基于改进蛙跳算法的云计算资源调度

资源合理调度是云计算研究热点。为了提高云计算资源的调度效率,提出一种改进蛙跳算法的云计算资源调度方法。首先对云资源调度问题进行分析,建立云资源调度的目标函数,然后采用蛙跳算法对云资源调度问题进行寻优,并将对蛙跳算法进行改进,加快搜索速度,以提高算法学习能力。实验结果表明,相对于其他云计算资源调度方法,该方法可以更快找到最优云计算资源调度方案,使云计算资源负载更加均衡,提高云计算资源的利用率。     

多资源均衡优化的布谷鸟算法

针对标准多目标布谷鸟算法(CSA)后期收敛速度慢、收敛精度不高的缺陷,提出一种求解多资源均衡优化问题的改进多目标布谷鸟算法。首先,引入非均匀变异算子,以均衡算法的全局搜索能力和局部寻优能力;然后,引进差分进化算子,促进群体间的合作和信息交流,提高算法的收敛精度。通过算例测试表明,改进的多目标布谷鸟算法比标准多目标算法和VEPSO-BP算法具有更好的全局收敛性。     

多资源均衡优化的布谷鸟算法

针对标准多目标布谷鸟算法(CSA)后期收敛速度慢、收敛精度不高的缺陷,提出一种求解多资源均衡优化问题的改进多目标布谷鸟算法。首先,引入非均匀变异算子,以均衡算法的全局搜索能力和局部寻优能力;然后,引进差分进化算子,促进群体间的合作和信息交流,提高算法的收敛精度。通过算例测试表明,改进的多目标布谷鸟算法比标准多目标算法和VEPSO-BP算法具有更好的全局收敛性。     

云环境下改进SOS的多目标任务调度算法

为有效获取云计算中多目标任务调度求解算法的全局最优解,提出一种云环境下基于改进期望服务质量(Qo S)的多目标任务调度算法。设计多目标任务调度框架,提出相应的目标函数与约束条件。利用准反射学习构建初始种群以改进共生生物搜索(SOS)算法,加入自适应变异率以提高全局搜索能力。通过设定坐标进行任务分配,利用改进后SOS算法实现多目标任务优化调度。云计算仿真结果表明,所提算法相比于其它算法,有效改善了能源利用率、能耗和时间成本,具有较好的Qo S传输性能。     

一种云计算资源的多目标优化的调度方法

云计算通过虚拟化技术将基础设施硬件资源虚拟化,以动态可缩放的方式提供给用户。云计算基础设施规模不断增加导致资源调度系统负载不均衡,从而造成资源浪费等问题。提出多目标优化资源调度策略和相应的算法,试图同时满足多个资源调度优化目标,如减少资源浪费,降低服务等级约定(SLA)违背率、保持系统负载均衡等。通过仿真实验,验证了多目标优化资源调度的策略能够在多个相互冲突的目标之间实现最优权衡。     

膜计算改进粒子群优化算法的云资源调度

云计算环境下的资源合理调度是当前的研究热点,针对粒子群优化算法的不足,引入膜计算理论,提出一种基于膜计算改进粒子群优化算法的云资源调度算法（PSO-MC）。对云资源调度问题进行分析,建立云资源调度的目标函数,受到膜计算的启发,将粒子放入膜中,主膜内粒子进行精细化局部寻优,辅助膜内的粒子进行全局搜索,通过膜区域之间信息传递搜索结果,找到云资源调度问题的最优解,在CloudSim平台对算法进行仿真实验。结果表明,PSO-MC算法减少了任务的平均完成时间,提高了任务处理的效率,使云计算资源调度更加合理。     

ACOCS:一种云环境下资源调度混合算法

蚁群算法在优化组合问题中有着重要的意义,传统的蚁群调度算法搜索速度慢、容易陷入局部最优。针对这种情况,结合布谷鸟搜索算法,提出一种基于蚁群算法与布谷鸟搜索算法的混合算法(ACOCS),用于云环境下的资源调度。该方法有效保留了蚁群算法求解精度高和鲁棒性的特性,并融入了布谷鸟搜索具有快速全局搜索能力的优势。仿真实验结果表明,提出的ACOCS调度算法有效减少了调度所需的响应时间,也在一定程度上提高了系统资源利用率。     

