地质矿产资源勘查问题及对策

为了适应我国当前社会发展的需要,地质矿产资源的利用率越来越高。我国拥有大量的矿产资源,但由于长期不合理的勘探、开发和出口,使矿产企业遭受了巨大的损失。要促进我国矿产资源产业的发展及转型,就必须加强地质矿产勘察力度,同时保证合理开发。     

Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing

Today, almost everyone is connected to the Internet and uses different Cloud solutions to store, deliver and process data. Cloud computing assembles large networks of virtualized services such as hardware and software resources. The new era in which ICT penetrated almost all domains (healthcare, aged-care, social assistance, surveillance, education, etc.) creates the need of new multimedia content-driven applications. These applications generate huge amount of data, require gathering, processing and then aggregation in a fault-tolerant, reliable and secure heterogeneous distributed system created by a mixture of Cloud systems (public/private), mobile devices networks, desktop-based clusters, etc. In this context dynamic resource provisioning for Big Data application scheduling became a challenge in modern systems. We proposed a resource-aware hybrid scheduling algorithm for different types of application: batch jobs and workflows. The proposed algorithm considers hierarchical clustering of the available resources into groups in the allocation phase. Task execution is performed in two phases: in the first, tasks are assigned to groups of resources and in the second phase, a classical scheduling algorithm is used for each group of resources. The proposed algorithm is suitable for Heterogeneous Distributed Computing, especially for modern High-Performance Computing (HPC) systems in which applications are modeled with various requirements (both IO and computational intensive), with accent on data from multimedia applications. We evaluate their performance in a realistic setting of CloudSim tool with respect to load-balancing, cost savings, dependency assurance for workflows and computational efficiency, and investigate the computing methods of these performance metrics at runtime.     

农业海量信息资源管理平台设计与实现

随着农业信息化的深入,如何描述、存储、管理和处理海量分布信息资源成为农业应用的首要问题。提出基于XML/XML Schema的统一农业信息资源描述框架,将XML描述与元数据约束相结合,采用层次化管理结构实现元数据与资源信息的组织;在HDFS(Hadoop Distributed File System)云存储架构适应性改进基础之上,设计实现了农业海量信息资源管理平台,实现了海量、分布、异构资源的分类组织、存储和访问服务。平台覆盖了全国6个省市资源节点,实现了3.2 TB农业资源存储管理,与传统资源检索方法相比,平台资源检索时间缩短73%;同时,用户在同一检索条件下能更多地获得相关资源,不仅查准率平均提高5%,而且资源利用率平均提高17.7%。平台在海量资源检索和利用效率方面获得提升。     

云计算环境下基于主动预测的节点部署模型研究

随着云计算的不断普及,越来越多的用户选择将自身的业务迁移至云计算系统。 用户的使用习惯与社会日常的运行规律 也伴随着大量用户涌入云计算系统,如每早8点集中地向云计算系统申请资源节点,这给系统带来了一种可预期的资源冲击。针对上述问题,提出了一种基于主动预测模式的资源部署模型。该模型首先根据预测模块中Holt-Winters季节指数平滑模型的算法周期长度来预测下一个时间周期的任务请求量,通过设计的主动预测算法判断是否应对当前的任务请求量做出响应并得出其具体数量、位置等参数指标,以实现对用户使用规律的主动应对。使用CloudSim进行仿真实验,系统地评判模型的性能。实验结果表明,AF-HW模型在应对可预测的海量并发任务请求时可有效地提升单点及整体的响应速率,使用户得到更好的体验。     

局域网限速状态下的资源调度模型仿真

在局域网限速状态下进行资源调度时,网络资源受到传输区域的影响,在调度过程中存在较大的流量突变随机性.传统的网络资源调度算法在应对海量突变流量时,采用延迟限制通信的方法,随便能缓解流量峰值,但仅仅通过外加通信约束条件约束、延迟流量突变的通信过程,调度过程存在弊端.提出采用混合蚁群算法的局域网限速状态下资源调度方法,将禁忌搜索作为蚁群算法局部搜索策略,并通过结合分布估计提高算法的收敛速度,把局域网限速状态下资源均衡与优化问题转化为组合优化的最短路径问题,进行计算求精确解.仿真结果表明,利用混合蚂蚁算法在局域网限速状态下进行资源调度,具一定鲁棒性和分布性,提升了调度工作效率.     

