基于收益驱动请求分类的多目标动态优先请求调度

请求调度通常需要在充分利用现有服务器资源的基础上满足响应时间最小化和系统吞吐量最大化的目标,但对于以盈利为目的的电子商务网站来说,关键还是要提高交易请求和VIP用户发起请求的达成率。针对电子商务网站请求调度的多重目标,首先提出了收益驱动的请求分类多维标准,在此基础上定义了请求优先级和调度优先级的概念,给出了基于请求分类的多目标动态优先调度算法MODP,并引入了基于事前过载判断而非负载测量的调度机制以避免控制延迟,有利于电子商务网站在多变的负载条件下自适应地实现差别服务和QoS保障。仿真实验证明了MODP机制与算法的有效性,将其与传统FCFS调度方法进行对比研究,结果表明:服务器无论在高载还是低载情况下,MODP调度策略在实现收益最大化、平均响应时间最小化的目标方面都具有明显的优势。     

基于生产函数的效用优化云计算资源调度算法

针对云数据中心资源利用率低,云服务提供商收益低等问题,提出一种基于生产函数的云服务提供商收益最大化同时兼顾用户满意度的资源调度算法;该算法将资源调度分两阶段处理,首先合理规划云服务器所有资源,最优化配置资源,然后结合用户请求,云服务代理从资源池选择配置好的资源并分配资源给用户,通过两阶段的算法实现,解决了云数据中心资源利用率低,云服务提供商收益低等问题;最后通过与基于博弈的效用优化算法比较,仿真结果表明,该调度算法具有更好的性能。     

基于微服务架构FPGA云平台的并发请求调度机制

基于微服务架构的现场可编程门阵列（FPGA）云平台在被大规模推广后积累了许多用户。针对FPGA云平台存在大量用户并发请求的问题,建立一种基于优先级调度的自定义参数响应指数计算模型。将5个请求关键影响因素作为自定义参数,采用层次分析法确定各参数权重,根据响应指数函数计算各请求的响应指数。在该模型的基础上,设计一种高并发请求调度（HCRS）算法,通过响应指数阈值对请求类别进行划分,使得高优先级请求优先得到处理,次优先级请求加入先进先出队列等待,低优先级请求暂时挂起,从而缩短请求响应时间以及请求响应延时,缓解由高并发请求带来的硬件节点资源分配压力。在真实运营的FPGA云平台中实现该算法并在实际环境中进行测试,结果表明,在并发请求量相同时,与先来先服务调度算法相比,HCRS算法的平均响应延时降低29 074 ms,平均请求响应时间缩短12 605 ms,其在提升系统吞吐量与并发度的同时可以有效优化硬件节点资源利用率。     

基于资源权重最大资源利用率的动态资源调度算法

对于在云环境下虚拟机放置问题,资源调度管理是其中一项关键技术,因而提出了一种基于最大综合利用率为标准的动态资源调度算法。首先,在云系统调度中心缓存用户的资源请求,将此请求当做一个任务,然后利用负载均衡调度算法,调度中心针对不同任务来分配适当的物理机,最后在物理机上建立虚拟等待队列,充分考虑云系统中计算资源权重比,利用基于权重最大资源利用率的算法进行虚拟机配置。仿真实验结果证明,该算法可以实现有效负载均衡,并在保证一定的QoS的情况下,能有效提高系统的综合利用率。     

基于元胞自动机遗传算法的云资源调度

针对传统遗传算法易陷入早熟收敛等问题,提出一种改进的元胞自动机遗传算法(CGA),并将其应用于云环境下的资源调度。分析云计算环境中处理用户群请求的庞大任务数及资源合理分配的问题,利用CGA算法寻求一组最优的资源调度方案。在CloudSim仿真平台上进行实验,结果表明,CGA算法能缩短任务完成时间,降低用户总成本,满足云环境下的资源调度要求。     

基于关联数据本地化的多密码作业流调度算法

针对云密码服务系统中服务请求多样、数据依赖性作业流与非数据依赖性作业流随机交叉并发等问题,为了避免处理节点之间关联数据的交互而带来的系统通信性能开销和数据安全性威胁,设计一种基于关联数据本地化的云密码作业流调度算法。首先通过任务请求密码功能的映射,保障多作业流请求密码功能的正确实现;然后对于具有相同请求密码功能的各任务中不同工作模式交叉问题,在提出任务优先级计算方法以促进多作业流调度公平性的基础上,采用分类调度的方法,在实现关联数据本地化的同时,保障了调度系统的整体性能。仿真结果表明,该算法不仅可以有效减少系统任务完成时间,提高资源利用率和公平性,并且具有良好的稳定性。     

基于多面体的分布式WiMAX Mesh网络带宽调度算法

在轮询带宽调度和随机带宽调度两种经典算法的基础上,提出一种基于多面体优先列表的带宽资源调度算法来分析和优化WiMAX网络Mesh模式的带宽请求和调度过程.该算法通过为各个用户站构建不同的静态优先请求次序,避免或者减少了"带宽资源分配不均"现象,使带宽资源能够在网络局部得到优化调度,并扩展到整个无线网络.分析和仿真结果表明,本算法能够极大地改善整个网络的运行性能.     

