云计算环境下基于蜜蜂觅食行为的任务负载均衡算法

针对云计算环境下的任务调度程序通常需要较多响应时间和通信成本的问题,提出了一种基于蜜蜂行为的负载均衡(HBB-LB)算法。首先,利用虚拟机(VM)进行负载平衡来最大化吞吐量;然后,对机器上任务的优先级进行平衡;最后,将平衡重点放在减少VM等待序列中任务的等待时间上,从而提高处理过程的整体吞吐量和优先级。利用CloudSim工具模拟云计算环境进行仿真实验,结果表明,相比粒子群优化(PSO)、蚁群算法(ACO)、动态负载均衡(DLB)、先入先出(FIFO)和加权轮询(WRR)算法, HBB-LB算法的平均响应时间分别节省了5%、13%、17%、67%、37%,最大完成时间分别节省了20%、23%、18%、55%、46%,可以更好地平衡非抢占式独立任务,适用于异构云计算系统。     

异构云中面向集群负载均衡的任务调度策略

负载均衡是提高资源利用率和系统稳定性的重要手段。基于改进的自适应变异粒子群算法,提出了一种异构环境下面向集群负载均衡的任务调度策略。在调度策略的设计中,融入了经济学“二八”定律,通过把握用户对集群节点安全性和可靠性的偏好程度并预估任务的负载信息,在保证系统负载尽量均衡的前提下,最小化任务执行时间的同时提高大客户满意度。仿真实验显示,改进的自适应变异粒子群算法比未改进的自适应变异粒子群算法和基本粒子群算法在收敛速度和跳出局部最优两个方面都有更好的表现。结果表明,改进的自适应变异粒子群算法在保证集群负载均衡的同时可以更好地提高云服务提供商的利润空间。     

A Survey of PSO-Based Scheduling Algorithms in Cloud Computing

Cloud computing provides effective mechanisms for distributing the computing tasks to the virtual resources. To provide cost-effective executions and achieve objectives such as load balancing, availability and reliability in the cloud environment, appropriate task and workflow scheduling solutions are needed. Various metaheuristic algorithms are applied to deal with the problem of scheduling, which is an NP-hard problem. This paper presents an in-depth analysis of the Particle Swarm Optimization (PSO)-based task and workflow scheduling schemes proposed for the cloud environment in the literature. Moreover, it provides a classification of the proposed scheduling schemes based on the type of the PSO algorithms which have been applied in these schemes and illuminates their objectives, properties and limitations. Finally, the critical future research directions are outlined.     

基于正交自适应鲸鱼优化的云计算任务调度

针对任务调度中存在的任务完成时间长、系统执行任务成本高且系统负载不均衡等问题,提出了一种基于正交自适应鲸鱼优化算法（OAWOA）的云计算任务调度方法。首先,将正交试验设计（OED）应用于种群初始化和全局搜索阶段,以提升和维持种群的多样性,避免算法过早陷入局部收敛状态;然后,利用自适应指数递减因子和双向搜索机制,来进一步加强算法的全局搜索能力;最后,对适应度函数进行优化,从而使算法实现多目标优化。通过仿真实验将所提的算法与鲸鱼优化算法（WOA）、粒子群优化（PSO）算法、蝙蝠算法（BA）以及其他两种改进的WOA进行比较。实验结果表明,在任务规模为50和500时所提算法都取得了更好的收敛效果,并且得到的系统执行任务的总时间和总成本均低于其他几种算法,同时负载均衡度仅低于BA。可见,所提算法在降低系统执行任务的总时间和总成本以及提高系统负载均衡方面均表现出了显著的优势。     

基于粒子群优化算法的虚拟机部署策略

针对云计算基础设施即服务(IaaS)中的虚拟机部署问题,提出一种基于粒子群优化(PSO)算法的部署策略。由于PSO算法在处理虚拟机部署这类大规模复杂问题时,具有收敛速度慢且容易陷入局部最优的缺点,首先,引入多种群进化模式提高算法收敛速度,并在此基础上加入高斯学习策略避免局部最优,提出了一种多种群高斯学习粒子群优化(MGL-PSO)算法;然后,根据部署模型,使用轮询(RR)算法对MGL-PSO进行初始化,进而提出了一种以负载均衡为目标的虚拟机部署策略。通过在CloudSim中进行仿真实验,验证了在解决虚拟机部署问题时,MGL-PSO相比PSO算法,具有更快的收敛速度,并且负载不均衡度降低了13.1%。在两种实验场景下,所提算法相比随机负载均衡(OLB)算法,其负载不均衡度分别平均降低了25%和15%;相比贪婪算法(GA),使负载不均衡度分别平均降低了19%和7%。     

