基于膜计算和蚁群算法的融合算法在云计算资源调度中的研究

针对云计算下的资源调度的问题,提出将蚁群算法的个体与云计算中的可行性资源调度进行对应,首先对云计算资源调度进行描述,其次针对蚁群算法的路径选择引入了平衡因子,对信息素进行了局部研究和全局研究,将蚁群个体引入到膜计算中,通过膜内运算和膜间运算,提高了算法的局部和全局收敛的能力,最后在云计算资源分配中,引入匹配表概念,将云计算任务和资源进行匹配,融合后的算法提高了算法的整体性能.仿真实验说明在网络消耗,成本消耗,能量消耗上有了明显的降低,提高了资源分配效率。     

云计算环境下资源调度系统设计与实现

在云计算环境下,对开放的网络大数据库信息系统中的数据进行优化调度,提高数据资源的利用效率和配置优化能力。传统的资源调度算法采用资源信息的自相关匹配方法进行资源调度,当数据传输信道中的干扰较大及资源信息流的先验数据缺乏时,资源调度的均衡性不好,准确配准度不高。提出一种基于云计算资源负载均衡控制和信道自适应均衡的资源调度算法,并进行调度系统的软件开发和设计。首先构建了云计算环境下开放网络大数据库信息资源流的时间序列分析模型,采用自适应级联滤波算法对拟合的资源信息流进行滤波降噪预处理,提取滤波输出的资源信息流的关联维特征,通过资源负载均衡控制和信道自适应均衡算法实现资源调度改进。仿真结果表明,采用资源调度算法进行资源调度系统的软件设计,提高了资源调度的信息配准能力和抗干扰能力,计算开销较小,技术指标具有优越性。     

面向SLA的云计算负载均衡策略

云计算负载均衡是保障SLA协议的关键问题之一。针对云计算负载均衡问题,提出一种面向SLA的负载均衡策略。该策略引入人工神经网络思想,建立负载均衡模型,采用单层感知器算法(SLPA)将虚拟机负载状态进行分类,然后利用结合了动态加权轮询算法的BP神经网络算法(BPNNA-DWRRA)有针对性地对虚拟机负载权重进行预测更新,最后将任务调度到最小权重所对应的可行虚拟机上。应用CloudSim进行仿真实验,结果表明了该策略的可行性,同时,相比加权最小链接算法和粒子群算法,该策略的平均响应时间分别节省了43.6%和22.5%,SLA违反率分别降低了20.7%和14.4%。因此,所提策略在响应用户任务时,请求响应时间短,SLA违反率低,保障了SLA。     

基于多维评价模型及改进蚁群优化算法的云计算资源调度策略

针对云计算环境下资源调度模型未充分考虑资源评价的问题,为更好适应不同节点计算性能和大规模数据环境的处理需求,提出了一种基于多维评价模型的虚拟机资源调度策略。首先,在云计算环境下建立包括网络性能在内的多维资源评价模型,在此基础上提出一种改进的蚁群优化算法实现资源调度策略;然后在云计算仿真平台CloudSim上进行实现。实验结果表明,该算法可以更好适应不同网络性能的计算环境,显著提高了资源调度的性能,同时降低了虚拟机负载均衡离差,满足了云计算环境下的虚拟机资源负载均衡需求。     

基于Wi-Fi与Web的云计算资源调度算法研究

为了缩短云计算执行时间,改善云计算性能,在一定程度上加强云计算资源节点完成任务成功率,需要对云计算资源进行调度。当前的云计算资源调度算法在进行调度时,通过选择合适的调度参数并利用CloudSim仿真工具,完成对云计算资源的调度。该算法在运行时有效地进行平衡负载,导致云计算资源调度的均衡性能较差,存在云计算资源调度结果误差大的问题。为此,提出一种基于Wi-Fi与Web的云计算资源调度算法。该算法首先利用自适应级联滤波算法对云计算资源数据流进行滤波降噪,然后以降噪结果为基础,采用本体论对云计算资源进行预处理操作,最后通过人工蜂群算法完成对云计算资源的调度。实验结果证明,所提算法可以良好地应用于云计算资源调度中,有效提高了云计算资源利用率,具有实用性以及可实践性,为该领域的后续研究发展提供了可靠支撑。     

