PARALLEL ADAPTIVELY MODIFIED CHARACTERISTIC BASIS FUNCTION METHOD BASED ON STATIC LOAD BALANCE

Characteristic Basis Function Method (CBFM) is a novel approach for analyzing the ElectroMagnetic (EM) scattering from electrically large objects. Based on dividing the studied object into small blocks, the CBFM is suitable for parallel computing. In this paper, a static load balance parallel method is presented by combining Message Passing Interface (MPI) with Adaptively Modified CBFM (AMCBFM). In this method, the object geometry is partitioned into distinct blocks, and the serial number of blocks is sent to related nodes according to a certain rule. Every node only needs to calculate the information on local blocks. The obtained results confirm the accuracy and efficiency of the proposed method in speeding up solving large electrical scale problems.

A New Clustering Algorithm Using Adaptive Discrete Particle Swarm Optimization in Wireless Sensor Network

Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one of the methods that can expand the lifespan of the whole network by grouping the sensor nodes according to some criteria and choosing the appropriate cluster heads(CHs). The balanced load of the CHs has an important effect on the energy consumption balancing and lifespan of the whole network. Therefore, a new CHs election method is proposed using an adaptive discrete particle swarm optimization (ADPSO) algorithm with a fitness value function considering the load balancing and energy consumption. Simulation results not only demonstrate that the proposed algorithm can have better performance in load balancing than low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), and dynamic clustering algorithm with balanced load (DCBL), but also imply that the proposed algorithm can extend the network lifetime more.

移动边缘计算不确定性任务持续卸载及资源分配方法

移动边缘计算场景中任务的不确定性增加了任务卸载及资源分配的复杂性和难度.鉴于此,提出一种移动边缘计算不确定性任务持续卸载及资源分配方法.首先,构建一种移动边缘计算不确定性任务持续卸载模型,通过基于持续时间片划分的任务多批次处理技术应对任务的不确定性,并设计多设备计算资源协同机制提升对计算密集型任务的承载能力.其次,提出一种基于负载均衡的自适应策略选择算法,避免计算资源过度分配导致信道拥堵进而产生额外能耗.最后,基于泊松分布实现了对不确定任务场景模型的仿真,大量实验结果表明时间片长度减小能够降低系统总能耗.此外,所提算法能够更有效地实现任务卸载及资源分配,相较于对比算法,最大可降低能耗11.8%.

高分三号数据分布式自适应负载均衡并行转换算法研究

针对PolSARpro软件自带高分三号数据转换模块无法自适应分布式差异化硬件环境问题,提出了一种按照该软件的数据格式要求进行分布式自适应负载均衡并行转换算法,将高分三号多极化数据快速、精确地进行定标与格式转换,对不同硬件配置环境自适应,能够抗数据偏斜,并且通过KingMap V10.5平台实现了算法并在实际数据中进行测试,试验结果表明性能提升了约12.7倍,验证了算法的可行性、高效性和正确性。

面向异构处理平台任务调度的麻雀优化算法

针对当前异构信号处理平台中各处理器任务数量分配不均衡、处理器性能发挥不完全以及系统运行效率低的问题,文中提出一种面向异构处理平台的麻雀优化算法。该算法利用了麻雀算法较强的全局寻优能力和麻雀种群内部的高效工作机制。在经典麻雀算法基础上,文中提出了符合任务调度的二进制异或编解码规则,将离散的任务分配方案映射为连续的麻雀位置信息。将处理器负载均衡指数作为适应度函数,选取每次迭代中的最优解;在麻雀遍历任务时,采用任务优先级分流排序策略。对通信密集型任务和计算密集型任务采取不同的计算式得到更符合任务特点的遍历顺序,生成随机任务图,并将所提算法同ICPA(Improved Critical Path Algortthm)算法进行对比。仿真结果表明,相比于ICPA算法,所提算法的负载均衡指数平均优化率为60%,各处理器负载情况更加均衡,能更好地发挥异构处理平台的整体效能。

