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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
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在网格环境中,如何对任务进行高效调度是当前研究的热点问题。目前Min—Min调度算法是一个简单、快速、有效的算法。但它很难满足网格任务对服务质量的要求。在独立型的任务调度模型的基础上,提出了一种基于权值的改进Min—Min调度算法。改进后的算法通过量化网格任务的优先级和等待时间,解决了原有算法存在的高质量任务和大任务等待时间过长的问题。仿真实验结果表明,改进后的算法满足了网格任务对优先级和等待时间的服务质量要求.是一种网格环境下有效的任务调度算法。 相似文献
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对于传统蚁群算法用于云计算资源分配和调度问题过程中存在的不足,提出了一种可以提高负载均衡度、缩短任务执行时间、降低任务执行成本的改进自适应蚁群算法,改进算法以能够基于用户提交的任务求解出执行时间较短、费用较低,负载率均衡的分配方案为目标,通过CloudSim平台对传统蚁群算法、最新的AC-SFL算法、改进自适应蚁群算法进行仿真实验对比。实验数据表明,改进后的自适应蚁群算法能够快速找出最优的云计算资源调度问题的解决方案,缩短了任务完成时间,降低了执行费用,保持了整个云系统中心的负载均衡。 相似文献
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在云计算环境中存在庞大的任务数,为了能更加高效地完成任务请求,如何进行有效地任务调度是云计算环境下实现按需分配资源的关键。针对调度问题提出了一种基于蚁群优化的任务调度算法,该算法能适应云计算环境下的动态特性,且集成了蚁群算法在处理NP-Hard问题时的优点。该算法旨在减少任务调度完成时间。通过在CloudSim平台进行仿真实验,实验结果表明,改进后的算法能减少任务平均完成时间、并能在云计算环境下有效提高调度效率。 相似文献
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In this paper, we propose an urgency‐ and efficiencybased wireless packet scheduling (UEPS) algorithm that is able to schedule real‐time (RT) and non‐real‐time (NRT) traffics at the same time while supporting multiple users simultaneously at any given scheduling time instant. The UEPS algorithm is designed to support wireless downlink packet scheduling in an orthogonal frequency division multiple access (OFDMA) system, which is a strong candidate as a wireless access method for the next generation of wireless communications. The UEPS algorithm uses the time‐utility function as a scheduling urgency factor and the relative status of the current channel to the average channel status as an efficiency indicator of radio resource usage. The design goal of the UEPS algorithm is to maximize throughput of NRT traffics while satisfying quality‐of‐service (QoS) requirements of RT traffics. The simulation study shows that the UEPS algorithm is able to give better throughput performance than existing wireless packet scheduling algorithms such as proportional fair (PF) and modifiedlargest weighted delay first (M‐LWDF), while satisfying the QoS requirements of RT traffics such as average delay and packet loss rate under various traffic loads. 相似文献
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计算网格环境下以QoS为指导的启发式任务调度 总被引:1,自引:1,他引:0
实现用户的服务质量QoS是网格的一个重要特征.网格环境下以服务质量为指导的任务调度是一个非常复杂且具有挑战性的问题,特别是在用户的任务具有多种QoS需求的情况下。利用效用函数对用户的多种QoS进行建模.提出了一个同时兼顾任务完成时间和用户服务质量的目标函数。在此基础上提出了一种计算网格环境下针对一组具有QoS需求的相互独立的计算任务的启发式调度算法。模拟实验结果显示,该算法能较好的满足不同用户的需求并提升系统资源的利用率。 相似文献
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To handle the low planning efficiency of the tasks with too long or too short service time,a task planning scheme was proposed based on task splitting and merging for relay satellite systems.First,a task splitting and merging was developed to transfer the task requirements of user to task units which could be planned with high efficiency.Secondly,based on the parallel machine scheduling model,the optimization problem of the task unit planning to maximize the number of completed tasks in the network was built.Further,a heuristic polynomial time scheduling algorithm was proposed.Simulation results show that compared to the traditional scheme,the task planning scheme perform better in terms of completed task number,resource utilization and fairness. 相似文献
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为保障边缘计算的服务质量,提出一种在多约束条件下边缘计算可信协同任务迁移策略。该策略基于任务需求,由边缘计算协同服务盟主节点组织调度协同服务盟员,基于用户任务迁移的K维权重指标,确定协同盟员调度优先级,以盟员负载均衡性为适应函数,通过贪心算法执行盟员任务分配与调度,基于路由捎带选择备用节点,通过迁移优先级评估,实现协同服务异常时的调度和迁移,由此提高边缘计算任务迁移的服务质量,保障任务迁移的可靠性。仿真实验表明,该机制能有效完成协同任务分发与迁移调度,提高边缘计算协同效率,保障网络服务质量。 相似文献
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Yufei Wang Jun Liu Yu Tong Qingwen Yang Yanyi Liu Hanbo Mou 《International Journal of Satellite Communications and Networking》2023,41(4):331-356
