区块链架构下高效的车联网跨域数据安全共享研究

为了解决车联网环境下跨信任域数据共享中跨域数据泄露严重、跨域共享不可控、跨域访问效率低的问题，提出了一种区块链架构下高效的车联网跨域数据安全共享方案。不同信任域的可信机构构成区块链，采用改进的密文策略属性基加密算法加密数据，结合区块链和星际文件系统进行存储，构建了基于区块链的跨域数据细粒度、安全共享方案；设计了基于混淆布隆过滤器的跨域访问验证方法，智能合约基于链上访问策略进行快速的解密测试，提高大量跨域密文的访问效率；设计了基于外包解密的跨域数据获取方法，可信机构为跨域访问请求进行密文转换，并执行包含复杂双线性配对运算的外包解密，减少了车辆在解密过程的时间开销。实验结果表明，所提方案有效提高了跨域密文转换和车辆解密的效率，与现有方案相比，跨域数据访问效率平均提升了60%。     

孔径分割多功能雷达的任务调度研究

针对孔径分割多功能雷达实时任务调度中资源利用不充分的问题,提出了一种基于扩展域的任务自适应调度方法。方法以时间指针为对象,对该时刻的任务分析范围从当前调度间隔扩展到整个调度时间轴,从整个调度时间轴上所有满足时间指针处执行条件的任务中,选择一个优先级最高的任务,可实现任务间的紧密安排,有效减少空闲资源的浪费,使得孔径分割雷达能够在有限的资源内调度执行更多的任务。最后,将该方法与传统多任务并行调度方法进行对比仿真,仿真结果证明了该方法的有效性和优越性。     

基于动态域划分的 MapReduce 安全冗余调度策略简

MapReduce现有调度策略无法实现云环境中多租户作业的安全隔离。提出一种基于动态域划分的安全冗余调度策略：通过引入冲突关系、信任度、安全标签等概念,建立一种动态域划分模型,以将待调度节点划分为与不同租户作业关联的冲突域、可信域或调度域;结合冗余方式,将租户作业同时调度到其可信域节点和调度域节点(但不允许为其冲突域节点),通过二者执行环境和部分计算结果的一致性验证决定是否重新调度。实验分析了其有效性和安全性。     

云计算环境下能量感知的任务调度算法

云计算环境下传统独立任务调度算法容易导致较高资源能耗或较大任务时间跨度.针对该问题,文中提出了两种能量感知的任务调度算法,并利用遗传算法并行化搜索合理调度方案.两种算法在搜索过程中,分别通过能耗时间归一和能耗时间双适应度方法定义适应度函数并进行个体选择.仿真结果表明,与单独考虑时间或能耗相比,这两种算法能够更有效地缩短任务执行时间跨度,降低资源能耗.     

基于随机延时注入的跨时钟域信号验证方法

为了应对现代SOC复杂的时钟结构给跨时钟域信号处理带来的隐患,分析了跨时钟域信号产生的亚稳态现象的根本原因和常用的跨时钟域信号的处理方法,针对跨时钟域信号处理难以验证的问题,提出了基于随机延时注入的跨时钟域仿真验证方法.通过将亚稳态现象抽象成采样数据在时钟上的随机抖动,使得芯片设计的RTL前仿真在没有时钟树物理信息的情况下能够模拟出亚稳态效应.分析结果表明此方法能够完成SOC芯片的跨时钟域信号的功能验证.     

一种基于策略的跨自主域访问控制模型研究

针对分布式环境下各自主域访问控制模型的异构性以及跨域访问中域自治与协作问题,提出了一种基于策略的跨自主域访问控制模型.该模型通过自主域间访问主体的不同粒度映射机制,支持域间的安全互操作;通过安全控制器并结合基于XACML的访问控制策略,实现了域间用户权限的逻辑整合.各域的相关权限信息封装在域内,既保持原有的独立性又实现了域间的协作,同时屏蔽了域间主体差异,解决了不同域系统互不认知和异构访问控制模型映射问题.     

MPLS VPN跨域技术在电力数据通信网中的深化应用

随着企业网的不断发展及MPLS VPN部署的推广实施,在提供服务的骨干网络中,跨越不同服务提供商管理边界的跨域部署成为必然的要求.跨域VPN的建立过程经过近几年的实践和快速发展,业界提出了几种VPN跨域方法,即OPTION A/B/C三种.MPLS VPN可以分为MPLS/BGP三层VPN和MPLS L2VPN,两种VPN都支持上述的三种跨域方法,采用相同的跨域理念.MPLS L3VPN跨域技术突破单个服务提供商管理域的限制,扩展了MPLS VPN架构的灵活性,使得部署方式满足了不断扩展的网络部署要求,成为一种非常成熟的VPN业务部署架构.本文以MPLS/BGP三层VPN及Option C跨域方式为例,阐述了电力数据通信网的VPN跨域实现方式.     

