共查询到19条相似文献,搜索用时 187 毫秒
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云计算是完全基于互联网的新兴技术。云计算环境中的任务调度问题一直都是该领域的研究热点。合理高效的任务调度算法在云环境中能有效的缩短任务完成时间,提高系统负载均衡,更好的满足用户与云提供商的需求。本文研究了云平台的任务调度机制,探究了任务调度过程中的关键性指标。通过云仿真平台CloudSim实现并分析了顺序调度算法、Min-Min算法和Max-Min算法,对比其在随机生成用户任务负载与虚拟机计算资源的情况下的任务完成时间,实验证明Min-Min算法与Max-Min算法均优于顺序调度算法。以此为未来研究提供实验支撑和方向。 相似文献
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针对目前大部分数据中心资源分配庞大和动态化的问题,研究基于人工智能技术的云计算资源分配模型。对蝙蝠算法和云计算资源模型进行设计,实现任务的划分,分配云计算资源;另外,创建资源分配调度框架,在用户终端收集任务调度模块,计算资源损耗;实现云任务的调度,接收用户提交的任务,初始化蝙蝠种群的脉冲频率,计算任务的优先权,从而输出全局最优解。利用C++开发仿真平台对不同数量服务器数据中心进行模拟,结果表明,通过此资源分配算法能够解决资源分配的问题,从而提高算法的分配效率。 相似文献
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为满足用户的性能需求及服务质量保障,针对复杂网络服务组合的特点和用户个性化的服务质量(Quality of Service, QoS)需求,研究了云环境下的网络感知服务组合问题,利用云服务和网络服务的QoS属性,提出了一种基于最优路径选择(Optimal Path Selection, OPS)的网络感知服务组合算法。该算法可以提升网络服务质量水平,改善用户体验质量。仿真结果表明,该算法在求解时间和质量两个方面都表现出了良好的性能,而且能动态适应用户复杂的需求,能够有效地解决云计算环境下的服务组合问题。 相似文献
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为了降低计算任务的时延和系统的成本,移动边缘计算(MEC)被用于车辆网络,以进一步改善车辆服务。该文在考虑计算资源的情况下对车辆网络时延问题进行研究,提出一种多平台卸载智能资源分配算法,对计算资源进行分配,以提高下一代车辆网络的性能。该算法首先使用K临近(KNN)算法对计算任务的卸载平台(云计算、移动边缘计算、本地计算)进行选择,然后在考虑非本地计算资源分配和系统复杂性的情况下,使用强化学习方法,以有效解决使用移动边缘计算的车辆网络中的资源分配问题。仿真结果表明,与任务全部卸载到本地或MEC服务器等基准算法相比,提出的多平台卸载智能资源分配算法实现了时延成本的显著降低,平均可节省系统总成本达80%。
相似文献7.
针对传统仿真系统平台的资源分配存在资源闲置、任务挤压和负载均衡等优化问题,利用云计算技术的优势研究并提出了模块化的云仿真平台框架,通过对云仿真资源调度策略研究,提出了一种改进的匈牙利算法.该算法克服了传统匈牙利算法只适用于一对一资源调度的不足,实现了多对一的仿真任务与云仿真资源分配方案,能尽量避免资源调度负载失衡.通过扩展云计算仿真平台CloudSim实现了模拟算法仿真.结果表明.该调度策略能有效的减小云环境下计算机的负载,提高了资源的利用率. 相似文献
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针对异构云无线接入网络(H-CRAN)网络下基于网络切片的在线无线资源动态优化问题,该文通过综合考虑业务接入控制、拥塞控制、资源分配和复用,建立一个以最大化网络平均和吞吐量为目标,受限于基站(BS)发射功率、系统稳定性、不同切片的服务质量(QoS)需求和资源分配等约束的随机优化模型,并进而提出了一种联合拥塞控制和资源分配的网络切片动态资源调度算法。该算法会在每个资源调度时隙内动态地为性能需求各异的网络切片中的用户分配资源。仿真结果表明,该文算法能在满足各切片用户QoS需求和维持网络稳定的基础上,提升网络整体吞吐量,并且还可通过调整控制参量的取值实现时延和吞吐量间的动态平衡。
相似文献9.
