共查询到19条相似文献,搜索用时 62 毫秒
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针对虚拟机放置问题,引入了布谷鸟搜索算法。首先,将虚拟机放置方案映射为鸟巢,并按照适应度高低将其分成顶巢和底巢。其次,通过扰动函数对底巢和顶巢进行扰动。最后,通过选择、迭代得到最佳放置方案。该算法可用于云数据中心的物理机整合,使放置物理机数量最小化。通过Cloudsim进行仿真,仿真结果表明,比起重排序分组遗传算法、分组遗传算法、改进的最小加载和改进的降序首次适应算法,提出的方法不仅避免了局部最优,而且具有更高的性能优势。 相似文献
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针对现有采用粒子群算法放置虚拟机问题存在优化目标单一,容易陷入局部最优解的问题,提出一种基于改进粒子群算法的虚拟机放置方法,通过综合考虑数据中心类型对优化侧重的影响和放置后的负载均衡度,鲁棒性,能源消耗,针对计算密集型数据中心和数据密集型数据中心设计不同的目标函数,对负载均衡度,鲁棒性,能源消耗3方面进行了多目标优化,使其更贴近实际情况。并且通过优化粒子初始位置调节收敛速度,设置动态的学习因子和引入粒子位置的漂移因素使其跳出局部最优解。仿真实验证明,本方法和粒子群算法,模拟退火算法比较,在负载均衡,鲁棒性,能源消耗三个方面都有不同程度的性能提升。 相似文献
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虚拟机的调度(放置)问题(Virtual Machine Placement, VMP)是云计算中的一个重要研究课题。它指的是使用特定的调度算法,将虚拟机放置到合适的主机上的过程,是一个典型的NP问题。目前的研究大量的使用了启发式的多目标优化算法,这类方法考虑了调度的目标与限制。针对满足目标和限制的条件下依旧会有多种解决方案的情况,引入了用户偏好表达的调度策略。具体的做法,首先,使用CP-nets定性地描述用户对虚拟机调度的偏好;其次,处理了多个用户偏好以及他们之间的冲突问题,最后,将元启发式算法得出的Pareto解进行偏好处理,返回满足用户偏好的最佳调度方案。实例分析和实验的结果表明,考虑用户偏好的虚拟机调度方案极大的提升了用户的满意程度。 相似文献
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基于求解TSP问题,提出一种离散型萤火虫群优化(DGSO)算法,该算法结合TSP问题特点,给出一种有效编码和解码方法,并定义适合编码的个体间距离计算公式和编码更新公式.同时,为增强算法求解TSP问题的局部搜索能力,加快算法的收敛速度,算法使用了操作简单的2-Opt优化算子.最后,通过对10个TSP问题进行仿真实验,实验结果表明本文提出的算法是在种群规模较小,迭代次数较少的情况下就可以收敛到已知最优解.在大规模TSP算例中算法获得的最优值与理论最优值的误差也在1%以下. 相似文献
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在云数据中心(IDC),虚拟机部署(VMP)策略是指如何在数据中心有限的物理资源中内放置合理的虚拟机(VM)。高效的虚拟机部署策略将更好的实现物理资源的整合和利用,最大化实现资源利用和能源节约的效果。该文中,根据虚拟机的存储和CPU两种资源为目标,研究资源利用率最大化的虚拟机放置策略。该策略基于遗传算法,根据虚拟机实际资源(内存和CPU)需求动态,动态的为虚拟机分配资源,实现最大限度降低数据中心资源利用不足和过度利用等概率。在最后,使用CloudSim仿真软件进行模拟实验,通过实验证明,较最佳拟合递减算法(BFD)相比,使用遗传算法对虚拟机进行动态分配,数据中心的资源利用率有较大的提升,同时也说明将多资源需求作为虚拟机放置策略考虑的重要性。 相似文献
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针对云数据中心数据获取效率低和服务器资源浪费问题,为优化云平台的数据访问和资源利用,文中提出了一种时延优化的云数据中心数据放置(LOP)方法。文中首先分析了云平台的性能,建立了云平台的资源利用和数据获取时间模型。然后基于非支配排序算法NSGA-Ⅲ实现了全局最优的数据放置策略,对数据资源进行合理部署,有效利用服务器的资源,提高了数据获取的效率。最后通过CloudSim仿真平台,对提出的数据放置方法进行了仿真和对比实验。实验结果表明,LOP方法能明显提高云服务器的资源利用率,缩短任务的数据获取时间。 相似文献
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为了充分实现中继协作,降低多中继协作通信系统功率分配优化问题的计算复杂度,提出了基于萤火虫算法的多中继功率分配方案。在一定的总功率和节点功率约束下,以最大化平均信噪比为优化目标函数,建立了多中继协作系统的功率分配最优化模型。选取该目标函数作为萤火虫的适应度函数,用向量表示萤火虫的状态,该向量的维数为待分配源节点和中继节点的个数,通过萤火虫聚集得到种群中最好的萤火虫,即可获得渐进最优功率分配。仿真结果表明,与平均功率分配相比,基于萤火虫算法的功率分配方案能降低2.44%~6.17%的比特差错率,提高了系统性能。 相似文献
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在基于多阈值的脑,CT图像分割算法中,最佳阈值选取是脑CT图像中的关键,针对传统多阈值法的阈值选择难题为了提高脑。CT图像的分割准确率,提出一种萤火虫群算法优化多阈值的脑CT图像分割方法首先建立了基于多阈值法的脑图像分割数学模型,然后通过萤火虫群算法数学模型进行求解,搜索到脑CT图像分割的最佳阈值,CT最后采用最佳阈值完成脑CT图像的分割。仿真结果表明,萤火虫群算法提高了脑CT图像的精度,获得了更加理想的脑CT图像结果。 相似文献
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In order to alleviate the harm of co-location attack in cloud environment,a virtual machine placement strategy based on Shamir was proposed,and a virtual machine placement framework was designed.The security of key data was ensured by the blockchain in the proposed placement strategy.The virtual machine placement policy could effectively improve the security of the virtual machine and the load balancing capability of the cloud environment,and reduce resource waste.Finally,the effectiveness of the scheme is proved by simulation experiments. 相似文献
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应用粒子群优化的绿色虚拟网络映射算法 总被引:1,自引:0,他引:1
绿色网络是近年来网络技术研究的热点,以节能为优化目标的虚拟网络映射算法成为基于网络虚拟化技术中资源分配研究的重点.提出了应用粒子群优化的绿色虚拟网络映射算法,重定义粒子群优化算法中的参数和粒子进化行为,以关闭底层网络节点和链路数量最多为适应度函数,在较低的算法复杂度条件下,获得绿色虚拟网络映射的最优解.仿真结果表明,与对比算法相比,静态环境下所提算法的运行时间大幅度降低;动态环境下所提算法的节点关闭率、链路关闭率、虚拟网络请求接受率均有所提升,算法运行时间也大大缩短. 相似文献
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FGWSO‐TAR: Fractional glowworm swarm optimization for traffic aware routing in urban VANET