跨域融合机理与运用研究

随着人类社会科技的发展与战争形态的演变,未来的冲突本质上是多领域的对抗,跨域融合成为研究未来战争制胜机理的重要课题.文中阐述了跨域融合相关研究的国内外发展现状,探讨了跨域融合基本概念的认识与理解,研究了跨域融合的关键机理,提出了跨域融合运用流程和运用模式,明确了跨域融合的协同运用机制,对跨域协同、多域作战、全域作战等理论研究有一定的学术参考价值.     

一种基于风险感知策略的多节点任务调度方法

为解决无人机(UAV)集群任务调度时面临各节点动态、不稳定的情况,该文提出一种面向多计算节点的可尽量避免任务中断且具有容错性的任务调度方法。该方法首先为基于多计算节点构建了一个以最小化任务平均完成时间为优化目标的任务分配策略;然后基于任务的完成时间和边缘计算节点的存留时间两者的概率分布,将任务计算节点上的执行风险量化成额外开销时间;最后以任务的完成时间与额外开销时间之和替换原本的完成时间,设计了风险感知的任务分配策略。在仿真环境下将该文提出的任务调度方法与3种基准调度方法进行了对比实验,实验结果表明该方法能够有效地降低任务平均响应时间、任务平均执行次数以及任务截止时间错失率。证明该文提出的方法降低了任务重调度和重新执行带来的额外开销,可实现分布式协同计算任务的调度工作,为复杂场景下的无人机集群网络提供新的技术支持。     

电力调度数据网MPLS VPN跨域互联技术研究

MPLS VPN以其诸多优点而在电力调度中得到了广泛的应用,文章在简要概述电力调度数据网应用MPLS VPN跨域互联必要性及MPLS VPN关键技术的基础上,对电力调度数据网MPLS VPN跨域互联技术进行分析,可为相关工作者提供参考。     

Task Scheduling and Lightpath Establishment in Optical Grids

Data-intensive Grid applications require huge data transferring between multiple geographically separated computing nodes where computing tasks are executed. For a future WDM network to efficiently support this type of emerging applications, neither the traditional approaches to establishing lightpaths between given source destination pairs are sufficient, nor are those existing application level approaches that consider computing resources but ignore the optical layer connectivity. Instead, lightpath establishment has to be considered jointly with task scheduling to achieve best performance. In this paper, we study the optimization problems of jointly scheduling both computing resources and network resources. We first present the formulation of two optimization problems with the objectives being the minimization of the completion time of a job and minimization of the resource usage/cost to satisfy a job with a deadline. When the objective is to minimize the completion time, we devise an optimal algorithm for a special type of applications. Furthermore, we propose efficient heuristics to deal with general applications with either optimization objective and demonstrate their good performances in simulation.     

一种云计算环境下任务调度策略

文章提出的问题是在云计算环境下任务调度策略。该策略的目标是将任务分配到计算单元上达到任务完成总时间最少和资源充分利用。基于此目标文章提出利用遗传算法对任务完成时间进行优化,并为处于空闲状态计算单元动态调整任务分配以改善资源利用率。利用CloudSim仿真平台验证该方法的有效性。     

An Optimized Collaborative Scheduling Algorithm for Prioritized Tasks with Shared Resources in Mobile-Edge and Cloud Computing Systems

Mobile edge computing (MEC) is a promising technology that has the potential to meet the latency requirements of next-generation mobile networks. Since MEC servers have limited resources, an orchestrator utilizes a scheduling algorithm to decide where and when each task should execute so that the quality of service (QoS) of each task is achieved. The scheduling algorithm should use the least possible resources required to meet the service demands. In this paper, we develop a two-level cooperative scheduling algorithm with a centralized orchestrator layer. The first scheduling level is used to schedule tasks locally on MEC servers. In contrast, the second level resides at the orchestrator and assigns tasks to a neighboring base station or the cloud. The tasks serve in accordance with their priority, which is determined by the latency and required throughput. We also present a resource optimization algorithm for determining resource distribution in the system in order to ensure satisfactory service availability at the minimum cost. The resource optimization algorithm contains two variations that can be employed depending on the traffic model. One variant is used when the traffic is uniformly distributed, and the other is used when the traffic load is unbalanced among base stations. Numerical results show that the cooperative model of task scheduling outperforms the non-cooperative model. Furthermore, the results show that the suggested scheduling algorithm performs better than other well-known scheduling algorithms, such as shortest job first scheduling and earliest deadline first scheduling.