自动服务组合是目前云计算中的关键技术与研究热点.为大规模用户提供多个满足个性化需求的组合服务是当前云环境下自动服务组合中急需解决的问题.提出了基于扩展图规划的Top-K服务组合方法,借助服务索引和增加图规划中的辅助节点,使得经过一次规划搜索即可找到Top-K个满足用户QoS要求的组合服务.实验表明,该方法能够有效提高服务组合的效率,并保证服务组合结果的正确性,更加适用于云计算环境下海量网络服务及大规模用户个性化需求的自动服务组合问题. 相似文献
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云计算环境下,用户数量和处理的任务数量庞大,对任务完成时间和满足客户的QoS需求上具有较高要求。针对云计算中资源调度问题进行了研究,在综合考虑运行时间和满足客户QoS需求的情况下,建立了云计算资源调度适应度函数模型,并在最大最小蚁群算法的基础上引进了双向收敛策略。通过在CloudSim平台模拟实验,表明该算法在云计算资源分配上具有较快的收敛速度和较好的QoS服务能力,是一种有效的资源调度算法。 相似文献
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Nan Zhang Xiaolong Yang Min Zhang Yan Sun Keping Long 《International Journal of Communication Systems》2018,31(1)
With the rapid development of cloud computing, the number of cloud users is growing exponentially. Data centers have come under great pressure, and the problem of power consumption has become increasingly prominent. However, many idle resources that are geographically distributed in the network can be used as resource providers for cloud tasks. These distributed resources may not be able to support the resource‐intensive applications alone because of their limited capacity; however, the capacity will be considerably increased if they can cooperate with each other and share resources. Therefore, in this paper, a new resource‐providing model called “crowd‐funding” is proposed. In the crowd‐funding model, idle resources can be collected to form a virtual resource pool for providing cloud services. Based on this model, a new task scheduling algorithm is proposed, RC‐GA (genetic algorithm for task scheduling based on a resource crowd‐funding model). For crowd‐funding, the resources come from different heterogeneous devices, so the resource stability should be considered different. The scheduling targets of the RC‐GA are designed to increase the stability of task execution and reduce power consumption at the same time. In addition, to reduce random errors in the evolution process, the roulette wheel selection operator of the genetic algorithm is improved. The experiment shows that the RC‐GA can achieve good results. 相似文献
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Integration of firefly optimization and Pearson service correlation for efficient cloud resource utilization
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Cloud Computing (CC) environment presents a simplified, centralized platform or resources to usage while necessitated at minimum cost. In CC, the main processes in is the allocation of resources of web applications. However, with the increasing demands of Cloud User (CU), an efficient resource allocation technique for web applications is required. According to the request made by the user and response obtained, the cost of resources has also to be optimized. To overcome such limitations, Pearson service correlation‐based firefly resource cost optimization (PSC‐FRCO) technique is designed. Pearson service correlation‐based firefly resource cost optimization technique not only improves the performance of cost aware resource allocation but also achieves higher efficiency while rendering services in cloud computing environment for web applications. Pearson service correlation‐based firefly resource cost optimization technique initially uses Pearson service correlation in which the user‐required service is identified by correlating the available services provided by cloud owner. This helps in improving the Response Time (RT) of cloud service provisioning. Next, firefly resource cost optimization algorithm is applied to identify and allocate the cost‐optimized cloud resources to users to afford required service from the cloud server. Thus, PSC‐FRCO technique improves the Resource Utilization Efficiency (RUE) of web applications with minimal computational cost. This technique conducts experimental works on parameters such as RT, Bandwidth Utilization Rate (BUR) computational cost, Energy Consumption (EC), and RUE. Experimental analysis reveals that PSC‐FRCO technique enhances enhances RUE and lessens RT as compared to state‐of‐the‐art works. 相似文献