基于代价优化的云工作流调度改进PSO算法

云计算可以通过即付即用的方式向用户工作流提供资源。为了解决资源服务代价异构环境下的云工作流任务调度代价问题,提出一种基于改进粒子群算法的云工作流任务调度算法WSA-IPSO。通过综合考虑任务的执行代价和依赖任务间发生数据传输时的通信代价,算法将总代价优化问题形式化为有向无环图DAG中的任务调度模型,并提出基于改进粒子群算法的优化模型对其进行求解。通过改进传统粒子群算法的粒子速度更新策略和惯性权重更新策略,算法可以以更快的收敛速度得到代价最小化的调度方案。通过仿真实验,与MCT算法及标准粒子群算法进行性能比较。实验结果表明,WSA-IPSO算法在降低总代价、任务分布的负载均衡以及算法收敛性方面比较同类算法均表现出更好的性能。     

基于改进的布谷鸟算法在云计算资源的研究

如何进行更好地资源调度一直都是云计算研究的热点,本文在云计算资源算法中引入布谷鸟算法,针对布谷鸟算法中出现的收敛速度快,容易局部震荡等现象,本文首先引入高斯变异算子来处理每一个阶段中的鸟窝最佳位置的选择,然后通过自适应动态因子来调整不同阶段中的鸟窝位置的选择,使得改进后的算法收敛精度提高,通过适应度函数的平衡以及遗传算法中的三种操作,使得本文算法能够有效的提高云计算环境下的资源分配效率,降低了网络消耗。在Cloudsim平台仿真实验中,通过三个方面的比较,本文算法在性能上、资源调度效率和任务调度方面都有很大改进,有效提高了云计算系统的资源调度能力。     

容错实时任务调度的DSPN建模与分析

复杂系统的形式化描述对新系统的设计以及现有系统的改进与评价都具有十分重要的作用。针对处理机系统容错实时混合任务调度,提出采用确定与随机Petri网(Deterministic and Stochastic Petri Net, DSPN)进行建模与性能分析。首先,根据任务执行的优先级、周期性、容错性和实时性,将任务分为四类;然后,采用DSPN对任务调度执行过程,不同优先级任务抢占式调度,处理机故障及故障恢复过程进行建模,由此构成处理机系统容错实时任务调度过程的DSPN模型;最后,仿真实验结果表明,在负载相同情况下,处理机利用率基本相同,且具有容错的实时任务调度算法可以有效地降低任务错失率。容错实时任务调度DSPN模型可以为复杂任务调度系统的Petri网建模与分析奠定了基础,并为实际工程应用提供了理论指导。     

A multi-timescale resource allocation algorithm based on self-learning for distributed fog radio access networks

Faced with limited network resources, diverse service requirements and complex network structures, how to efficiently allocate resources and improve network performances is an important issue that needs to be addressed in 5G or future 6G networks. In this paper, we propose a multi-timescale collaboration resource allocation algorithm for distributed fog radio access networks (F-RANs) based on self-learning. This algorithm uses a distributed computing architecture for parallel optimization and each optimization model includes large time-scale resource allocation and small time-scale resource scheduling. First, we establish a large time-scale resource allocation model based on long-term average information such as historical bandwidth requirements for each network slice in F-RAN by long short-term memory network (LSTM) to obtain its next period required bandwidth. Then, based on the allocated bandwidth, we establish a resource scheduling model based on short-term instantaneous information such as channel gain by reinforcement learning (RL) which can interact with the environment to realize adaptive resource scheduling. And the cumulative effects of small time-scale resource scheduling will trigger another round large time-scale resource reallocation. Thus, they constitute a self-learning resource allocation closed loop optimization. Simulation results show that compared with other algorithms, the proposed algorithm can significantly improve resource utilization.     

基于遗传算法的云计算资源调度策略研究

对云计算环境中的资源调度问题进行了研究,鉴于当前云计算环境中资源利用率不高,节点负载不均衡的问题,提出了一种新的基于遗传算法的关于虚拟机负载均衡的调度策略。根据历史数据和系统的当前状态以及通过遗传算法,该策略能够达到最佳负载均衡和减少或避免动态迁移,同时还引入了平均负载来衡量该算法的全局负载均衡效果。最后通过在CloudSim平台进行仿真实验,结果表明,该策略具有相当好的全局收敛性和效率,当系统虚拟机被调度之后,算法在很大程度上能够解决负载不均衡和高迁移成本问题,并且极大地提高了资源利用率。     

