一种基于分层多代理的云计算负载均衡方法

针对现有云计算系统中负载均衡方法的不足,借鉴系统逻辑分层和多代理的思想,提出一种基于分层多代理的云计算负载均衡方法.通过对云计算平台逻辑分层,在任务代理层设置任务监控代理和任务子代理,根据用户任务的差异性,采用基于任务优先级和QoS目标约束的调度策略协同完成任务调度;在资源代理层设置资源监控代理和资源子代理,考虑物理节点的异构性,采用基于启发式贪婪的资源分配策略协同完成虚拟机到物理节点的映射.通过评估对比仿真实验,结果表明该方法在任务调度效率、任务完成时间、截止时间违背率和负载均衡度方面表现更优,多代理有效地分担了中心管理节点的管理负载,使云计算平台的任务处理能力、资源利用率及鲁棒性均得到了进一步的提升.     

基于混合遗传算法的云计算任务节能调度算法

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。     

基于改进蚁群算法的自适应云资源调度模型研究

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。     

PSO 应用于 QoS 偏好感知的云存储任务调度

研究有QoS偏好要求的云存储任务调度。首先,分析云存储与云计算的差异,用存在矩阵避免无效解的产生。其次,归纳云存储的QoS需求为时间、代价与质量3大类,并据此修改PSO算法的适应度函数用权重因子调节QoS偏好。实验发现,在不同优先级任务分布不均的情况下,分布广的任务的偏好会掩盖其他任务的偏好,因而不适宜用PSO进行整体性调度,而必须进行分级调度。实验证明,改进后的分级PSO算法对QoS偏好具有较好的感知能力。     

ForCES网络中的资源调度算法和经济模型(英文)

Resource scheduling algorithm for ForCES(Forwarding and Control Element Separation) networks need to meet the flexibility,programmability and scalability of node resources.DBC(Deadline Budget Constrain) algorithm relies on users select cost or time priority,then scheduling to meet the requirements of users.However,this priority strategy of users is relatively simple,and cannot adapt to dynamic change of resources,it is inevitable to reduce the QoS.In order to improve QoS,we refer to the economic model and resource scheduling model of cloud computing,use SAL(Service Level Agreement) as pricing strategy,on the basis of DBC algorithm,propose an DABP(Deadline And Budget Priority based on DBC) algorithm for ForCES networks,DABP combines both budget and time priority to scheduling.In simulation and test,we compare the task finish time and cost of DABP algorithm with DP(Deadline Priority) algorithm and BP(Budget Priority) algorithm,the analysis results show that DABP algorithm make the task complete with less cost within deadline,benifical to load balancing of ForCES networks.     

云环境下任务调度算法对比研究

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。     

Task scheduling mechanisms in cloud computing: A systematic review

Today, cloud computing has developed as one of the important emergent technologies in communication and Internet. It offers on demand, pay per use access to infrastructure, platforms, and applications. Due to the increase in its popularity, the huge number of requests need to be handled in an efficient manner. Task scheduling as one of the challenges in the cloud computing supports the requests for assigning a particular resource so as to perform effectively. In the resource management, task scheduling is performed where there is the dependency between tasks. Many approaches and case studies have been developed for the scheduling of these tasks. Up to now, a systematic literature review (SLR) has not been presented to discover and evaluate the task scheduling approaches in the cloud computing environment. To overcome, this paper presents an SLR‐based analysis on the task scheduling approaches that classify into (a) single cloud environments that evaluate cost‐aware, energy‐aware, multi‐objective, and QoS‐aware approaches in task scheduling; (b) multicloud environment that evaluates cost‐aware, multi‐objective, and QoS‐aware task scheduling; and (c) mobile cloud environment that is energy‐aware and QoS‐aware task scheduling. The analytical discussions are provided to show the advantages and limitations of the existing approaches.     

一种基于免疫克隆的偏好多维QoS云资源调度优化算法

针对云计算环境中高效资源调度问题,首先从理论上对云资源调度进行了建模,对用户应用偏好和多维QoS中的用户效用进行了量化,给出了多维QoS优化的目标函数.结合具有快速多目标优化能力的免疫克隆算法,提出了一种云资源调度优化算法.根据应用偏好信息为抗体分配偏好优先级,据此进行抗体的免疫克隆操作,提高抗体免疫基因操作的预见性,...     

基于粒子群算法的嵌入式云计算资源调度

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。     

Crow–penguin optimizer for multiobjective task scheduling strategy in cloud computing

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.     