基于模糊聚类分析的云计算负载平衡策略

如何实现资源访问的负载平衡成为云计算实施的关键问题之一。基于云计算环境的特点,改进了模糊聚类算法,将粒子群优化算法与模糊C均值聚类算法融合,提高算法正确率。将改进后的聚类算法应用于对各个计算节点的输入输出(I/O)及中央处理器(CPU)利用率的分析,得到对于负载度的分类,并以此为依据判断需要迁移任务的节点,进而实现负载平衡。实验结果表明无论是在UCI机器学习库或是针对提出的负载平衡机制环境下,改进的模糊聚类算法在算法的准确率方面均优于传统算法10%以上,且在算法稳定性方面亦优于传统算法。     

Honey bee behavior inspired load balancing of tasks in cloud computing environments

Scheduling of tasks in cloud computing is an NP-hard optimization problem. Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing (HBB-LB), which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue.     

Multi-objective Swarm Intelligence schedulers for online scientific Clouds

Cloud Computing is a promising paradigm for parallel computing. However, as Cloud-based services become more dynamic, resource provisioning in Clouds becomes more challenging. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. In a Cloud, an appropriate number of Virtual Machines (VM) is created and allocated in physical resources for executing jobs. This work focuses on the Infrastructure as a Service (IaaS) model where custom VMs are launched in appropriate hosts available in a Cloud to execute scientific experiments coming from multiple users. Finding optimal solutions to allocate VMs to physical resources is an NP-complete problem, and therefore many heuristics have been developed. In this work, we describe and evaluate two Cloud schedulers based on Swarm Intelligence (SI) techniques, particularly Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. We also perform a sensitivity analysis by varying the specific-parameter values of each algorithm to evaluate the impact on the performance of the two objective metrics. The intra-Cloud network traffic is also measured. Simulated experiments performed using CloudSim and job data from real scientific problems show that the use of SI-based techniques succeeds in balancing the studied metrics compared to Genetic Algorithms.     

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

Cloud data centers consume high volume of energy for processing and switching the servers among different modes. Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability. On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system. In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations. In this view, the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization (IMFP-VMMO) model in cloud environment. The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction. To accomplish this, IMFP-VMMO model employs Gradient Boosting Decision Tree (GBDT) classification model at initial stage for effectual prediction of VM failures. At the same time, VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm (QO-AFSA) which in turn reduces the energy consumption. The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model. The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.     

基于动态调整阈值的虚拟机迁移算法

针对当前数据中心服务器能耗优化和虚拟机迁移时机合理性问题,提出一种基于动态调整阈值（DAT）的虚拟机迁移算法。该算法首先通过统计分析物理机历史负载数据动态地调整虚拟机迁移的阈值门限,然后通过延时触发和预测物理机的负载趋势确定虚拟机迁移时机。最后将该算法应用到实验室搭建的数据中心平台上进行实验验证,结果表明基于DAT的虚拟机迁移算法比静态阈值法关闭的物理机数量更多,云数据中心能耗更低。基于DAT的虚拟机迁移算法能根据物理机的负载变化动态迁移虚拟机,达到提高物理机资源利用率、降低数据中心能耗、提高虚拟机迁移效率的目的。     

Global optimization of an optical chaotic system by Chaotic Multi Swarm Particle Swarm Optimization

The control and estimation of unknown parameters of chaotic systems are a daunting task till date from the perspective of nonlinear science. Inspired from ecological co-habitation, we propose a variant of Particle Swarm Optimization (PSO), known as Chaotic Multi Swarm Particle Swarm Optimization (CMS-PSO), by modifying the generic PSO with the help of the chaotic sequence for multi-dimension unknown parameter estimation and optimization by forming multiple cooperating swarms. This achieves load balancing by delegating the global optimizing task to concurrently operating swarms. We apply it successfully in estimating the unknown parameters of an autonomous chaotic laser system derived from Maxwell-Bloch equations. Numerical results and comparison demonstrate that for the given system parameters, CMS-PSO can identify the optimized parameters effectively evolving at each iteration to attain the pareto optimal solution with small population size and enhanced convergence speedup.     