实时VOD系统的调度策略

基于对VOD系统用户请求的分析,提出并实现了基于内存的实时调度策略.定量分析了用户请求的特征,并在此基础上提出和实现了FCFS、SATF和HPFS调度算法,并对其中的各特征参数进行了讨论.     

基于仿生自主神经系统的节能高效云调度研究*

为了实现兼顾性能和能耗的高效云调度管理机制,提出了一种基于仿生自主神经系统（BANS）的云调度管理系统。建立了理论模型来评估和分析重要的性能和能耗指标,并利用纯利润优化模型均衡性能和能耗之间的制约关系。基于理论分析结果,进一步利用最优性分析和自主触发机制实现了动态灵活的局部资源管理,同时,采用启发式算法来获取面向用户请求分发的全局最优调度策略。实验结果展示了重要的性能-能耗制约关系,同时也表明,相比传统负载均衡调度机制,局部自主资源管理可以在纯利润上带来约60%的显著提升,全局请求调度还将进一步带来约15%的提升效果。     

云平台调度监控系统的设计与实现

传统的云平台调度系统根据节点请求排队分配计算资源,效率低下;为提高云计算资源利用率,改进云软件服务能力,设计并实现了基于/proc文件面向云软件服务的监控与调度系统,系统采集并汇集节点内核信息,同时设计了分层调度,使系统可根据监控结果以及节点上传的请求情况来合理分配资源的调度系统,提高了系统资源利用率;最后进行了仿真实验,实验结果表明文章设计的系统资源利用率比传统系统提高了48%,具有极强的实际应用价值。     

Resource scheduling algorithm with load balancing for cloud service provisioning

Cloud computing uses scheduling and load balancing for virtualized file sharing in cloud infrastructure. These two have to be performed in an optimized manner in cloud computing environment to achieve optimal file sharing. Recently, Scalable traffic management has been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, latency reducing during multidimensional resource allocation still remains a challenge. Hence, there necessitates efficient resource scheduling for ensuring load optimization in cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The method constructs a Fuzzy-based Multidimensional Resource Scheduling model to obtain resource scheduling efficiency in cloud infrastructure. Increasing utilization of Virtual Machines through effective and fair load balancing is then achieved by dynamically selecting a request from a class using Multidimensional Queuing Load Optimization algorithm. A load balancing algorithm is then implemented to avoid underutilization and overutilization of resources, improving latency time for each class of request. Simulations were conducted to evaluate the effectiveness using Cloudsim simulator in cloud data centers and results shows that the proposed method achieves better performance in terms of average success rate, resource scheduling efficiency and response time. Simulation analysis shows that the method improves the resource scheduling efficiency by 7% and also reduces the response time by 35.5 % when compared to the state-of-the-art works.     

基于随机模型的云平台资源调度策略设计

针对云计算资源管理的实际需求,提出一种基于随机模型的云平台调度策略,设计合理高效的资源调度算法,解决传统代数模型请求丢失率高以及其他随机模型负载均衡指标性能较差的问题,从而在服务性能和执行效率的基础上保证服务器的资源负载,使云平台处于相对稳定的状态。在实验环境中的验证结果表明,该调度策略能够优化虚拟资源的使用效率和服务响应时间,同时能够达到较好的负载均衡并降低运营成本。     

基于纠删码的细粒度云存储调度方案

针对云存储系统中数据获取时延长以及数据下载不稳定的问题,提出了一种基于存储节点负载信息和纠删码技术的调度方案。首先,利用纠删码对文件进行编码存储以降低每份数据拷贝的大小,同时利用多个线程并发下载以提高数据获取的速度;其次,通过分析大量存储节点的负载信息确定影响时延的性能指标并对现有的云存储系统架构进行优化,设计了一种基于负载信息的云存储调度算法LOAD-ALGORITHM;最后,利用开源项目OpenStack搭建了一个云计算平台,根据真实的用户请求数据在云平台上进行部署和测试。实验结果表明,相比于现有的工作,调度算法在数据获取时延方面最高能减少15%的平均时延,在数据下载稳定性方面最高能降低40%的时延波动。该调度方案在真实的云平台环境下能有效地提高数据获取速度和稳定性,降低数据获取时延,达到更好的用户体验。     

A cost-effective power-aware approach for scheduling cloudlets in cloud computing environments

On a cloud platform, the user requests are managed through workload units called cloudlets which are assigned to virtual machines through cloudlet scheduling mechanism that mainly aims at minimizing the request processing time by producing effective small length schedules. The efficient request processing, however, requires excessive utilization of high-performance resources which incurs large overhead in terms of monetary cost and energy consumed by physical machines, thereby rendering cloud platforms inadequate for cost-effective green computing environments. This paper proposes a power-aware cloudlet scheduling (PACS) algorithm for mapping cloudlets to virtual machines. The algorithm aims at reducing the request processing time through small length schedules while minimizing energy consumption and the cost incurred. For allocation of virtual machines to cloudlets, the algorithm iteratively arranges virtual machines (VMs) in groups using weights computed through optimization and rescaling of parameters including VM resources, cost of utilization of resources, and power consumption. The experiments performed with a diverse set of configurations of cloudlets and virtual machines show that the PACS algorithm achieves a significant overall performance improvement factor ranging from 3.80 to 23.82 over other well-known cloudlet scheduling algorithms..