Research on optimal two element exchange algorithm for large scale cloud computing server scheduling problem

With the flourishing of cloud computing industry,the rational management and scientific scheduling of cloud computing servers has become an important issue.In terms of model,a new mixed integer programming (MIP) model with affinity constraints and anti-affinity constraints was proposed to describe the scheduling problem of large scale cloud computing server.Considering the time cost of solving large-scale MIP problems,an optimal two element exchange algorithm was designed with the basics of branch and bound method and local search algorithm.By constantly extracting MIP sub-problems from completing scheduling problems and using branch and bound method to solve the sub-problems,the algorithm continuously optimized the server scheduling schemes,so that the scheduling schemes approached the optimal solution.The experimental results show that the algorithm has great advantages over the other methods in testing data set ALISS,and can reduce the resource consumption of cloud computing center by more than 4% when the same task is completed.     

K-PSO: An improved PSO-based container scheduling algorithm for big data applications

In recent years, Docker container technology is being applied in the field of cloud computing at an explosive speed. The scheduling of Docker container resources has gradually become a research hotspot. Existing big data computing and storage platforms apply with traditional virtual machine technology, which often results in low resource utilization, a long time for flexible scaling and expanding clusters. In this paper, we propose an improved container scheduling algorithm for big data applications named Kubernetes-based particle swarm optimization(K-PSO). Experimental results show that the proposed K-PSO algorithm converges faster than the basic PSO algorithm, and the running time of the algorithm is cut in about half. The K-PSO container scheduling algorithm and algorithm experiment for big data applications are implemented in the Kubernetes container cloud system. Our experimental results show that the node resource utilization rate of the improved scheduling strategy based on K-PSO algorithm is about 20% higher than that of the Kube-scheduler default strategy, balanced QoS priority strategy, ESS strategy, and PSO strategy, while the average I/O performance and average computing performance of Hadoop cluster are not degraded.     

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

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.     

Multicriteria decision‐making techniques for avoiding similar task scheduling conflict in cloud computing

An efficient task scheduling approach shows promising way to achieve better resource utilization in cloud computing. Various task scheduling approaches with optimization and decision‐making techniques have been discussed up to now. These approaches ignored scheduling conflict among the similar tasks. The conflict often leads to miss the deadlines of the tasks. The work studies the implementation of the MCDM (multicriteria decision‐making) techniques in backfilling algorithm to execute deadline‐based tasks in cloud computing. In general, the tasks are selected as backfill tasks, whose role is to provide ideal resources to other tasks in the backfilling approach. The selection of the backfill task is challenging one, when there are similar tasks. It creates conflict in the scheduling. In cloud computing, the deadline‐based tasks have multiple parameters such as arrival time, number of VMs (virtual machines), start time, duration of execution, and deadline. In this work, we present the deadline‐based task scheduling algorithm as an MCDM problem and discuss the MCDM techniques: AHP (Analytical Hierarchy Process), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to avoid similar task scheduling conflicts. We simulate the backfilling algorithm along with three MCDM mechanisms to avoid scheduling conflicts among the similar tasks. The synthetic workloads are considered to study the performance of the proposed scheduling algorithm. The mechanism suggests an efficient VM allocation and its utilization for deadline‐based tasks in the cloud environment.     

Deadline based scheduling for data-intensive applications in clouds

Cloud computing emerges as a new computing pattern that can provide elastic services for any users around the world. It provides good chances to solve large scale scientific problems with fewer efforts. Application deployment remains an important issue in clouds. Appropriate scheduling mechanisms can shorten the total completion time of an application and therefore improve the quality of service (QoS) for cloud users. Unlike current scheduling algorithms which mostly focus on single task allocation, we propose a deadline based scheduling approach for data-intensive applications in clouds. It does not simply consider the total completion time of an application as the sum of all its subtasks’ completion time. Not only the computation capacity of virtual machine (VM) is considered, but also the communication delay and data access latencies are taken into account. Simulations show that our proposed approach has a decided advantage over the two other algorithms.     

云计算环境下任务调度算法的研究

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。     

A genetic algorithm‐based task scheduling for cloud resource crowd‐funding model

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.     

云计算资源调度算法的研究与实现

在研究蚁群算法、任务分配和资源调度的基础上,提出了一种改进的蚁群资源调度算法.首先通过引入节点可信度机制在一定程度上增强了云计算资源的搜索能力和节点完成任务的成功率.然后在改进的算法中使用了信息素的局部更新机制和全局更新机制,可以有效地平衡负载.最后通过选取合适的参数利用CloudSim仿真工具对改进的资源调度算法进行实验测试,实验结果表明此算法缩短了任务的执行时间,改善了云计算资源调度的性能.     

基于云计算平台的智慧城市管理系统设计

为了解决智慧城市管理过程中常出现资源调度速度过慢问题,设计了云计算平台的智慧城市管理系统。该系统采用云管理模块下监控各硬件设备,并构建云计算资源调度目标函数,利用文化粒子群算法对目标函数求解,得到云计算资源调度方案,最后测试结果表明,该系统能够实现智慧城市有效管理,并能实时监测城市情况,在实行资源调度时,任务完成时间较短且系统利用率较高,能够实现资源最大化利用。     

A workflow task scheduling algorithm based on the resources' fuzzy clustering in cloud computing environment

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.