电力局域网信道优化调度建模仿真与研究

在电力局域网限速状态下,合理调度电力资源是解决网络拥堵的关键.电力网络信道资源调度过程中,为了保证调度的精度,调度过程被人为添加了过多的约束条件,用于描述局域网络限速过程.传统的信道调度方法在网络限速状态下资源调度的过程中,其多约束条件下的调度过程本身就是个矛盾的过程,由于约束造成了负载不均衡,资源调度效率不高.提出基于最大最小价格的分布式信道调度方法.分析了电力局域网限速状态下的信道调度原理,获取信道调度的约束条件.将信道调度问题分解成无需任何约束条件的非线性规划问题和有约束条件的线性规划问题,获取这两者信道传输方程,在信道的传输过程中采用最大价格的最大最小公平传输速率分配方法控制信道的调度,获取最优信道调度方案.实验结果表明,改进算法具有良好的调度性能.     

A resource oriented integration architecture for the Internet of Things: A business process perspective

The vision of the Internet of Things (IoT) foresees a future Internet incorporating smart physical objects that offer hosted functionality as IoT services. These services when integrated with the traditional enterprise level services form the creation of ambient intelligence for a wide range of applications. To facilitate seamless access and service life cycle management of large, distributed and heterogeneous IoT resources, service oriented computing and resource oriented approaches have been widely used as promising technologies. However, a reference architecture integrating IoT services into either of these two technologies is still an open research challenge. In this article, we adopt the resource oriented approach to provide an end-to-end integration architecture of front-end IoT devices with the back-end business process applications. The proposed architecture promises a programmer friendly access to IoT services, an event management mechanism to propagate context information of IoT devices, a service replacement facility upon service failure, and a decentralized execution of the IoT aware business processes.     

Proportional and double imitation rules for spectrum access in cognitive radio networks

In this paper, we tackle the problem of opportunistic spectrum access in large-scale cognitive radio networks, where the unlicensed Secondary Users (SUs) access the frequency channels partially occupied by the licensed Primary Users (PUs). Each channel is characterized by an availability probability unknown to the SUs. We apply population game theory to model the spectrum access problem and develop distributed spectrum access policies based on imitation, a behavior rule widely applied in human societies consisting of imitating successful behaviors. We develop two imitation-based spectrum access policies based on the basic Proportional Imitation (PI) rule and the more advanced Double Imitation (DI) rule given that a SU can only imitate the other SUs operating on the same channel. A systematic theoretical analysis is presented for both policies on the induced imitation dynamics and the convergence properties of the proposed policies to the Nash equilibrium. Simple and natural, the proposed imitation-based spectrum access policies can be implemented distributedly based on solely local interactions and thus is especially suited in decentralized adaptive learning environments as cognitive radio networks.     

Commodity or competitive advantage? Analysis of the ERP value paradox

A true e-business enabled firm needs the support from a well-tuned enterprise resource planning (ERP) system for providing real time data. However, many companies complain that after their huge investments in ERP systems, they found the ERP systems do not bring them new orders, new profits, or competitive advantage as ERP vendors claim. Academic studies also found mixed results regarding ERP’s payoff. In line with resource based view (RBV), the study proposes an integrated model to shed light on the ERP value paradox. We try to answer “With what organizational resources and by building what firm specific capabilities, the investment in ERP systems may bring firms competitive advantage”. Using a sample of 150 ERP and e-business adopters in the US, we found that (1) organizational resources such as managerial skills and organizational change management play a more important role than IT resources (ERP, e-Business technologies) in generating business integration capability. (2) However, neither IT resources nor organizational resources directly provide firms with competitive advantage. Instead, business integration capability built from the two resources plays a mediating role through which business achieves competitive advantage.     

分布式系统中的资源发现机制综述

资源发现问题亦即由给定的资源描述找到满足该描述的资源地址。如何快速并且准确地从分布存储的资源中找到所需的资源,是一个挑战性的问题。传统的网格资源发现系统采用注册中心和索引的方式,但这些方式不能满足网格系统规模不断扩大的需求。而对等网络是一种扩展性好的分布式系统,采用已有的P2P技术解决网格环境下的资源发现问题是一种有效的途径。介绍了网格系统中的资源发现、P2P系统的资源发现以及基于P2P的网格资源发现系统,并对它们进行了比较。