     

Workload-based multi-task scheduling in cloud manufacturing

Cloud manufacturing is an emerging service-oriented business model that integrates distributed manufacturing resources, transforms them into manufacturing services, and manages the services centrally. Cloud manufacturing allows multiple users to request services at the same time by submitting their requirement tasks to a cloud manufacturing platform. The centralized management and operation of manufacturing services enable cloud manufacturing to deal with multiple manufacturing tasks in parallel. An important issue with cloud manufacturing is therefore how to optimally schedule multiple manufacturing tasks to achieve better performance of a cloud manufacturing system. Task workload provides an important basis for task scheduling in cloud manufacturing. Based on this idea, we present a cloud manufacturing multi-task scheduling model that incorporates task workload modelling and a number of other essential ingredients regarding services such as service efficiency coefficient and service quantity. Then we investigate the effects of different workload-based task scheduling methods on system performance such as total completion time and service utilization. Scenarios with or without time constraints are separately investigated in detail. Results from simulation experiments indicate that scheduling larger workload tasks with a higher priority can shorten the makespan and increase service utilization without decreasing task fulfilment quality when there is no time constraint. When time constraint is involved, the above strategy enables more tasks to be successfully fulfilled within the time constraint, and task fulfilment quality also does not deteriorate.     

云计算环境下实训教学平台动态迁移策略

云服务环境下最大特点是按需交付,通过虚拟化技术将相关资源构建统一调度池,并且按照用户需求为用户提供服务,因此,云服务具有并行计算、开放性以及按需交付特性.对于实训教学平台来说,在云计算环境下需要面对各种用户需求,如请求任务各种各样,实验任务类型不尽相同,设备资源存在较大差异,通过虚拟化技术来实现规范化管理何资源共享,对云资源进行调度来才能有效满足用户需求,为此,在本文中提出了云计算环境下实训教学平台动态迁移策略.策略设计了三层协同资源调度机制来实现对资源和任务管理,重点研究了任务分割、资源划分、资源调度策略等,在此基础上对系统进行仿真实验,验证云计算环境下实训教学平台动态迁移策略可行与有效性.     

Formally modeling and analyzing cost‐aware job scheduling for cloud data center

With the rapid development of cloud computing, many distributed data centers have been deployed. This means larger energy consumption requirements from the data center. How to reduce the cost of data center has received significant attention recently. Although there are several efforts in studying energy consumption of the data center, very few have considered modeling and analyzing cost‐aware job scheduling for the cloud data center. To address this emerging problem, we propose a systematic approach that considers both basic elements and their relationships in cloud data center. First, we present a formal language to describe the cloud data center, and a job scheduling net is proposed to formally model the basic elements such as user request, Web portal, data center, and server. Second, we minimize the total cost of the cloud data center by considering the multidimensional resource and local electricity price on the basis of the state space of constructed model. The dynamic job scheduling algorithm and its specific execution steps are proposed based on the alternating direction method of multipliers algorithm. Third, the operational semantics and related theories of Petri nets for establishing the correctness of our proposed method are presented. Finally, a series of simulations are performed to illustrate that the proposed method can guarantee the correct behavior of job scheduling in the cloud data center while meeting the required cost.     

基于概率模型检验的云渲染任务调度定量验证

云渲染技术已被广泛应用于影视和动漫等行业.与传统的渲染农场和租赁市场模式不同，云渲染系统依托云计算基础设施提供多种软件服务进行渲染作业的方式，正逐渐成为新兴的计算模式.由于任务执行和资源操作等作业调度对于用户而言是透明的，这要求云渲染系统应具备智能化以实现计算资源优化调度和多端任务管理，并对系统可靠性提出了更高要求.针对这一问题，提出了采用概率模型检验对云渲染系统任务调度进行定量评估.首先，考虑渲染服务失效等因素引发的随机系统异常和指令错误，如文件损坏和渲染任务超时等，提出了基于离散马尔可夫链（DTMC）的概率模型对云渲染系统的文件准备模块、资源请求模块、渲染任务执行模块进行形式化建模；其次，从服务质量属性角度提出了9类验证性质用于定义云渲染系统的可靠性，采用概率计算树逻辑（PCTL）描述检验性质公式并执行工具PRISM计算和验证渲染系统可靠性；最后，结合案例和实验证明了该方法的可行性和有效性，尤其是对改进前后云渲染系统进行定量检验，可用于指导如何进行失效恢复和任务切换.因此，该方法在一定程度上可提高云渲染系统的可靠性.