电能表数据采集终端负载自适应均衡方法研究

针对电能表数据采集终端数据量及访问量高导致的负载失衡问题,研究基于元模型的电能表数据采集终端负载自适应均衡方法。将电能表数据采集终端的电能表数据作为模型元数据,经数据提取、转化、加载处理后,将处理完成的数据存储至元数据仓库内。在元数据负载大小未知条件下,创新性地采用基本负载均衡算法分配元数据仓库中元数据的负载,以求解元数据的负载量。当负载过大时,通过迁移负载低节点的目录子树,均衡处理了元数据的负载量,实现了电能表数据采集终端访问负载均衡。实例测试结果表明,该方法应对速度快。该方法降低了电能表数据采集终端负载,解决了元数据请求激增问题,提高了集群负载的均衡程度。

Status-Aware Resource Adaptation in Information-Centric and Software-Defined Network

In recent years, realising a ＂clean- state＂ design for the future Internet has become an important research focus. An architecture com- bining an Information-Centric Network （ICN） and Sottware-Defmed Network （SDN） （IC-SDN） has gradually attracted more attention. How- ever, the existing studies regarding IC-SDN still lack support in terms of the ＂network status awareness＂ function, resulting in unreasonable resource allocation. In this paper, we propose a new status-aware resource adaptation sche- me, i.e. a status-aware module is embedded into basic elements （Forwarding Node （FN） and Resource adaption Manager （RM））. The FNs collect the network status dynamically for the controller to reallocate network resources accor- ding to the fluctuations in environmental con- ditions. Simulation results show that, compared with the existing IC-SDN mechanism, the pro- posed scheme reduced the link bandwidth var- iance by 56% and the content delivery latency by 40%. The proof-of-concept implementation demonstrates the feasibility of our proposed sc- heme for small-scale deployment.

An adaptive dynamic feedback load balancing algorithm based on QoS in distributed file system

An adaptive dynamic load balancing algorithm based on QoS is proposed to improve the performance of load balancing in distributed file system,combining the advantages of a variety of load balancing algorithms.The new algorithm uses a tuple containing the number of files and the total file size as the QoS measure for the requested task.The master node sets a threshold for the requested task based on the QoS to filter storage nodes that meet the requirements of the task.In order to guarantee the reliability of the new algorithm,we consider the impact of CPU utilization,memory usage,disk IO occupancy rate,network bandwidth usage and hard disk usage on load balancing performance when calculating the real-time load balancing of storage nodes.The heterogeneity of the network is considered when the master node schedule task assignments to ensure the fairness of the algorithm.The comprehensive evaluation value is determined based the performance load ratio,which is calculated from the real-time load value of the storage node and a performance value after normalization.The master node assigns tasks to the storage node with the highest comprehensive evaluation value.The storage nodes provide adaptive feedback based on changes in the degree of connectivity,rather than periodic update of the load information.The actual distributed file system environment is set up on the server cluster,the performance of the new algorithm is tested through a contrast experiment.The experimental results show that the new algorithm can effectively reduce the average response time of the system,improve throughput,and enable the system load to reach a good balance.

Adaptive and scalable load balancing for metadata server cluster in cloud-scale file systems

Big data is an emerging term in the storage indus- try, and it is data analytics on big storage, i.e., Cloud-scale storage. In Cloud-scale （or EB-scale） file systems, load bal- ancing in request workloads across a metadata server cluster is critical for avoiding performance bottlenecks and improv- ing quality of services. Many good approaches have been pro- posed for load balancing in distributed file systems. Some of them pay attention to global namespace balancing, making metadata distribution across metadata servers as uniform as possible. However, they do not work well in skew request dis- tributions, which impair load balancing but simultaneously increase the effectiveness of caching and replication, in this paper, we propose Cloud Cache （C2）, an adaptive and scal- able load balancing scheme for metadata server cluster in EB-scale file systems. It combines adaptive cache diffusion and replication scheme to cope with the request load balanc- ing problem, and it can be integrated into existing distributed metadata management approaches to efficiently improve their load balancing performance. C2 runs as follows： 1） to run adaptive cache diffusion first, if a node is overloaded, load- shedding will be used; otherwise, load-stealing will be used; and 2） to run adaptive replication scheme second, if there is a very popular metadata item （or at least two items） causing a node be overloaded, adaptive replication scheme will be used,in which the very popular item is not split into several nodes using adaptive cache diffusion because of its knapsack prop- erty. By conducting performance evaluation in trace-driven simulations, experimental results demonstrate the efficiency and scalability of C2.

