Policy based resource allocation in IaaS cloud

In present scenario, most of the Infrastructure as a Service (IaaS) clouds use simple resource allocation policies like immediate and best effort. Immediate allocation policy allocates the resources if available, otherwise the request is rejected. Best-effort policy also allocates the requested resources if available otherwise the request is placed in a FIFO queue. It is not possible for a cloud provider to satisfy all the requests due to finite resources at a time. Haizea is a resource lease manager that tries to address these issues by introducing complex resource allocation policies. Haizea uses resource leases as resource allocation abstraction and implements these leases by allocating Virtual Machines (VMs). Haizea supports four kinds of resource allocation policies: immediate, best effort, advanced reservation and deadline sensitive. This work provides a better way to support deadline sensitive leases in Haizea while minimizing the total number of leases rejected by it. Proposed dynamic planning based scheduling algorithm is implemented in Haizea that can admit new leases and prepare the schedule whenever a new lease can be accommodated. Experiments results show that it maximizes resource utilization and acceptance of leases compared to the existing algorithm of Haizea.     

Combinatorial auction-based allocation of virtual machine instances in clouds

Most of the current cloud computing providers allocate virtual machine instances to their users through fixed-price allocation mechanisms. We argue that combinatorial auction-based allocation mechanisms are especially efficient over the fixed-price mechanisms since the virtual machine instances are assigned to users having the highest valuation. We formulate the problem of virtual machine allocation in clouds as a combinatorial auction problem and propose two mechanisms to solve it. The proposed mechanisms are extensions of two existing combinatorial auction mechanisms. We perform extensive simulation experiments to compare the two proposed combinatorial auction-based mechanisms with the currently used fixed-price allocation mechanism. Our experiments reveal that the combinatorial auction-based mechanisms can significantly improve the allocation efficiency while generating higher revenue for the cloud providers.     

Heavy traffic optimal resource allocation algorithms for cloud computing clusters

Cloud computing is emerging as an important platform for business, personal and mobile computing applications. In this paper, we study a stochastic model of cloud computing, where jobs arrive according to a stochastic process and request resources like CPU, memory and storage space. We consider a model where the resource allocation problem can be separated into a routing or load balancing problem and a scheduling problem. We study the join-the-shortest-queue routing and power-of-two-choices routing algorithms with the MaxWeight scheduling algorithm. It was known that these algorithms are throughput optimal. In this paper, we show that these algorithms are queue length optimal in the heavy traffic limit.     

电子政务基础架构云的容量规划方法研究

云计算技术的引入对电子政务的深化推进起到了积极的作用。鉴于基础架构云的受众面广且实现了资源的共享调配,科学地进行容量规划显得尤为重要。通过分析电子政务基础架构云的资源消耗规律和影响因素,提出利用离散概率分布算法对容量进行合理规划,结合具体应用示例进行方法验证。结果表明该方法可对电子政务基础架构云的资源容量进行有效预判,对提升基础架构管理有实际意义,并可为同类项目提供参考。     

云计算环境下数字图书馆资源合理配置仿真

云计算环境下数字图书馆资源的合理配置在提高资源合理利用方面的意义重大。对图书馆资源进行合理配置时,需要对数字图书馆资源的资源节点和资源模块进行计算。传统方法主要根据数字图书馆的资源聚类系数进行配置,忽略了资源配置时间的影响,导致出现效性差问题,提出基于粒子群算法的云计算环境下数字图书馆资源合理配置方法。利用数字图书馆资源节点的连接度和总度数来分析相连节点的概率,并通过聚类函数计算数字图书馆全部资源节点的平均值和资源配置总流程时间,分析资源配置的质量指标,建立资源配置模型,计算数字图书馆资源配置的适应度值,并利用资源配置速度向量计算资源消耗,得到资源消耗量,采用迭代次数对资源配置的模块数量进行计算,实现云计算环境下数字图书馆资源的合理配置。仿真结果表明,所提的资源配置方法具有有效性和合理性。     

基于神经网络改进的云环境下暴发式请求部署策略研究

针对暴发式任务请求给云计算系统性能带来的影响,结合现有资源部署模型,提出了一种基于误差反向传播神经网络改进的资源部署模型来应对上述问题。模型判断出暴发式任务请求的始末时,自动启动网络模块,通过事先训练好的网络进行参数调整值的预测,以达到动态跟踪云计算系统底层资源与外界任务请求变化的目的。通过CloudSim对模型进行了仿真实验,结果证明,引入神经网络模块可有效提高现有系统的资源部署响应速度。     

基于服务级别协议的云资源分配

与网格、集群等传统计算模式不同,云计算为用户提供了一种利用远程计算资源的实用商业模型。在不同的客户之间动态分配云资源池以获得最大收入,成为云服务提供商最为关心的问题。云计算中心需要把面向客户的服务层指标转换为面向系统的操作层指标,根据服务级别协议动态管理云计算资源。研究了基于服务级别协议的服务提供商收入最大化问题,借助排队论模型对资源分配问题进行了形式化描述,然后依据定价机制、服务请求到达率、服务率、可用资源等因素给出了资源最优分配方案。实验结果表明,该算法优于相关算法。     