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In mobile distributed applications, such as traffic alert dissemination, dynamic route planning, file sharing, and so on, vehicular ad hoc network (VANET) has emerged as a feasible solution in recent years. However, the performance of the VANET depends on the routing protocol in accord with the delay and throughput requirements. Many of the routing protocols have been extensively studied in the literature. Although there are exemptions, they escalate research challenges in traffic aware routing (TAR) protocol of VANET. This paper introduces the fractional glowworm swarm optimization (FGWSO) for the TAR protocol of VANET in an urban scenario that can identify the optimal path for the vehicle with less traffic density and delay time. The proposed FGWSO searches the optimal routing path based on the fitness function formulated in this paper. Fractional glowworm swarm optimization is the combination of the GWSO and fractional theory. Moreover, exponential weighted moving average is utilized to predict the traffic density and the speed of the vehicle, which is utilized as the major constraints in the fitness function of the optimization algorithm to find the optimal traffic aware path. Simulation of FGWSO shows the significant improvement with a minimal end‐to‐end delay of 6.6395 seconds and distance of 17.3962 m, respectively, in comparison with the other existing routing approaches. The simulation also validates the optimality of the proposed TAR protocol. 相似文献
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Mostafa Ghobaei‐Arani Ali Asghar Rahmanian Mahboubeh Shamsi Abdolreza Rasouli‐Kenari 《International Journal of Communication Systems》2018,31(8)
In recent years, the increasing use of cloud services has led to the growth and importance of developing cloud data centers. One of the challenging issues in the cloud environments is high energy consumption in data centers, which has been ignored in the corporate competition for developing cloud data centers. The most important problems of using large cloud data centers are high energy costs and greenhouse gas emission. So, researchers are now struggling to find an effective approach to decreasing energy consumption in cloud data centers. One of the preferred techniques for reducing energy consumption is the virtual machines (VMs) placement. In this paper, we present a VM allocation algorithm to reduce energy consumption and Service Level Agreement Violation (SLAV). The proposed algorithm is based on best‐fit decreasing algorithm, which uses learning automata theory, correlation coefficient, and ensemble prediction algorithm to make better decisions in VM allocation. The experimental results indicated improvement regarding energy consumption and SLAV, compared with well‐familiar baseline VM allocation algorithms. 相似文献
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《信息技术》2017,(10):141-145
支持向量机(SVM)在雷达目标高分辨距离像(HRRP)识别中可获得较高的正确识别率和更好的泛化性能,然而其性能很大程度上取决于其参数包括核函数参数σ2和惩罚因子C的合理选择。所以利用粒子群优化算法(PSO)全局搜索能力强的优点来搜寻最优参数,并针对粒子群优化易陷入局部最优的问题,提出一种惯性权重自适应改变的改进方法。通过对雷达目标高分辨率距离像(HRRP)的识别实验发现,利用PSO优化SVM参数的方法克服了传统SVM存在的很难精确找到最优参数的缺点,识别准确率也有很大提高;同时惯性权重自适应改变的方法也有效解决了PSO优化的"早熟"问题,大大缩短参数寻优时间。 相似文献
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Sayyid Shahab Nabavi Sukhpal Singh Gill Minxian Xu Mohammad Masdari Peter Garraghan 《International Journal of Communication Systems》2022,35(1):e4747
Technology providers heavily exploit the usage of edge-cloud data centers (ECDCs) to meet user demand while the ECDCs are large energy consumers. Concerning the decrease of the energy expenditure of ECDCs, task placement is one of the most prominent solutions for effective allocation and consolidation of such tasks onto physical machine (PM). Such allocation must also consider additional optimizations beyond power and must include other objectives, including network-traffic effectiveness. In this study, we present a multi-objective virtual machine (VM) placement scheme (considering VMs as fog tasks) for ECDCs called TRACTOR , which utilizes an artificial bee colony optimization algorithm for power and network-aware assignment of VMs onto PMs. The proposed scheme aims to minimize the network traffic of the interacting VMs and the power dissipation of the data center's switches and PMs. To evaluate the proposed VM placement solution, the Virtual Layer 2 (VL2) and three-tier network topologies are modeled and integrated into the CloudSim toolkit to justify the effectiveness of the proposed solution in mitigating the network traffic and power consumption of the ECDC. Results indicate that our proposed method is able to reduce power energy consumption by 3.5% while decreasing network traffic and power by 15% and 30%, respectively, without affecting other QoS parameters. 相似文献