基于多微云协作的计算任务卸载

针对多微云计算模式下计算任务卸载过程复杂、任务响应时间长的问题,构建面向多微云协作的计算任务卸载模型,并提出加权自适应惯性权重的粒子群优化（WAIW-PSO）算法,快速求解最优卸载策略。首先,对移动终端-微云-远程云的任务执行过程进行建模;其次,考虑多用户对计算资源的竞争,构建基于多微云协作的任务卸载模型;最后,针对求解最佳任务卸载策略复杂度过高的情况,提出WAIW-PSO算法求解卸载问题。仿真实验结果表明,与标准粒子群优化（PSO）算法以及基于高斯函数递减惯性权重的粒子群优化（GDIWPSO）算法相比,WAIW-PSO算法可以根据进化代数和个体适应度综合调整惯性权重,寻优能力较强,求解最优卸载策略的时间最短;在不同设备数、任务数等情况下选择不同任务卸载策略进行对比实验的结果表明,基于WAIW-PSO算法的卸载策略可以明显缩短任务总完成时间。     

基于粒子群算法的再入式飞行器再入走廊计算方法研究

提出一种利用粒子群算法计算再入式飞行器走廊的方法。从再入式飞行器再入过程的运动方程出发,将连续无限维的再入飞行器走廊上边界计算问题,转化成计算走廊上有限个点的多个最优控制问题,最后利用粒子群寻优解决每个最优控制问题,从而得到可行的走廊上边界曲线,这种方法得到的走廊上界曲线比传统的准平衡滑翔条件估计的上界要高,更能体现RLV的飞行能力。     

混合自适应粒子群工作流调度优化算法

针对具有截止期的云工作流完成时间与执行成本冲突的问题,提出一种混合自适应粒子群工作流调度优化算法（HAPSO）。首先,基于截止期建立有向无环图（DAG）云工作流调度模型;然后,通过范数理想点与自适应权重的结合,将DAG调度模型转化为权衡DAG完成时间和执行成本的多目标优化问题;最后,在粒子群优化（PSO）算法的基础上引入自适应惯性权重、自适应学习因子、花朵授粉算法的概率切换机制、萤火虫算法（FA）和粒子越界处理方法,从而平衡粒子群的全局搜索与局部搜索能力,进而求解DAG完成时间与执行成本的目标优化问题。实验中对比分析了PSO、惯性权重粒子群算法（WPSO）、蚁群算法（ACO）和HAPSO的优化结果。实验结果表明,HAPSO在权衡工作流（30～300任务数）完成时间与执行成本的多目标函数值上降低了40.9%～81.1%,HAPSO在工作流截止期约束下有效权衡了完成时间与执行成本。此外,HAPSO在减少完成时间或降低执行成本的单目标上也有较好的效果,验证了HAPSO的普适性。     

Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm

IoT is one of the most important technologies that are used over the past few years, where everything is connected to the Internet; it is used in many fields; one of these fields is healthcare system that includes mobile health and remote patient monitoring (patients with kidney, heart disease, cancer, blood pressure, diabetes, respiratory disease and stroke). Integration of IoT and cloud computing can improve the performance of healthcare system and the development of the innovative applications in future. One of the major problems that cannot be ignored in cloud computing system is load balancing. Solving that problem leads to reduce the response time, power consumption, cost and increase server availability. This paper is comprised of two parts which are creating and implementing healthcare system by using IoT, and solving the problem of load balancing of the cloud computing by using intelligent algorithm called sparrow search algorithm (SSA). The SSA is used to select the best virtual machine (VM) among a group of VMs depending on the its fitness value; also many and varied tasks are scheduled with priority and assign to the best VMs depending on the its instruction millions (IM), where the task that has high IM is assigned to the best VM that has high fitness value. The outcomes demonstrated that the proposed method focuses to reduce the latency and packet loss while maximizing the throughput in healthcare systems; also the SSA has proved its robustness, efficiency and success when compared to other methods in terms of reducing makespan time, total processing time and provides load balancing among VMs, where the value of makespan time, processing time and degree of imbalance has decreased into (23.05), (899.8979) and (177.7675), respectively, in case of applying 500 tasks.

