云计算环境下的自适应资源管理技术综述

云环境下的自适应资源管理是当前云计算研究领域的热点问题,是云计算具备弹性扩展、动态分配和资源共享等特点的关键技术支撑,具有重要的理论意义和实用价值.其主要研究点包括:虚拟机放置优化算法,虚拟资源动态伸缩模型、多IDC间的全局云计算资源调度、全局资源配置及能力规划模型等.对云环境下自适应资源管理研究现状进行分析研究,并指出当前研究中存在的一些主要问题,同时进一步展望本领域未来的研究方向.     

An adaptive switching scheme for iterative computing in the cloud

Delta-based accumulative iterative computation (DAIC) model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAIC model and the asynchronous DAIC model only satisfy some given conditions, respectively, and perform poorly under other conditions either for high synchronization cost or for many redundant activations. As a result, the whole performance of both DAIC models suffers fromthe serious network jitter and load jitter caused bymultitenancy in the cloud. In this paper, we develop a system, namely HybIter, to guarantee the performance of iterative algorithms under different conditions. Through an adaptive execution model selection scheme, it can efficiently switch between synchronous and asynchronous DAIC model in order to be adapted to different conditions, always getting the best performance in the cloud. Experimental results show that our approach can improve the performance of current solutions up to 39.0%.     

ARMCO: Advanced topics in resource management for ubiquitous cloud computing: An adaptive approach

Cloud Computing can be seen as one of the latest major evolution in computing offering unlimited possibility to use ICT in various domains: business, smart cities, medicine, environmental computing, mobile systems, design and implementation of cyber-infrastructures. The recent expansion of Cloud Systems has led to adapting resource management solutions for large number of wide distributed and heterogeneous datacenters. The adaptive methods used in this context are oriented on: self-stabilizing, self-organizing and autonomic systems; dynamic, adaptive and machine learning based distributed algorithms; fault tolerance, reliability, availability of distributed systems. The pay-per-use economic model of Cloud Computing comes with a new challenge: maximizing the profit for service providers, minimizing the total cost for customers and being friendly with the environment.This special issue presents advances in virtual machine assignment and placement, multi-objective and multi-constraints job scheduling, resource management in federated Clouds and in heterogeneous environments, dynamic topology for data distribution, workflow performance improvement, energy efficiency techniques and assurance of Service Level Agreements.     

An adaptive switching scheme for iterative computing in the cloud ,

Delta-based accumulative iterative computation （DAIC） model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAIC model and the asynchronous DAIC model only satisfy some given conditions, respectively, and perform poorly under other conditions either for high synchronization cost or for many redundant activations. As a result, the whole performance of both DAIC models suffers from the serious network jitter and load jitter caused by multi- tenancy in the cloud. In this paper, we develop a system, namely Hyblter, to guarantee the performance of iterative algorithms under different conditions. Through an adaptive execution model selection scheme, it can efficiently switch between synchronous and asynchronous DAIC model in order to be adapted to different conditions, always getting the best performance in the cloud. Experimental results show that our approach can improve the performance of current solutions up to 39.0%.     

云计算架构下的动态副本管理策略

为有效管理云计算环境中的数据副本,减少系统带宽消耗、最小化响应时间和平衡负载,提出了一种动态副本管理策略.通过建立文件可用性和副本数量间的关系模型来计算系统应维持的最小副本数量;基于数据被请求访问的次数和传输花费进行副本放置;数据被请求时结合节点间带宽和节点效用选择副本.实验结果表明了该策略的正确性有效性.     

Efficient resource management for virtual desktop cloud computing

In virtual desktop cloud computing, user applications are executed in virtual desktops on remote servers. This offers great advantages in terms of usability and resource utilization; however, handling a large amount of clients in the most efficient manner poses important challenges. Especially deciding how many clients to handle on one server, and where to execute the user applications at each time is important. Assigning too many users to one server leads to customer dissatisfaction, while assigning too little leads to higher investments costs. We study different aspects to optimize the resource usage and customer satisfaction. The results of the paper indicate that the resource utilization can increase with 29% by applying the proposed optimizations. Up to 36.6% energy can be saved when the size of the online server pool is adapted to the system load by putting redundant hosts into sleep mode.     

