的任务需求,达到多种资源分配的公平性,设计了一个虚拟化的异构云计算体系结构,提出了该体系结构下基于占优资源的多资源联合公平分配算法(maximizing multi-resource fairness based on dominant resource,MDRF),并且证明了算法的帕累托等相关属性;给出了占优资源熵(dominant resource entropy,DRE)和占优资源权重(dominant resource weight,DRW)的定义,占优资源熵更加精确地刻画了用户资源请求与任务所调度到的服务器资源之间的适应程度,使系统的自适应能力更强同时提高了资源利用率.占优资源权重保障了用户优先获取资源的优先次序,协同所采用保障公平性的Max-Min Fairness策略,使资源的分配更加有序.实验表明,我们的策略有更高的系统资源利用率,并且使需求与供给更加匹配,进而使用户的占优资源获取更多,提高了服务质量.     

列存储中的OLAP多查询优化方法

为了使列存储OLAP(on-line analytical processing)操作中I/O和CPU开销较大的扫描、连接、聚集操作实现有效的共享和复用,提出了一个多查询优化技术。根据列存储以及OLAP操作的特点,提出了一系列转换规则,为OLAP查询请求产生的一组相关查询语句生成一个单一全局查询计划。为了达到共享复用的目的,在全局计划中引入新的过滤结点、分组结点、合并结点和聚集结点。同时,借用MuGA(multiply group by algo-rithm)算法,通过分组结点、合并结点、连接结点实现维表及事实表元组的分组序号标记,从而实现列扫描、列连接的共享。并为聚集结点提出了一个多阶段聚集算法,结合最终生成的事实表复合分组序号,实现聚集操作的复用。在SSB(star schema benchmark)数据集上设计实验,证明了该多查询优化策略的有效性。     

渲染网格调度策略研究

针对渲染网格的特点提出了渲染网格的二个调度模型.模型一提出了在局部资源的计算结点内,采用基于用户优先级的CPU频度轮转调度方案,达到提高系统吞吐率的目的.模型二提出了适合市场机制的渲染网格调度模型,并给出了竞标的报价算法.该算法在公共网格开放软件Globus Toolkit4.0上实现,符合国际标准WSRF规范.实验结果表明本调度策略在处理渲染网格任务时性能得到了很好的体现.     

缓冲辅助中继网络中的缓冲资源分配方法

随着通信技术和智能终端的发展,移动内容消费出现了明显的增长。人们随时随地享受无线网络带来的多媒体娱乐体验的同时,也给蜂窝网络带来了巨大的压力,尤其是热门视频的重复传输,浪费了网络资源。预存储技术被认为是解决这类问题的有效方法。它是在非高峰时段,将热门视频等预先存储在网络边缘,以缓解高峰时段网络流量的压力。为了给用户提供更好的内容传输效率,预存储技术中基于缓冲辅助的中继网络被提出。由于中继结点缓冲空间有限,当其需要服务多个用户时,有必要对中继结点的缓冲资源进行优化分配。基于此,本文提出了一种中继结点的缓冲资源分配方法。首先,根据中继结点数据到达模型和数据处理模型,以及中继结点能忍受的缓冲区溢出概率,得到中继结点可以贡献出的最大缓冲区比例;接着,根据用户的数据到达模型和数据服务模型,给出用户缓冲区溢出概率与缓冲区比例关系的表达式;然后,建立优化问题,以最小化网络中所有用户缓冲区溢出概率和为目标,给出最优的缓冲区分配方法;最后,仿真结果证实了本文提出的方法比均匀分配法能获得更小的用户缓冲区溢出概率和。     

Multi-criteria human resource allocation for solving multistage combinatorial optimization problems using multiobjective hybrid genetic algorithm

Multi-criteria human resource allocation involves deciding how to divide human resource of limited availability among multiple demands in a way that optimizes current objectives. In this paper, we focus on multi-criteria human resource allocation for solving multistage combinatorial optimization problem. Hence we tackle this problem via a multistage decision-making model. A multistage decision-making model is similar to a complex problem solving, in which a suitable sequence of decisions is to be found. The task can be interpreted as a series of interactions between a decision maker and an outside world, at each stage of which some decisions are available and their immediate effect can be easily computed. Eventually, goals would be reached due to the found of optimized variables. In order to obtain a set of Pareto solutions efficiently, we propose a multiobjective hybrid genetic algorithm (mohGA) approach based on the multistage decision-making model for solving combinatorial optimization problems. According to the proposed method, we apply the mohGA to seek feasible solutions for all stages. The effectiveness of the proposed algorithm was validated by its application to an illustrative example dealing with multiobjective resource allocation problem.     

Multiobjective model for solving resource‐leveling problem with discounted cash flows

Nowadays, executers are struggling to improve the economic and scheduling situation of projects. Construction scheduling techniques often produce schedules that cause undesirable resource fluctuations that are inefficient and costly to implement on site. The objective of the resource‐leveling problem is to reduce resource fluctuation related costs (hiring and firing costs) without violating the project deadline. In this article, minimizing the discounted costs of resource fluctuations and minimizing the project makespan are considered in a multiobjective model. The problem is formulated as an integer nonlinear programming model, and since the optimization problem is NP‐hard, we propose multiobjective evolutionary algorithms, namely nondominated sorting genetic algorithm‐II (NSGA‐II), strength Pareto evolutionary algorithm‐II (SPEA‐II), and multiobjective particle swarm optimization (MOPSO) to solve our suggested model. To evaluate the performance of the algorithms, experimental performance analysis on various instances is presented. Furthermore, in order to study the performance of these algorithms, three criteria are proposed and compared with each other to demonstrate the strengths of each applied algorithm. To validate the results obtained for the suggested model, we compared the results of the first objective function with a well‐tuned genetic algorithm and differential algorithm, and we also compared the makespan results with one of the popular algorithms for the resource constraints project scheduling problem. Finally, we can observe that the NSGA‐II algorithm presents better solutions than the other two algorithms on average.     