基于离散人工蜂群算法的云任务调度优化

针对现今云计算任务调度只考虑单目标和云计算应用对虚拟资源的服务的质量要求高等问题,综合考虑了用户最短等待时间、资源负载均衡和经济原则,提出一种离散人工蜂群(ABC)算法的云任务调度优化策略。首先,从理论上建立了云任务调度的多目标数学模型;然后,结合偏好满意度策略并引入局部搜索算子和改变侦察蜂搜索方式,提出多目标离散型人工蜂群(MDABC)算法的优化策略。通过不同的云任务调度仿真实验,显示了改进离散人工蜂群算法相对于基础离散人工蜂群算法、遗传算法以及经典贪心算法,能够得到较高的综合满意度,表明了改进离散人工蜂群算法能够更好地改善虚拟资源中云任务调度系统的性能,具有一定的普适性。     

云环境中基于分解的多目标工作流调度算法

云服务提供商在给用户提供海量虚拟资源的同时,也面临着一个现实的问题,即怎样调度这些资源,以最小的代价(完工时间、执行费用、资源利用率等)完成工作流的执行。针对IaaS环境下的工作流调度问题,以完工时间和执行费用作为目标,提出了一种基于分解的多目标工作流调度算法。该算法结合了基于列表的启发式算法和多目标进化算法的选择过程,采用一种分解方法,将多目标优化问题分解为一组单目标优化子问题,然后同时求解这些单目标子问题,使得调度过程更为简单有效。算法利用天马项目发布的现实世界中的工作流进行实验,结果表明,和MOHEFT算法以及NSGA-II*算法相比较,所提出的算法能得到更优的Pareto解集,同时具有更低的时间复杂度。     

基于狮子优化和GSA混合的多目标任务调度算法

针对现有云计算环境中调度算法资源利用率低,调度成本高的问题,提出了一种基于狮子优化和引力搜索算法(gravity search algorithm,GSA)混合的多目标任务调度算法,该算法使用成本、能耗、资源利用作为目标函数,将狮子搜索和引力搜索算法进行有效地组合来执行智能过程调度,改善调度过程中的优化问题,避免陷入局部最优.实验结果表明,相对于其他调度算法,提出的多目标任务调度算法的性能具有明显的优势,解决了传统单目标资源调度算法存在的缺陷,最终优化方案可以获得最低成本、最低能耗和最高利润.     

云计算中基于改进的布谷鸟算法的资源调度

云计算的资源调度一直以来都是研究的重点,引入布谷鸟算法来解决资源分配问题,首先描述云计算资源模型,其次针对该算法存在局部收敛速度快,容易造成局部最优值的问题,采用三个方面来改进,其一采用变长因子进行调整,减小探索求解质量之间的差别;其二使用差分变异策略更新鸟窝位置;其三使用基于Coelho的混沌全局搜素和局部搜索避免了Levy的随意扰动.通过测试函数说明表明本文算法的性能优于基本布谷鸟算法, Cloudsim仿真平台说明本文的算法在消耗时间,成本和用户满意度方面具有明显的优势.     

CGSA scheduler: A multi-objective-based hybrid approach for task scheduling in cloud environment

In cloud computing task scheduling is one of the important processes. The key problem of scheduling is how to allocate the entire task to a corresponding virtual machine while maximizing profit. The main objective of this paper is to execute the entire task with low cost, less resource use, and less energy consumption. To obtain the multi-objective function for scheduling, in this paper we propose a hybridization of cuckoo search and gravitational search algorithm (CGSA). The vital design of our approach is to exploit the merits of both cuckoo search (CS) and gravitational search algorithms (GSA) while avoiding their drawbacks. The performance of the algorithm is analyzed based on the different evaluation measures. The algorithms like GSA, CS, Particle swarm optimization (PSO), and genetic algorithm (GA) are used as a comparative analysis. The experimental results show that our proposed algorithm achieves the better result compare to the existing approaches.     