基于共享介质争用访问机制的电采暖设备自适应控制方法

“煤改电”背景下大量电采暖设备的无序接入,极易造成尖峰负荷、引起电压波动甚至越限,影响电能质量与用电安全。针对这一问题,提出了基于共享介质争用访问机制的电采暖设备自适应控制方法。通过刻画电采暖功率变化与区域内电压分布关系函数,分析了各节点电压波动的关键影响因素。以此为基础,构建了以家庭为最小控制单元的电采暖设备自适应控制方法实施架构:采用自组织映射(SOM)神经网络得到家庭近期活跃的电采暖设备数量,进而借鉴通信网络中信道访问与数据传输冲突监测机制,使电采暖设备通过实时采集本地环境信息和电气信息进行自主控制和冲突规避,实现了无需通信的自适应控制。仿真结果验证了所提控制方法在减小用电尖峰负荷、平抑负荷曲线及抑制电压越限方面的有效性以及在实时控制方面的优越性。

自适应ANP CRITIC的无人机集群网络优化算法

针对簇首选取过程中的指标权重适应性差与分簇规模差异性大等问题,提出一种基于自适应ANP CRITIC的无人机集群网络优化算法。结合无人机集群的分簇规模与节点能耗,通过节点连通度、节点负载失衡度、节点平均通信距离、节点剩余能量和节点相对移动性五个因素指标,设计ANP CRITIC自适应机制,实现不同编队飞行场景下各因素指标主客观权重的求解;引入博弈论的思想修正主客观权重,根据簇首权值选取最优簇首,并对文中算法性能进行仿真和分析。仿真结果表明,相较于LID算法、WCA算法和IWCA算法,文中算法的簇首数量和簇内平均通信距离控制在较优范围;在网络中节点失效率达到1%,20%,50%的情况下,文中算法无人机集群网络寿命分别延长约99.36%,47.97%和12.85%;簇内节点间通信质量分别提升约425%,147%和31%。结论可为无人机集群网络管理优化提供理论参考。

基于自适应遗传算法的无线激光通信网络负载均衡成簇算法

由于无线激光通信网络吞吐量低、节点传输延时高和存在分组丢失率多等问题,提出基于自适应遗传算法的无线激光通信网络负载均衡成簇算法。利用AGCH算法对无线激光通信网络节点进行分组和成簇,从中取得簇头节点,构建资源调度模型,利用该模型对簇头节点中的资源进行分配调度,采用自适应遗传算法对建立的模型进行求解,以此提升无线激光通信网络负载均衡效果,实现无线激光通信网络负载均衡。实验结果表明,通过对该算法进行网络吞吐量测试、节点传输延时测试和分组丢失率测试,验证了该算法的有效性强、实用性高。

可穿戴电子设备通信终端自适应负载均衡系统设计

当前使用的通信终端负载均衡系统因缺乏移动性和自主性,无法在动态环境中达到稳定通信的效果,为了解决该问题,设计了可穿戴电子设备通信终端自适应负载均衡系统。使用虚拟化服务器,将物理服务器虚拟成若干个服务实例,获得控制权限。利用编码器信号采集装置采集编码器信号,设计负载均衡硬件设备。通过探针监测、接收和计时触发模块,获取反馈的采集指令。计算等待响应的请求百分比,根据负载均衡处理需求判断结果,计算负载信息更新数量,设计负载信息更新流程,完成通信终端的负载均衡处理。由实验结果可知,该系统在静态环境下的2段号提取时间比实际时间少30 ms,在动态环境下的1段号提取时间比实际时间少14 ms,能够达到稳定通信的效果。