Credit-based scheme for security-aware and fairness-aware resource allocation in cloud computing

Cloud computing systems include different types of participants with varied requirements for resources and multiple tasks; these varying requirements must be considered in the design of fairness-aware resource allocation schemes for better resources sharing. However, some participants may be malicious with a goal to damage the resource allocation fairness and increase their own utility. Hence, the resource scheduling policy must guarantee allocation fairness among the participants; further, it must ensure that fairness is not affected by the malicious usage of resources, that could cause resource exhaustion and lead to denial of service. In order to address this challenge, we propose a credit-based mechanism for resource allocation that will avoid the malicious usage of resources and, simultaneously, guarantee allocation fairness. In our scheme, a credit factor is introduced for each participant in order to evaluate the history of resource utilization and determine future resource allocation. Our model encourages a participant to release the occupied resources in timely manner after the completion of a task and imposes a punishment for malicious occupation of resources. We prove the fairness of our model and provide linear and variable gradient approaches to determine the credit factor for different scenarios. We simulate our model and perform experiments on a real cloud computing platform. The results prove the rationality, effectiveness and correctness of our approaches.     

容错云计算网络中自适应故障检测方案研究

当前大多数故障检测(FD)技术无法针对动态网络条件自动调整它们的检测服务参数,导致无法应用于实际环境中。针对容错云计算网络中的故障检测特性进行研究,提出了一种通用的容错云计算网络自适应故障检测(SFD)算法;基于上述通用算法,对当前故障检测器进行优化,提出了一种自适应累积型故障检测器,并对SFD算法的部署进行了研究。基于七种典型的广域网案例,比较评估了该故障检测算法与当前其他算法的性能。实验结果表明,该算法可以实现故障的自适应检测,满足用户需求,拥有较高的系统性能。     

云计算环境下动态资源碎片管理机制

针对云计算环境下用户所需资源与服务资源规格不完全相符以及在资源预留过程中完整资源被切割而产生的资源碎片问题,提出一种云环境下考虑碎片资源重利用的动态资源管理策略。研究了云计算环境下资源碎片的形成原因,构建了碎片资源池,制定了任务对碎片资源接收的度量标准,在充分考虑当前任务对资源查找、调度、匹配的同时,进一步讨论了任务调度对资源的分割情况,进而指出资源碎片对后续任务接收能力的影响,搭建了云计算环境下动态资源碎片调度模型。理论分析和Cloudsim仿真实验证明,该资源管理策略能有效实现碎片资源的优化重组,提高了资源对后续任务的接收能力,与此同时保证了较高的资源利用率。     

An adaptive model-free resource and power management approach for multi-tier cloud environments

With the development of cloud environments serving as a unified infrastructure, the resource management and energy consumption issues become more important in the operations of such systems. In this paper, we investigate adaptive model-free approaches for resource allocation and energy management under time-varying workloads and heterogeneous multi-tier applications. Specifically, we make use of measurable metrics, including throughput, rejection amount, queuing state, and so on, to design resource adjustment schemes and to make control decisions adaptively. The ultimate objective is to guarantee the summarized revenue of the resource provider while saving energy and operational costs. To validate the effectiveness, performance evaluation experiments are performed in a simulated environment, with realistic workloads considered. Results show that with the combination of long-term adaptation and short-term adaptation, the fluctuation of unpredictable workloads can be captured, and thus the total revenue can be preserved while balancing the power consumption as needed. Furthermore, the proposed approach can achieve better effect and efficiency than the model-based approaches in dealing with real-world workloads.     

An identity authentication scheme based on cloud computing environment

In order to solve the shortcomings of traditional identity authentication technology, such as low security, low efficiency, a mobile terminal identity authentication scheme based on cloud computing environment is proposed in this paper. In addition, the two-dimensional code technology is used for identity authentication in the cloud computing environment, and the QR coding technology is also used. The dynamic authentication of the mobile terminal is realized by using the two-dimensional code as the information transmission carrier. According to the security analysis, the scheme has simple structure and no need to use the third party equipment, which has high security and adaptability. Finally, the two fusion of two-dimensional code proposed in this paper provides a new way of thinking for the identity authentication based on the cloud environment, and also promotes the development of the Internet of things.     

