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冲突分解算法是一种能够有效改善无线信道多址接入协议性能的方式.传统的冲突分解算法包括树形分解算法和先到先服务的冲突分解算法,但是这两种分解算法都存在着一定的不足.本文提出了一种新型的混合型冲突分解算法(Hybrid Splitting Algorithm-HSA),HSA算法继承了树形分解算法和先到先服务冲突分解算法的优点,不仅考虑了碰撞分组的产生时间,使先产生的分组先得到服务,同时当系统中存在产生间隔比较接近的分组时,采用树形分解算法,使整个分解过程不再仅仅局限于分组的产生时间,从而有效地减少了分解所需的总时隙数,提高了冲突分解算法的性能.理论分析和仿真结果显示HSA算法是一种正确可行的算法. 相似文献
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重点介绍了MIMO-OFDM系统中基于QR分解的几种信号检测算法,分析了各种算法的优缺点;指出信号检测顺序是降低误差传播的关键。基于改进的Gram-Schmidt正交排序QR检测算法用迭代运算代替矩阵求逆运算,有效地改进了传统算法的缺点,降低了计算量,使系统在复杂度和性能之间取得了良好的折中,并在最后对该算法与MMSE准则联合的算法进行了介绍。 相似文献
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本文提出了一种改进的非负矩阵分解语音增强算法,该算法可分为训练和增强两部分。首先,为了降低训练复杂度,采用卷积非负矩阵分解只提取噪声字典。增强时,考虑语音信号稀疏性比噪声信号稀疏性强,通过稀疏非负矩阵分解重构出语音幅度谱,采用交替方向乘子法进行优化迭代,克服了经典乘性迭代易陷入局部最优、分母只能收敛到零极限等问题。最后,基于算法融合的思想,将重构的语音幅度谱与谱减法、最小均方误差幅度谱估计得到的幅度谱进行加权融合。仿真实验中,在10种不同噪声环境中,通过多种评价标准证明所提算法能取得较好的增强效果。 相似文献
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传统稀疏分解算法正交匹配追踪(OMP)算法里采用内积最大值来寻找最优原子,该方法容易陷入局部最优,为了弥补这一缺点,采用了新的算法:A*OMP算法,该算法使用A*搜索(即最佳优先搜索技术)寻找最优原子,该搜索方式寻找的最优原子具有全局最优性。实验表明相比传统OMP算法而言,该算法有效地提高了信号的重构精度。 相似文献
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云计算中主机和任务的数量都是十分庞大的,如何通过任务分配调度来减少成本开销和降低能耗是当前云计算和绿色计算领域研究的热点问题。根据云计算任务以及运行环境的特点,将云计算任务分配问题抽象为多维多背包求解问题,并采用改进的混合遗传算法对该问题进行求解。实验结果表明,改进的混合遗传算法能够在较短的时间内找到问题的优化解,并且根据该算法实现的任务分配策略能够有效地减少任务执行的成本开销和能耗。 相似文献
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为了提高云计算服务集群资源调度和任务分配的优化效果,提出一种基于改进的人工蜂群优化算法的云计算资源调度策略。针对ABC算法后期收敛速度慢,容易陷入局部最优的问题,引入了控制因子调度策略,通过自适应调整搜索空间,动态地调整蜜蜂之间的信息度,不断地进行信息交换跳出局部最优从而获得全局最优解。在云计算仿真平台CloudSim进行实验,结果表明,此方法能够缩短云环境下的任务平均运行时间,有效地提高了资源利用率。 相似文献
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在Min-Min的基础上,针对所存在的缺陷,提出了一种负载均衡的改进算法.仿真实验表明,在一定条件下,改进后的算法比传统的算法有一定的提高. 相似文献
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云计算的应用将数据存储、网络服务由用户桌面推向了Web,实现了高校各项事务的快速高效运行,也降低了硬件资源成本.但同时,随着云计算的拓展,其安全问题越来越受到关注.如用户信息在云端更易受到黑客攻击、蓄意窃取等非法利用.为此,基于云计算安全现状,探讨高校云计算安全性分析及参考模型,并从相关技术来提出解决云计算安全的对策和思路. 相似文献
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A workflow task scheduling algorithm based on the resources' fuzzy clustering in cloud computing environment 下载免费PDF全文
Fengyu Guo Long Yu Shengwei Tian Jiong Yu 《International Journal of Communication Systems》2015,28(6):1053-1067
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. 相似文献
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基于粒子群的网格任务调度算法研究 总被引:5,自引:0,他引:5
为了更好地解决异构动态环境下的资源管理问题,提出了一种网格环境下的任务调度模型。该模型考虑了当前网格虚拟组织下的计算资源、存储资源和带宽资源,模型的最优化目标是实现三者利用率最高和代价最低,即构造min-max函数。与遗传算法相比,利用粒子群优化算法对min-max函数求解提高了资源的利用率和任务的执行效率,同时在随着迭代次数增加的情况下,搜索速度、寻优率和避免早熟方面也有明显的提高。 相似文献
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It is a visible fact that the growth of mobile devices is enormous. More computations are required to be carried out for various applications in these mobile devices. But the drawback of the mobile devices is less computation power and low available energy. The mobile cloud computing helps in resolving these issues by integrating the mobile devices with cloud technology. Again, the issue is increased in the latency as the task and data to be offloaded to the cloud environment uses WAN. Hence, to decrease the latency, this paper proposes cloudlet‐based dynamic task offloading (CDTO) algorithm where the task can be executed in device environment, cloudlet environment, cloud server environment, and integrated environment. The proposed algorithm, CDTO, is tested in terms of energy consumption and completion time. 相似文献
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Harvinder Singh Sanjay Tyagi Pardeep Kumar 《International Journal of Communication Systems》2020,33(14)
Task scheduling in the cloud is the multiobjective optimization problem, and most of the task scheduling problems fail to offer an effective trade‐off between the load, resource utilization, makespan, and Quality of Service (QoS). To bring a balance in the trade‐off, this paper proposes a method, termed as crow–penguin optimizer for multiobjective task scheduling strategy in cloud computing (CPO‐MTS). The proposed algorithm decides the optimal execution of the available tasks in the available cloud resources in minimal time. The proposed algorithm is the fusion of the Crow Search optimization Algorithm (CSA) and the Penguin Search Optimization Algorithm (PeSOA), and the optimal allocation of the tasks depends on the newly designed optimization algorithm. The proposed algorithm exhibits a better convergence rate and converges to the global optimal solution rather than the local optima. The formulation of the multiobjectives aims at a maximum value through attaining the maximum QoS and resource utilization and minimum load and makespan, respectively. The experimentation is performed using three setups, and the analysis proves that the method attained a better QoS, makespan, Resource Utilization Cost (RUC), and load at a rate of 0.4729, 0.0432, 0.0394, and 0.0298, respectively. 相似文献