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Vahid Mohammadian Nima Jafari Navimipour Mehdi Hosseinzadeh Aso Darwesh 《International Journal of Communication Systems》2023,36(9):e5481
Recently, cloud computing has been recognized as an effective paradigm for offering an on-demand platform, software services, and an efficient infrastructure to cloud clients. Due to the exponential growth of cloud tasks and the rapidly increasing number of cloud users, scheduling and balancing these tasks among involved heterogeneous virtual machines becomes an Non-deterministic Polynomial hard (NP-hard) optimization problem considering significant constraints, such as high rate of resource usage, low scheduling time, and low implementation cost. Therefore, various meta-heuristic algorithms have been widely used to tackle the issue. The current paper proposes a novel load balancing mechanism using the ant colony optimization and artificial bee colony algorithms, called LBAA, which aims to balance the load division among systems in data centers. The simulation outcomes confirm that our algorithm outperforms previous works regarding response time, imbalance degree, makespan, and resource utilization up to 25%, 15%, 12%, and 10%, respectively. 相似文献
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This paper proposed an energy‐aware cross‐layer mobile cloud resource allocation approach. In this paper, a hybrid cloud architecture is adopted for provisioning mobile service to mobile device users, which include nearby local cloud and remote public cloud. The computation‐intensive tasks can be processed by the remote public cloud, while the delay‐sensitive computation can be processed by the nearby local cloud. On the basis of the system context and mobile user preferences, the energy‐aware cross‐layer mobile cloud resource allocation approach can optimize the consumption of cloud resource and system performance. The cooperation and collaboration among local cloud agent, public cloud supplier, and mobile cloud user are regulated through the economic approach. The energy‐aware cross‐layer mobile cloud resource allocation is performed on the local cloud level and the public cloud level, which comprehensively considers the benefits of all participants. The energy‐aware cross‐layer mobile cloud resource allocation algorithm is proposed, which is evaluated in the experiment environment, and comparison results and analysis are discussed. 相似文献
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为解决应用调度算法进行全域电力资源调度,资源剩余率依旧较高的问题,提出结合用户画像与关联规则的新型调度算法,实现全域电力资源的合理分配。运用双聚类算法,对整个调度区域内所有用户用电数据进行分析,构建电力用户画像从而描述用户用电个性化需求;以用户画像为基础,建立以满足用户需求为核心的全域资源分配模式;总结全域内资源调度子任务,计算不同子任务之间的支持度和置信度,结合关联规则实现子任务的分组;根据子任务组进行资源分域,在每个分域中设置二级调度中心,再与全域一级调度中心相连接,实现全域资源集中调度。实验结果表明,所提调度算法应用后,电力测试系统每日的全域资源剩余率出现了大幅降低,仅保持在12%左右。该算法具有较好的实际应用价值。 相似文献
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针对蜂窝用户与D2D用户所构成的异构网络系统中同频干扰问题,提出一种基于图着色的加权优先D2D资源分配算法.该算法不仅允许多个D2D用户复用一个蜂窝用户资源,而且能够实现简单功控.首先建立异构干扰图,对系统终端用户及干扰类型进行分类异构.然后计算着色优先级,考虑各种影响因子以提升算法的实用性.最后再由分配结果进行组内功率控制,以满足绿色通信的要求.仿真表明,该算法不仅可以降低系统用户接入损失率,提高系统吞吐量,而且还减少了功率消耗. 相似文献
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为了解决设备对设备(Device-to-Device,D2D)资源共享带来的信号干扰问题,提出了一种5G异构云无线接入网络的D2D通信资源分配算法.在保证服务质量的前提下,将宏用户设备的频谱资源分配给D2D和中继用户设备,并且把资源分配问题看作一对一的匹配博弈.采用婚姻匹配理论,得到初始的匹配方案.在初始匹配的基础上,提出了一种遵循卡尔多-希克斯(Kaldor-Hicks)原则的资源交换策略,以提高系统的吞吐量.仿真结果表明,该资源分配算法收敛较快,与现有方案相比,能使系统吞吐量提升15%以上,能给系统用户带来约10%的增益,并且有较强抗信道干扰能力. 相似文献
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Reihaneh Khorsand Mohammadreza Ramezanpour 《International Journal of Communication Systems》2020,33(9)
The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA). Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well‐known NP‐hard problem. To resolve this problem, this paper proposes an energy‐efficient task‐scheduling algorithm based on best‐worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision‐making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization. 相似文献