高能物理云平台中的弹性计算资源管理机制

虚拟化技术作为一种新的资源管理技术,正在高能物理领域得到越来越广泛的应用。静态虚拟机集群方式已经逐渐不能满足多作业队列对于计算资源动态的需求。为此,实现了一种云计算环境下面向多作业队列的弹性计算资源管理系统。系统通过高吞吐量计算系统HTCondor运行计算作业,使用开源的云计算平台Openstack管理虚拟计算节点,给出了一种结合虚拟资源配额服务,基于双阈值的弹性资源管理算法,实现资源池整体伸缩,同时设计了二级缓冲池以提高伸缩效率。目前系统已部署在高能所公共服务云IHEPCloud上,实际运行结果表明,当计算资源需求变化时系统能够动态调整各队列虚拟计算节点数量,同时计算资源的CPU利用率相比传统的资源管理方式有显著的提高。     

一种基于云计算的微电网能源管理方案

针对微电网资源配置与优化、潮流分析、经济调度等问题,可通过基于云计算的分层控制结构进行处理.该结构可通过更加经济的方式进行资源配置与优化,同时集成分析历史数据与实时数据,为用户提供更精确的分析和更灵活的服务.     

云计算环境下的DPSO资源负载均衡算法

负载均衡问题是云计算研究的热点问题之一。运用离散粒子群算法对云计算环境下的负载均衡问题进行研究,根据云计算环境下资源需求动态变化,并且对资源节点服务器的要求较低的特点,把各个资源节点当做网络拓扑结构中的各个节点,建立相应的资源-任务分配模型,运用离散粒子群算法实现资源负载均衡。验证表明,该算法提高了资源利用率和云计算资源的负载均衡。     

Dynamic cloud resource management for efficient media applications in mobile computing environments

Single-instruction-set architecture (Single-ISA) heterogeneous multi-core processors (HMP) are superior to Symmetric Multi-core processors in performance per watt. They are popular in many aspects of the Internet of Things, including mobile multimedia cloud computing platforms. One Single-ISA HMP integrates both fast out-of-order cores and slow simpler cores, while all cores are sharing the same ISA. The quality of service (QoS) is most important for virtual machine (VM) resource management in multimedia mobile computing, particularly in Single-ISA heterogeneous multi-core cloud computing platforms. Therefore, in this paper, we propose a dynamic cloud resource management (DCRM) policy to improve the QoS in multimedia mobile computing. DCRM dynamically and optimally partitions shared resources according to service or application requirements. Moreover, DCRM combines resource-aware VM allocation to maximize the effectiveness of the heterogeneous multi-core cloud platform. The basic idea for this performance improvement is to balance the shared resource allocations with these resources requirements. The experimental results show that DCRM behaves better in both response time and QoS, thus proving that DCRM is good at shared resource management in mobile media cloud computing.     

云计算环境中高效可扩展的元数据管理方法

针对现有可扩展的元数据管理方法存在性能较低问题,提出一种高效可扩展的元数据管理方法,它首先采用动态二叉映射树来实现元数据服务器精确定位,然后采用延迟更新方法来动态更新二叉映射树,最后提出动态K叉编码树的元数据组织方法以提高元数据服务器扩展时选择迁移元数据的速度。实验结果表明,它有效提高了云计算环境中可扩展元数据管理方法的效率。     

基于云环境下的资源评价模型研究

分析了己有的调度机制和常用的任务调度算法,并在此基础上设计了资源评价模型。将资源评价模型加入调度系统中,资源信息由评价模块进行分析和评价,并提供给任务调度器,实现资源和任务的优化匹配,提高了服务质量(QoS)。     

云计算环境下资源的节能调度

在云计算环境下,如何在有效地进行资源调度,缩短任务执行时间的同时,降低能耗,已经成为一个重要问题。对此,以任务执行时间与能耗作为优化目标,建立了一个节能调度模型,并通过采用特殊的种群初始化方法以及引入学习机制等方法对非支配排序遗传算法(NSGA-Ⅱ)进行改进,将其应用于云计算的节能调度问题。最后通过算例测试,验证了所提算法能够在减少任务执行时间的同时,有效降低能耗。     

移动云计算中基于身份的轻量级加密方案

针对移动云计算中数据安全和移动用户的隐私保护问题,结合在线离线和外包解密技术,对基于身份加密机制（IBE）中加密和解密算法进行扩展,提出了一种可外包解密的基于身份在线离线加密方案,并证明其安全性,构造出适合于移动云计算环境中轻量级设备保护隐私数据的方案。为了减少移动终端运行IBE的加密和解密开销,利用在线离线技术将IBE的加密分解为离线和在线两个阶段,使得移动设备仅需执行少量简单计算即可生成密文;在此基础上,利用外包解密技术,修改IBE的密钥生成算法和解密算法,增加一个密文转化算法,将解密的大部分复杂计算外包给云服务器,移动设备仅计算一个幂乘运算即可获得明文。与现有IBE方案的性能相比,该方案具有较少的加解密开销,适合于轻量级移动设备。