CREW: Cost and Reliability aware Eagle-Whale optimiser for service placement in Fog

Integration of Internet of Things (IoT) with industries revamps the traditional ways in which industries work. Fog computing extends Cloud services to the vicinity of end users. Fog reduces delays induced by communication with the distant clouds in IoT environments. The resource constrained nature of Fog computing nodes demands an efficient placement policy for deploying applications, or their services. The distributed and heterogeneous features of Fog environments deem it imperative to consider the reliability performance parameter in placement decisions to provide services without interruptions. Increasing reliability leads to an increase in the cost. In this article, we propose a service placement policy which addresses the conflicting criteria of service reliability and monetary cost. A multiobjective optimisation problem is formulated and a novel placement policy, Cost and Reliability-aware Eagle-Whale (CREW), is proposed to provide placement decisions ensuring timely service responses. Considering the exponentially large solution space, CREW adopts Eagle strategy based multi-Whale optimisation for taking placement decisions. We have considered real time microservice applications for validating our approaches, and CREW has been experimentally shown to outperform the existing popular multiobjective meta-heuristics such as NSGA-II and MOWOA based placement strategies.     

多QoS约束网格作业调度问题的多目标演化算法

针对网格计算中的多QoS约束网格作业调度问题，以独立作业为研究对象，将其规约为多目标组合最优化问题．通过深入剖析多目标最优化理论及其演化算法，结合网格作业调度自然特征，提出了一种解决多QoS约束网格作业调度问题的多目标演化算法．该算法求解多个QoS维度效用函数指标的非劣解集，尝试解决多管理域间网格用户、资源管理者等网格实体的多目标协同问题．仿真结果表明，在时间维度、可靠性维度、安全性维度QoS效用值等用户级QoS指标，以及丢弃作业数等系统级指标方面该算法与QoS-Min-min和QoS-Sufferage等同类算法相比具有较好的综合性能．     

网格资源发现中资源虚拟组织构建方法研究

怎样合理有效地组织网格资源是网格资源发现的关键问题.对网格环境下资源的组织方式进行了研究,提出了网格资源结点动态自组织算法,通过该算法能够将网格资源结点合理有效地组织成具有资源类型的网格资源虚拟组织;提出了资源虚拟组织大小划分的原则,有效地解决了网格资源发现系统中资源虚拟组织与系统规模之间的矛盾,通过仿真试验验证了该方法的有效性.     

基于遗传算法的排课问题分层研究

分析了目前高校排课存在的问题,研究如何利用遗传算法解决排课问题以及冲突,并设计应该考虑的各种约束条件。把传统的排课问题分为时间排课和教室排课两个方面来研究,在时间排课方面又分为单目标排课和多目标排课两个步骤来考虑。通过计算机化管理的排课问题,能够有效地提高工作效率。     

项目资源均衡研究综述

作为一类经典的项目调度问题,资源均衡研究的是如何通过安排活动来均衡整个项目周期内资源的使用.资源均衡问题因其重要的理论价值和应用背景,一直是项目调度领域的重点研究课题.鉴于此,对国内外项目资源均衡的研究成果进行了系统性总结与梳理:介绍了资源均衡问题的数学模型和常用的测试问题库;综述了求解资源均衡问题的各类算法;总结了资源均衡问题的一些扩展问题和应用情况.最后指出了未来进一步的研究方向.     

网格资源模型综述

网格计算将广域网上的各种计算资源、信息资源、设备资源等集成起来,以统一的方式向用户提供服务,是当前网络计算领域的研究热点.其中资源模型是关系到广域分布式环境中资源共享和协同工作效率的关键.本文介绍了网格中的资源模型的主要研究内容,以及国内外相关领域的研究现状.     

Unsatisfying functions and multiobjective fuzzy satisfaction design using genetic algorithms

This paper describes a new fuzzy satisfaction method using genetic algorithms (GA) for multiobjective problems. First, an unsatisfying function, which has a one-to-one correspondence with the membership function, is introduced for expressing "fuzziness". Next, the multiobjective design problem is transformed into a satisfaction problem of constraints by introducing an aspiration level for each objective. Here, in order to handle the fuzziness involved in aspiration levels and constraints, the unsatisfying function is used, and the problem is formulated as a multiobjective minimization problem of unsatisfaction ratings. Then, a GA is employed to solve the problem, and a new strategy is proposed to obtain a group of Pareto-optimal solutions in which the decision maker (DM) is interested. The DM can then seek a satisfaction solution by modifying parameters interactively according to preferences.     

Single-machine scheduling to minimize total convex resource consumption with a constraint on total weighted flow time

In this paper, we consider single-machine scheduling problem in which processing time of a job is described by a convex decreasing resource consumption function. The objective is to minimize the total amount of resource consumed subject to a constraint on total weighted flow time. The optimal resource allocation is obtained for any arbitrary job sequence. The computational complexity of the general problem remains an open question, but we present and analyze some special cases that are solvable by using polynomial time algorithms. For the general problem, several dominance properties and some lower bounds are derived, which are used to speed up the elimination process of a branch-and-bound algorithm proposed to solve the problem. A heuristic algorithm is also proposed, which is shown by computational experiments to perform effectively and efficiently in obtaining near-optimal solutions. The results show that the average percentage error of the proposed heuristic algorithm from optimal solutions is less than 3%.