云计算环境下兼顾买卖双方利益的双向拍卖资源分配算法

针对当前云计算环境下的资源分配算法不能充分考虑买卖双方利益的问题,本文提出了一种适用于云计算环境的组合双向拍卖资源分配模型。首先,初始化云经纪人列表和供应商报价列表,拍卖人通知拍卖参与者拍卖开始;然后,根据属性值按升序排序云经纪人请求和云服务供应商报价列表,从而确定投标获胜者;最后,获胜的云经纪人向相关云服务供应商发送任务并支付费用,云服务商执行任务。仿真实验使用CloudSim原型化,在基于Java的仿真云环境中从经济角度进行了效率评估。仿真结果表明,本文模型适用于云环境中的资源分配,在经济上非常有效。相比其他的现有模型,本文模型更能鼓励参与者在买卖双方公平公正的前提下根据真实估值竞购资源。     

云计算环境下基于主动预测的节点部署模型研究

随着云计算的不断普及,越来越多的用户选择将自身的业务迁移至云计算系统。 用户的使用习惯与社会日常的运行规律 也伴随着大量用户涌入云计算系统,如每早8点集中地向云计算系统申请资源节点,这给系统带来了一种可预期的资源冲击。针对上述问题,提出了一种基于主动预测模式的资源部署模型。该模型首先根据预测模块中Holt-Winters季节指数平滑模型的算法周期长度来预测下一个时间周期的任务请求量,通过设计的主动预测算法判断是否应对当前的任务请求量做出响应并得出其具体数量、位置等参数指标,以实现对用户使用规律的主动应对。使用CloudSim进行仿真实验,系统地评判模型的性能。实验结果表明,AF-HW模型在应对可预测的海量并发任务请求时可有效地提升单点及整体的响应速率,使用户得到更好的体验。     

Business-driven short-term management of a hybrid IT infrastructure

We consider the problem of managing a hybrid computing infrastructure whose processing elements are comprised of in-house dedicated machines, virtual machines acquired on-demand from a cloud computing provider through short-term reservation contracts, and virtual machines made available by the remote peers of a best-effort peer-to-peer (P2P) grid. Each of these resources has different cost basis and associated quality of service guarantees. The applications that run in this hybrid infrastructure are characterized by a utility function: the utility gained with the completion of an application depends on the time taken to execute it. We take a business-driven approach to manage this infrastructure, aiming at maximizing the profit yielded, that is, the utility produced as a result of the applications that are run minus the cost of the computing resources that are used to run them. We propose a heuristic to be used by a contract planner agent that establishes the contracts with the cloud computing provider to balance the cost of running an application and the utility that is obtained with its execution, with the goal of producing a high overall profit. Our analytical results show that the simple heuristic proposed achieves very high relative efficiency in the use of the hybrid infrastructure. We also demonstrate that the ability to estimate the grid behaviour is an important condition for making contracts that allow such relative efficiency values to be achieved. On the other hand, our simulation results with realistic error predictions show only a modest improvement in the profit achieved by the simple heuristic proposed, when compared to a heuristic that does not consider the grid when planning contracts, but uses it, and another that is completely oblivious to the existence of the grid. This calls for the development of more accurate predictors for the availability of P2P grids, and more elaborated heuristics that can better deal with the several sources of non-determinism present in this hybrid infrastructure.     

Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems

Resource management remains one of the main issues of cloud computing providers because system resources have to be continuously allocated to handle workload fluctuations while guaranteeing Service Level Agreements (SLA) to the end users. In this paper, we propose novel capacity allocation algorithms able to coordinate multiple distributed resource controllers operating in geographically distributed cloud sites. Capacity allocation solutions are integrated with a load redirection mechanism which, when necessary, distributes incoming requests among different sites. The overall goal is to minimize the costs of allocated resources in terms of virtual machines, while guaranteeing SLA constraints expressed as a threshold on the average response time. We propose a distributed solution which integrates workload prediction and distributed non-linear optimization techniques. Experiments show how the proposed solutions improve other heuristics proposed in literature without penalizing SLAs, and our results are close to the global optimum which can be obtained by an oracle with a perfect knowledge about the future offered load.     

混合云中的一个高效协调器

云计算提供了4种部署模型:公有云、私有云、社区云和混合云.通常,一个私有云中可用的资源是有限的,因此云用户不得不从公有云租用资源.这意味着云用户将会产生额外的费用.越来越多的企业选择混合云来部署它们的应用.在混合云中,为了实现用户的利益最大化,必须满足使用资源的费用最小化和用户的QoS,为此为混合云用户提供了一个既能最小化资源费用又能保证满足QoS的资源分配方法.实验结果表明,该算法在保持低操作成本的同时还满足了用户的QoS.     

云环境下面向暴发式任务请求的资源部署模型设计

针对云计算环境面临的暴发式任务请求对系统性能带来的影响,提出了一种资源部署模型BWA来应对上述问题.首先由模型的负载监听模块负责监测云计算系统任务请求的变化量,实时判断暴发式任务请求的始末.然后通过引入新的资源部署策略,来避免局部热点的产生,加快系统的响应速度.最后利用跟踪预测算法预置计算节点来进一步加快云计算系统为用户提供服务的速率.通过CloudSim对资源部署模型进行了实验仿真,结果证明,该模型可有效优化系统响应速度.