基于接纳控制和ILP资源调度模型的BDAaaS系统架构

为了使云计算平台为大数据分析提供有效支持,提出一种大数据分析即服务(BDAaaS)的系统架构;首先,当用户向系统提交大数据分析应用(BDAA)时,通过接纳控制器评估任务的执行时间和成本并作出接纳决策;然后,通过服务等级协议(SLA)管理器根据任务的服务质量(QoS)需求制定SLA;最后,利用提出的整数线性规划(ILP)资源调度模型,以最小化执行成本为目标,在满足SLA下合理调度资源来执行任务;仿真结果表明,提出的方案能够有效降低任务执行时间,具有有效性和可行性。     

Profit maximization in cache-aided intelligent computing networks

This paper focuses on the profit maximization problem in a reconfigurable intelligent surfaces (RIS) aided computing network, where multiple heterogeneous users offload their computational tasks to one computational access point (CAP) for seeking computing acceleration at the cost of profit. In particular, the CAP can also pre-store a part of the computing task to speed up computing, and the system has limited communication and computing resources, where heterogeneous users have different offloading requirements and the CAP can dynamically allocate the system resources to meet the requirements of users to earn profits. To maximize the system profit, we devise the system by proposing a resource allocation scheme which employs a genetic algorithm (GA), based on statistical channel state information (CSI) of wireless links. The proposed algorithm maximizes the long-term profit of the system by optimizing resource allocation among users. Finally, simulation results are provided to verify the proposed scheme. The results show that our proposed resource allocation scheme outperforms the conventional ones.     

基于MPI和OpenMP的三维弹性波方程混合并行有限差分算法*

三维弹性波方程有限差分模拟具有大计算量和大内存消耗的特点,在常规计算机上使用传统算法往往无法满足计算要求。该文以高性能计算机集群为平台,基于MPI和OpenMP混合编程技术,构建了一种新型三维弹性波方程并行有限差分算法。该算法基于MPI将总任务分配给多个进程,同时在每个进程中基于OpenMP将子任务分配给多个线程。各个进程具有独立的内存空间,各个线程共享所在进程的内存空间。充液井孔声场的数值模拟结果表明,与基于OpenMP的并行有限差分算法相比,基于MPI和OpenMP的混合并行有限差分算法可以利用计算机集群的多个节点进行并行计算,既极大地提高了计算速度,又有效地降低了单个节点的内存消耗。     

The Application of Fractal Transform and Entropy for Improving Fault Tolerance and Load Balancing in Grid Computing Environments

This paper applies the entropy-based fractal indexing scheme that enables the grid environment for fast indexing and querying. It addresses the issue of fault tolerance and load balancing-based fractal management to make computational grids more effective and reliable. A fractal dimension of a cloud of points gives an estimate of the intrinsic dimensionality of the data in that space. The main drawback of this technique is the long computing time. The main contribution of the suggested work is to investigate the effect of fractal transform by adding R-tree index structure-based entropy to existing grid computing models to obtain a balanced infrastructure with minimal fault. In this regard, the presented work is going to extend the commonly scheduling algorithms that are built based on the physical grid structure to a reduced logical network. The objective of this logical network is to reduce the searching in the grid paths according to arrival time rate and path’s bandwidth with respect to load balance and fault tolerance, respectively. Furthermore, an optimization searching technique is utilized to enhance the grid performance by investigating the optimum number of nodes extracted from the logical grid. The experimental results indicated that the proposed model has better execution time, throughput, makespan, latency, load balancing, and success rate.     

大数据云中心虚拟机资源高效分配应用研究

为降低大数据云中心的能量消耗和实现资源的优化配置,提出一种虚拟机资源高效分配策略。 提出的策略对选定的特征上具备相似性任务分组的聚类进行定义,将各组任务映射到定制化的高效虚拟机类型。其高效指的是以最低限度的资源损耗成功执行任务。虚拟机的相关参数为核数量、内存量和存储量。虚拟机分配基于日志中提取的历史数据,并以任务的使用模式为基础。提出的资源分配策略以任务的实际资源使用量为基础,实现了能源消耗的降低。实验结果表明：不同聚类任务下,提出的虚拟机资源分配策略可以大幅节约能源消耗,具有较低的平均任务拒绝次数。     