基于云计算技术的教育信息化管理系统

教育资源配置和管理手段存在不足,导致教育资源存在资源分配不均衡、共享程度低、资源调度配准程度差等问题,为此构建基于云计算技术的教育信息化管理系统,合理、准确完成教育资源调动,提高教育信息化管理效果。并通过基于云计算资源负载均衡控制和信道自适应均衡实现教育资源调度,完成教育信息化管理。测试结果表明:该系统衰减量较低、负载均衡度较好,可高效完成教育资源调度,并且教育资源的调动配准度为98%以上,可准确完成教育资源的匹配调度,提升教育信息化管理效果。

基于改进鲸鱼优化算法的资源调度策略

传统云计算在资源调度中存在效率低、计算时间分配不合理的问题;鲸鱼算法(WOA)存在收敛能力不足、容易陷入局部最优等问题。对此提出一种改进的WOA-3资源调度策略。该策略对WOA三个阶段优化,并对WOA-3和鲸鱼算法等分别进行性能优化和资源调度实验。性能优化实验表明WOA-3算法比WOA、PSO和GWO算法具有更好的收敛速度和精度;在资源调度实验中,改进的WOA-3算法相较于WOA和PSO算法分别提升了5.87%和9.76%。

考虑带宽限制的网络通信负载自适应均衡方法

为了提高通信网络负载均衡性,提出考虑带宽限制的网络通信负载自适应均衡方法。对网络通信流量信息进行监测,利用小波变换算法对监测流量信息展开去噪预处理,采用分簇聚类算法进行聚类处理;利用采样周期抽头间隔采样模式构建通信链路信道传输模型,以负载自适应均衡为优化目标,构建负载自适应均衡模型,并考虑带宽限制,设置约束条件;采用灰狼算法求解带宽约束下的负载自适应均衡模型,实现网络通信负载自适应均衡。测试结果表明:该方法的均衡指数高于0.9,网络通信时延低于1.87 ms,网络通信吞吐量达到2900kb,有效提高了通信网络负载均衡性。

一种面向会话的自适应负载均衡算法

在论述负载均衡技术相关工作的基础上,基于IP报文头多域分类方法,提出自适应负载均衡算法MSF(minimum sessions first),通过动态调整TCP流数目最少的流束,能够在各处理节点间保持动态负载均衡的同时维持会话的完整性.模拟结果表明,MSF算法具有设计简洁、负载均匀度好、重映射破坏度小、会话完整性破坏度小等优点,对不同负载具有良好的综合性能.该算法已经成功地应用在国防科学技术大学计算机学院研制的高速网络安全设备中,在保持较好的负载均衡效果的前提下保证了会话的完整性,提高了网络安全设备的性能.

对象存储系统中自适应的元数据负载均衡机制

面向对象的存储系统在研究、工程以及服务领域均得到了广泛的应用.在面向对象的存储系统中,元数据的负载均衡对于提高整个系统的I/O性能具有重要的作用.现有的元数据负载均衡策略不能动态地平衡元数据的访问负载,而且自适应性以及容错特性有待提高.提出了一种自适应的分布式元数据负载均衡机制(adaptabledistributed load balancing of metadata,简称ADMLB),包含基本的负载均衡算法和分布式的增量负载均衡算法.采用基本的负载均衡算法按照服务器的性能公平地分布负载,使用分布式的负载均衡算法定时地调整负载的分布.ADMLB采取分布式的方法均衡地在元数据服务器之间分布负载,根据负载的变化自适应地进行调整,具有很好的容错特性,而且用户可以高效地定位元数据服务器.