Midland: a service-oriented cluster computing infrastructure

Midland is a service-oriented software infrastructure that enables the clustering of arbitrarily large collections of computing resources. The resulting clusters may be integrated to form an open, dynamically configurable computational grid system where each cluster defines a self-reliant and independent management domain. Web Services make up the primary integration mechanism both at the cluster and grid levels, respectively. This is complemented by a light XML based messaging protocol exclusively used for cluster bound interactions. The paper describes Midland’s architecture, and the service-oriented approach taken to develop the associated resource management mechanisms. It also includes an exposition of the model of service capacity which is one of the enablers of the service-centric strategy underlying the cluster management mechanisms. The operational performance of Midland is illustrated experimentally in the context of a Grid test-bed comprising three clusters. The experimental results highlight the performance of the model of service capacity as well as some aspects of Midland operational performance.     

A classification-based privacy-preserving decision-making for secure data sharing in Internet of Things assisted applications

The introduction of the Internet of Things (IoT) paradigm serves as pervasive resource access and sharing platform for different real-time applications. Decentralized resource availability, access, and allocation provide a better quality of user experience regardless of the application type and scenario. However, privacy remains an open issue in this ubiquitous sharing platform due to massive and replicated data availability. In this paper, privacy-preserving decision-making for the data-sharing scheme is introduced. This scheme is responsible for improving the security in data sharing without the impact of replicated resources on communicating users. In this scheme, classification learning is used for identifying replicas and accessing granted resources independently. Based on the trust score of the available resources, this classification is recurrently performed to improve the reliability of information sharing. The user-level decisions for information sharing and access are made using the classification of the resources at the time of availability. This proposed scheme is verified using the metrics access delay, success ratio, computation complexity, and sharing loss.     

认知雷达抗干扰中的博弈论分析综述

雷达对抗的核心研究内容主要是干扰策略与抗干扰策略之间的对抗博弈,其作为电子战研究领域的热点一直备受学者们关注.该文综述了学者们利用合作与非合作博弈方法来分析雷达在进行目标探测和干扰抑制时所使用的策略,主要通过不同体制的雷达利用认知技术感知和学习外界复杂的电磁环境,合理地分配发射功率、控制编码序列、设计波形、研究检测和跟踪方法以及分配雷达通信资源等.这样雷达既节约发射所消耗的功率,又可以自适应地搜索和跟踪目标而不被敌方所发现,从而使雷达在复杂多变的现代战场环境中达到自身最优的性能.最后,对认知雷达抗干扰中的博弈论分析研究进行总结和展望,并指出了一些博弈论在认知雷达抗干扰策略应用中所面临的潜在问题和挑战.     

我国推行人力资源会计的探讨

人力资源是经济增长的最终源泉,因此,对人力资源进行有效管理显得极为重要。通过传统会计和人力资源会计的分析对比,结合国外的具体作法,提出推行人力资源会计的见解。     

基于SOA技术的高校URP建设的研究

通过基于SOA技术的高校管理工作一体化信息系统软件的设计,综合性地量化学校各项重要信息,使信息一体化程度更为集中,帮助管理工作者、校务管理人员做出动态的管理决策调整,对于URP流程改造提供具体的实践模型参考。同时研究出一种能够应用于各类普通高校管理工作的无线与网络相结合的软件系统设计模型,提供一种可量化的工作数据系统样本,以比较低廉的代价解决校园信息化过程中的信息孤岛问题,进一步提升校园信息一体化程度。     

On Lazy Bin Covering and Packing problems

In this paper, we study two interesting variants of the classical bin packing problem, called Lazy Bin Covering (LBC) and Cardinality Constrained Maximum Resource Bin Packing (CCMRBP) problems. For the offline LBC problem, we first prove the approximation ratio of the First-Fit-Decreasing and First-Fit-Increasing algorithms, then present an APTAS. For the online LBC problem, we give a competitive analysis for the algorithms of Next-Fit, Worst-Fit, First-Fit, and a modified HARMONIC_M algorithm. The CCMRBP problem is a generalization of the Maximum Resource Bin Packing (MRBP) problem Boyar et al. (2006) [1]. For this problem, we prove that its offline version is no harder to approximate than the offline MRBP problem.     

Distributed bandwidth allocation based on alternating evolution algorithm

In a network, end nodes have to compete for bandwidth through some distributed congestion control algorithms. It is a great challenge to ensure the efficiency and fairness of the distributed control algorithms. TCP congestion control algorithms do not perform well in terms of their efficiency and fairness in high speed networks. In this paper, we propose a novel asymptotic evolution algorithm based on the Logistic Model to allocate limited bandwidth resource. The algorithm introduces an explicit bandwidth pre-allocation factor. The factor is carried by the packet and is computed in routers based on the information of the router capacity, the aggregate load, and the instantaneous queue length; therefore the algorithm does not require the routers to keep the per-flow state. According to this pre-allocation bandwidth factor, the senders asymptotically adjust their sending rate and the bandwidth factor changes asymptotically along with the variation of the aggregate load and the queue length in the routers; therefore the sending rate and the pre-allocation bandwidth factor form alternating evolution and eventually reach a steady state.     

基于蚁群算法的网格资源发现模型研究

本文通过对传统的网格资源发现存在的问题进行分析,针对其不足,引入蚁群算法,提出基于蚁群算法的网格资源发现模型（AA_GRRM）,设计并分析AA_GRRM的体系结构,并对其关键模块分析设计,以提高网格资源发现效率。     

基于WSRF的FPGA远程实验服务

介绍WSRF规范,结合FPGA实验特点和WSRF协议中状态管理,设计基于WSRF的远程实验架构。使用WSRF.NET设计与实现FPGA实验资源服务,通过实验验证了服务的正确性。实验结果表明,使用WSRF规范设计FPGA实验资源服务能解决实验的中间状态问题,实现实验时间、空间的开放。     

Inverse data envelopment analysis model to preserve relative efficiency values: The case of variable returns to scale

This paper studies the inverse Data Envelopment Analysis (inverse DEA) for the case of variable returns to scale (inverse BCC). The developed inverse BCC model can preserve relative efficiency values of all decision making units (DMUs) in a new production possibility set composing of all current DMUs and a perturbed DMU with new input and output values. We consider the inverse BCC model for a resource allocation problem, where increases of some outputs and decreases of the other outputs of the considered DMU can be taken into account simultaneously. The inverse BCC problem is in the form of a multi-objective nonlinear programming model (MONLP), which is not easy to solve. We propose a linear programming model, which gives a Pareto-efficient solution to the inverse BCC problem. However, there exists at least an optimal solution to the proposed model if and only if the new output vector is in the set of current production possibility set. The proposed approach is illustrated via a case study of a motorcycle-part company.