With the development of space information network (SIN), new network applications are emerging. Satellites are not only used for storage and transmission but also gradually used for calculation and analysis, so the demand for resources is increasing. But satellite resources are still limited. Mobile edge computing (MEC) is considered an effective technique to reduce the pressure on satellite resources. To solve the problem of task execution delay caused by limited satellite resources, we designed Space Mobile Edge Computing Network (SMECN) architecture. According to this architecture, we propose a resource scheduling method. First, we decompose the user tasks in SMECN, so that the tasks can be assigned to different servers. An improved ant colony resource scheduling algorithm for SMECN is proposed. The heuristic factors and pheromones of the ant colony algorithm are improved through time and resource constraints, and the roulette algorithm is applied to route selection to avoid falling into the local optimum. We propose a dynamic scheduling algorithm to improve the contract network protocol to cope with the dynamic changes of the SIN and dynamically adjust the task execution to improve the service capability of the SIN. The simulation results show that when the number of tasks reaches 200, the algorithm proposed in this paper takes 17.52% less execution time than the Min-Min algorithm, uses 9.58% less resources than the PSO algorithm, and achieves a resource allocation rate of 91.65%. Finally, introducing dynamic scheduling algorithms can effectively reduce task execution time and improve task availability. 相似文献
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A multidimensional cloud computing architecture is designed and a multidimensional cloud resource scheduling model is constructed based on the stakeholder perspective of cloud users and cloud service providers to meet the high QoS requirements of cloud users (such as task execution time and task completion time) with low computing costs (such as energy consumption,economic costs and system availability).For the second-level cloud resource scheduling,an MQoS cloud resource scheduling algorithm based on multiple Greedy algorithm is proposed.The experimental results show that under the four cloud computing application scenarios with no aftereffects,the MQoS cloud resource scheduling algorithm has an overall increase of 206.42%~228.99% and 34.26%~56.93 in terms of multidimensional QoS degree compared with FIFO and M2EC algorithms.It has an average overall reduction of 0.48~0.49 and 0.20~0.27 in terms of cloud data center load balance difference. 相似文献
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提出了一种将遗传算法与蚁群算法融合的新算法,用以满足多QoS约束的组播路由优化。算法首先利用遗传算法生成若干组优化解,将其转换成蚁群算法的信息素初值,然后利用蚁群算法来求取满足QoS约束的最优解。仿真结果表明此算法是有效的,其性能优于文献[6]中算法。 相似文献
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基于粒子群算法的嵌入式云计算资源调度 总被引:2,自引:0,他引:2
随着移动互联网的发展,基于嵌入式设备的云计算服务成为研究热点。在国内,嵌入式云计算目前正处于探索研究阶段,云资源管理调度是嵌入式云计算的核心技术之一,其效率直接影响嵌入式云计算系统的性能。为了提高云计算性能,本文提出一种基于粒子群优化算法的云计算任务调度模型。粒子群算法中粒子位置代表可行的资源调度方案,以云计算任务完成时间及资源负载均衡度作为目标函数,通过粒子群优化算法,找出最优资源调度方案。在matlab实验平台进行了仿真,通过大量数据模拟实验表明,该模型可以快速找到最优调度方案,提高资源利用率,具有较好的实用性和可行性。 相似文献
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优化网格资源调度算法可以提高网格系统执行效率,给任务安排合理的执行顺序和合适的处理器是优化网格资源调度算法需突破的关键技术.文中研究并实现了(Heterogeneous-Earliest-Finish) HEFT[1]算法和新的(Hierarchical Reliability-Driven Scheduling)HRDS算法.采用DAG[2]任务图生成函数,通过对已有HEFT算法进行研究,采用SimGrid为在分布计算环境下进行分布并行应用调度研究提供一个仿真环境,对HRDS算法进行了改进和验证.验证过程中在HRDS算法中加入了可靠性开销作为调度依据,并把算法分为两层调度,本地可靠性驱动调度和全局可靠性驱动调度.两算法的调度结果在SimGrid网格模拟器中仿真调度,仿真成功并且调度结果在可靠性和性能方面HRDS都比HEFT算法要好. 相似文献
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In future wireless network, one user will require multiple homogeneous or heterogeneous services simultaneously. Then, the scheduling algorithm is not only responsible for assigning a resource block to different users but also sharing the assigned resource block among multiple services for one user. Most of the traditional scheduling algorithms are designed to serve one service per user, and cannot be applied directly to this scenario because of the fairness criterion. This article focuses on adaptive resource allocation for multiple services per user at the downlink of orthogonal frequency division multiplexing (OFDM) based system. This article addresses this integrative resource scheduling problem based on utility function. First, the optimal algorithm for dynamic subcarrier allocation and share is deduced for homogeneous best-effort service system. Then the algorithm is extended to heterogeneous services system by classifying the delay sensitive service according to the head-of-line packet delay. The design goal is to maximize aggregate utility function to exploit multiuser diversity gain to the greatest extent even as guaranteeing quality of service (QoS) for delay sensitive service. 相似文献