     

改进的粒子群算法在化工过程优化中的应用

在现有自适应粒子群优化算法的研究基础上本文引入1种反弹机制(Rebound Mechanism),提出了1种改进的粒子群算法——反弹自适应粒子群优化算法。RAPSO能在搜索过程中充分利用粒子的飞行速度和方向等信息(下文称为动量信息),维持粒子的多样性以提升算法的搜索性能。通过比较,本文提出的RAPSO在一定程度上改进了现有的自适应粒子群算法的优化性能。运用RAPSO对催化裂化装置进行优化试验,其结果表明无论在单变量优化还是在多变量优化中,该装置的转化率都得到了一定程度的提高。     

Energy-efficient Tasks Scheduling Heuristics with Multi-constraints in Virtualized Clouds

Reducing energy consumption has become an important task in cloud datacenters. Many existing scheduling approaches in cloud datacenters try to consolidate virtual machines (VMs) to the minimum number of physical hosts and hence minimize the energy consumption. VM live migration technique is used to dynamically consolidate VMs to as few PMs as possible; however, it introduces high migration overhead. Furthermore, the cost factor is usually not taken into account by existing approaches, which will lead to high payment cost for cloud users. In this paper, we aim to achieve energy reduction for cloud providers and payment saving for cloud users, and at the same time, without introducing VM migration overhead and without compromising deadline guarantees for user tasks. Motivated by the fact that some of the tasks have relatively loose deadlines, we can further reduce energy consumption by proactively postponing the tasks without waking up new physical machines (PMs). A heuristic task scheduling algorithm called Energy and Deadline Aware with Non-Migration Scheduling (EDA-NMS) algorithm is proposed, which exploits the looseness of task deadlines and tries to postpone the execution of the tasks that have loose deadlines in order to avoid waking up new PMs. When determining the VM instant types, EDA-NMS selects the instant types that are just sufficient to guarantee task deadline to reduce user payment cost. The results of extensive experiments show that our algorithm performs better than other existing algorithms on achieving energy efficiency without introducing VM migration overhead and without compromising deadline guarantees.     

具有反向学习和自适应逃逸功能的粒子群优化算法

为克服粒子群优化算法进化后期收敛速度慢、易陷入局部最优等缺点,提出一种具有反向学习和自适应逃逸功能的粒子群优化算法.通过设定的阈值,算法将种群进化状态划分为正常状态和"早熟"状态: 若算法处于正常的进化状态,采用标准粒子群优化算法的进化模式;当粒子陷入"早熟"状态,运用反向学习和自适应逃逸功能,对个体最优位置进行反向学习,产生粒子的反向解,增加粒子的反向学习能力,增强算法逃离局部最优的能力,提高算法寻优率.在固定评估次数的情况下,对8个基准测试函数进行仿真,实验结果表明:所提算法在收敛速度、寻优精度和逃离局部最优的能力上明显优于多种经典粒子群优化算法,如充分联系的粒子群优化算法(FIPS)、基于时变加速度系数的自组织分层粒子群优化算法(HPSO-TVAC)、综合学习的粒子群优化算法(CLPSO)、自适应粒子群优化算法(APSO)、双中心粒子群优化算法(DCPSO)和具有快速收敛和自适应逃逸功能的粒子群优化算法(FAPSO)等.     

无线网状网中面向负载均衡的网关部署算法

综合考虑网关数量和网关负载均衡两方面因素,提出一种基于力的贪婪启发式分簇算法,在此基础上对杂交粒子群优化进行改进,给出一种适用于无线网状网的网关部署算法。仿真实验结果表明,该算法能以较少的网关数量实现网络负载均衡,从而提高吞吐量,改善网络性能。     

基于MPSO算法的云计算资源调度策略

针对云计算服务集群资源调度和负载平衡的优化问题,提出一种基于改进的粒子群优化算法的云计算资源调度策略。将动态多群体协作和变异粒子逆向飞行思想引入到粒子群优化算法中,从而控制全局搜索和局部搜索,尽量避免陷入局部最优。在CloudSim 平台进行模拟测试,结果表明,该调度策略有效且执行效率较高。