A multi-objective co-evolutionary algorithm for energy-efficient scheduling on a green data center

Nowadays, the environment protection and the energy crisis prompt more computing centers and data centers to use the green renewable energy in their power supply. To improve the efficiency of the renewable energy utilization and the task implementation, the computational tasks of data center should match the renewable energy supply. This paper considers a multi-objective energy-efficient task scheduling problem on a green data center partially powered by the renewable energy, where the computing nodes of the data center are DVFS-enabled. An enhanced multi-objective co-evolutionary algorithm, called OL-PICEA-g, is proposed for solving the problem, where the PICEA-g algorithm with the generalized opposition based learning is applied to search the suitable computing node, supply voltage and clock frequency for the task computation, and the smart time scheduling strategy is employed to determine the start and finish time of the task on the chosen node. In the experiments, the proposed OL-PICEA-g algorithm is compared with the PICEA-g algorithm, the smart time scheduling strategy is compared with two other scheduling strategies, i.e., Green-Oriented Scheduling Strategy and Time-Oriented Scheduling Strategy, different parameters are also tested on the randomly generated instances. Experimental results confirm the superiority and effectiveness of the proposed algorithm.     

基于SaaS的弹性云平台优化调度策略设计

云计算平台利用虚拟化技术使软件应用变得更有效率的同时, 也给资源管理和服务调度带来了挑战。在研究了软件服务（SaaS）与基础设施服务（IaaS）调度的区别基础上, 重点考虑SaaS层的资源调度, 提出基于随机理论的调度模型, 把该层调度描述成一种多目标的优化问题。除了服务质量的要求, 还考虑了弹性这一云服务的重要特性, 并提供了任务调度与弹性服务副本的匹配策略。实验表明本调度机制的设计优化了云平台的整体性能, 达到了较好的负载均衡与资源利用率。     

负载敏感的云任务三支聚类评分调度研究

在云计算商业化的服务模式中,追求服务质量、负载均衡与经济原则的多目标优化调度。针对集群资源使用率偏低的现象,提出了三支聚类评分（three-way clustering weight,TWCW）算法,首先分析云任务的多样化需求与资源的动态特性,采用三支聚类算法对任务集合聚类划分,然后结合任务属性对类簇对象进行评分调度。基于Cloudsim实验模拟表明:相比于k-means与FCM聚类调度,三支聚类评分算法（TWCW）在任务平均响应时间与资源利用率等方面均有显著提升。     

Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length. Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling. In the end, a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment. A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the PACO. Thus, the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time, system throughput, and maximizing resource utilization.     

An intelligent water drops-based workflow scheduling for IaaS cloud

Cloud computing is an emerging technology in a distributed environment with a collection of large-scale heterogeneous systems. One of the challenging issues in the cloud data center is to select the minimum number of virtual machine (VM) instances to execute the tasks of a workflow within a time limit. The objectives of such a strategy are to minimize the total execution time of a workflow and improve resource utilization. However, the existing algorithms do not guarantee to achieve high resource utilization although they have abilities to achieve high execution efficiency. The higher resource utilization depends on the reusability of VM instances. In this work, we propose a new intelligent water drops based workflow scheduling algorithm for Infrastructure-as-a-Service (IaaS) cloud. The objectives of the proposed algorithm are to achieve higher resource utilization and minimize the makespan within the given deadline and budget constraints. The first contribution of the algorithm is to find multiple partial critical paths (PCPs) of a workflow which helps in finding suitable VM instances. The second contribution is a scheduling strategy for PCP-VM assignment for assigning the VM instances. The proposed algorithm is evaluated through various simulation runs using synthetic datasets and various performance metrics. Through comparison, we show the superior performance of the proposed algorithm over the existing ones.