Hadoop平台下自响应故障感知的检测技术研究实现

当今云计算环境下,Hadoop已经成为大数据处理的事实标准。然而云计算具有大规模、高复杂和动态性的特点,容易导致故障的发生,影响Hadoop上运行的作业。虽然Hadoop具有内置的故障检测和恢复机制,但云环境中不同节点负载大小的变化,被调度的作业仍然导致失败。针对此问题提出自响应故障感知的检测调度方法,对异构环境负载能力的不同,而做出服务器快节点和慢节点的判断,把作业分配调度到合适的节点上执行,调整任务决策来尽可能的防止任务失败的发生。最后在Hadoop框架下与基本调度器进行实验性能比较,结果显示该方法减少作业失败率最高达19%,并缩短了作业执行时间,同时也减少CPU和内存的使用。     

Progressive Traffic-Oriented Resource Management for Reducing Network Congestion in Edge Computing

Edge computing can deliver network services with low latency and real-time processing by providing cloud services at the network edge. Edge computing has a number of advantages such as low latency, locality, and network traffic distribution, but the associated resource management has become a significant challenge because of its inherent hierarchical, distributed, and heterogeneous nature. Various cloud-based network services such as crowd sensing, hierarchical deep learning systems, and cloud gaming each have their own traffic patterns and computing requirements. To provide a satisfactory user experience for these services, resource management that comprehensively considers service diversity, client usage patterns, and network performance indicators is required. In this study, an algorithm that simultaneously considers computing resources and network traffic load when deploying servers that provide edge services is proposed. The proposed algorithm generates candidate deployments based on factors that affect traffic load, such as the number of servers, server location, and client mapping according to service characteristics and usage. A final deployment plan is then established using a partial vector bin packing scheme that considers both the generated traffic and computing resources in the network. The proposed algorithm is evaluated using several simulations that consider actual network service and device characteristics.     

基于多任务管理系统的高清视频处理

提出了一种基于多任务管理系统的高清视频处理技术,具有提升高清视频处理实时性,优化计算资源利用率,降低高清视频处理应用设计难度的特点。首先,介绍了面向异构多核计算环境的多任务管理系统,用于多种类型任务的调度执行及计算资源的负载均衡。在此基础上,设计了一种软件流水线,将对于高清视频的复杂而重复的处理过程分解成多类型的任务,提交至多任务管理系统。最后,对基于多任务管理系统的高清视频处理技术进行了实验验证。结果表明,异构多核环境下,高清视频处理的计算性能提升了3.7倍。     

异构环境下基于双重预取的Hadoop调度算法

Hadoop处理海量数据时,无论是Map任务还是Reduce任务都需要耗费大量的时间传输数据,故提出一种基于双重预取的调度算法;该算法通过估算节点上任务执行的进度来预测Map任务的执行节点,然后通知节点提前预取所需的数据,并且在Map任务完成的数量达到预定值时,开始为Reduce任务预取部分数据;由于在异构的环境下集群中节点的性能各不相同,为此采取了改进的预测模型,以提高任务进度判断的准确性;实验证明,本算法在作业响应时间等方面优于现有的调度算法。     

Computation bits maximization in backscatter-aided wireless powered MEC using binary offloading

This article considers a backscatter-aided wireless powered mobile edge computing (BC-aided WPMEC) network, in which the tasks data of each Internet of Things (IoT) device can be computed locally or offloaded to the MEC server via backscatter communications, and design a resource allocation scheme regarding the weighted sum computation bits (WSCB) maximization of all the IoT devices. Towards this end, by optimizing the mobile edge computing (MEC) server’s transmit power, IoT devices’ power reflection coefficients, local computing frequencies and time, the time allocation between the energy harvesting and task offloading, as well as the binary offloading decision at each IoT device, we built a WSCB maximization problem, which belongs to a non-convex mixed integer programming problem. For solving this, the proof by contradiction and the objective function’s monotonicity are considered to determine the optimal local computing time of each IoT device and the optimal transmit power of the MEC server, and the time-sharing relaxation (TSR) is adopted to tackle the integer variables, which are used to simplify the original problem. Then, we decouple the simplified problem into two sub-problems by means of the block coordinate decent (BCD) technology, and each of the sub-problems is transformed to a convex one by introducing auxiliary variables. Based on this, we design a two-stage alternative (TSA) optimization algorithm to solve the formulated WSCB problem. Computer simulations validate that the TSA algorithm has a fast convergent rate and also demonstrate that the proposed scheme achieves a higher WSCB than the existing schemes.