     

Research on Cloudlet selection strategy for data streaming applications in mobile cloud environment

Most existing Cloudlet selection strategies only used the resources of one Cloudlet to compute offloading,which couldn’t obtain the superior performance improvement for mobile data streaming application with many parallel components.To address this issue,a Cloudlet selection strategy based on chemical reaction optimization algorithm was proposed.The strategy aims to reduce application’s completion time and energy consumption of mobile device.When the dependencies among application’s components was satisfied,the strategy can take full advantage of the computing capability of multi-cloudlet to execute the parallel components of mobile data stream application simultaneously.Therefore the strategy can improve the execution efficiency and reduce the energy consumption of mobile device.The simulation results reveal that the proposed strategy can achieves 18.3% on average performance improvement than POCSS strategy does in multi-Cloudlet environment.     

Integrated Scheduling of Grid Applications in WDM Optical Light-Trail Networks

A novel task graph model, flexible task model (FTM), is proposed for modeling the grid computing tasks and the relationships among the tasks. In this model, a task may generate output before the task completes whereas previous work assumes that no output is available until the task is completed. In addition, a task in FTM can start to execute when it has collected a minimum amount of required input from its predecessors. FTM is more general and flexible than the conventional task graph model considered in previous work. Based on FTM, we investigate the problem of scheduling grid applications that integrates the resource allocation for task execution and service provisioning for subwavelength data communication between the tasks. Data communication between grid tasks under the FTM model is better supported using light-trails in wavelength division multiplexing (WDM) networks, than lightpaths. The objective is to minimize the total amount of time for task completion or makespan. Simulation results show that our proposed scheduling algorithm under FTM significantly reduces the total task completion time compared with that under the conventional task graph model. Moreover, the communication service provisioning using light-trails is very resource efficient.      

移动网格可分任务调度研究

解决独立、等长度任务在性能异构且随时间动态变化的一组固定和移动资源上的调度问题,其中移动设备具有间歇连接性.提出动态循环调度DCS算法,用环结构组织任务,调度过程体现了重调度策略和复制策略.对算法进行性能分析和实验验证,表明算法性能和移动资源的间歇连接特征、任务运行跨度、移动资源数量、任务长度等因素相关.     

云计算环境下任务调度算法的研究

在云计算环境中存在庞大的任务数,为了能更加高效地完成任务请求,如何进行有效地任务调度是云计算环境下实现按需分配资源的关键。针对调度问题提出了一种基于蚁群优化的任务调度算法,该算法能适应云计算环境下的动态特性,且集成了蚁群算法在处理NP-Hard问题时的优点。该算法旨在减少任务调度完成时间。通过在CloudSim平台进行仿真实验,实验结果表明,改进后的算法能减少任务平均完成时间、并能在云计算环境下有效提高调度效率。     

A workflow task scheduling algorithm based on the resources' fuzzy clustering in cloud computing environment

Cloud computing is the key and frontier field of the current domestic and international computer technology, workflow task scheduling plays an important part of cloud computing, which is a policy that maps tasks to appropriate resources to execute. Effective task scheduling is essential for obtaining high performance in cloud environment. In this paper, we present a workflow task scheduling algorithm based on the resources' fuzzy clustering named FCBWTS. The major objective of scheduling is to minimize makespan of the precedence constrained applications, which can be modeled as a directed acyclic graph. In FCBWTS, the resource characteristics of cloud computing are considered, a group of characteristics, which describe the synthetic performance of processing units in the resource system, are defined in this paper. With these characteristics and the execution time influence of the ready task in the critical path, processing unit network is pretreated by fuzzy clustering method in order to realize the reasonable partition of processor network. Therefore, it largely reduces the cost in deciding which processor to execute the current task. Comparison on performance evaluation using both the case data in the recent literature and randomly generated directed acyclic graphs shows that this algorithm has outperformed the HEFT, DLS algorithms both in makespan and scheduling time consumed. Copyright © 2014 John Wiley & Sons, Ltd.