An efficient energy-aware method for virtual machine placement in cloud data centers using the cultural algorithm

Excessive consumption of energy in cloud data centers whose number is increasing day by day has led to substantial problems. Hence, offering efficient schemes for virtual machine (VM) placement to decrease energy consumption in cloud computing environments has become a significant research field in recent years. In this paper, with the goal of reducing energy consumption in cloud data centers, we present a VM placement method using the cultural algorithm. In the proposed algorithm called balance-based cultural algorithm for virtual machine placement (BCAVMP), a new fitness function is introduced to evaluate VM allocation solutions. In this function, by using the sum of balance vector lengths for each VM placement, balanced utilization of resources is considered. Also, by applying the amount of energy usage in the fitness function, solutions with lower energy consumption are intended. The performance of the proposed method is evaluated using CloudSim simulator. The simulation results indicate that by appropriate VM assignment and resource wastage reduction, energy consumption in cloud data centers can be decreased.

     

DFA-VMP: An efficient and secure virtual machine placement strategy under cloud environment

The problem of Virtual Machine (VM) placement is critical to the security and efficiency of the cloud infrastructure. Nowadays most research focuses on the influences caused by the deployed VM on the data center load, energy consumption, resource loss, etc. Few works consider the security and privacy issues of the tenant data on the VM. For instance, as the application of virtualization technology, the VM from different tenants may be placed on one physical host. Hence, attackers may steal secrets from other tenants by using the side-channel attack based on the shared physical resources, which will threat the data security of the tenants in the cloud computing. To address the above issues, this paper proposes an efficient and secure VM placement strategy. Firstly, we define the related security and efficiency indices in the cloud computing system. Then, we establish a multi-objective constraint optimization model for the VM placement considering the security and performance of the system, and find resolution towards this model based on the discrete firefly algorithm. The experimental results in OpenStack cloud platform indicates that the above strategy can effectively reduce the possibility of malicious tenants and targeted tenants on the same physical node, and reduce energy consumption and resource loss at the data center.     

混合计算环境中截止期约束下的科学工作流调度策略

整合云和网格基础设施,增强科研机构现有网格系统的计算能力并向应用提供截止时间保障的服务是科学研究领域的热点。在这种"网格-云"混合计算环境中,对何时租借云虚拟资源以及如何租借做出有效决策是一个难题。现有的一些调度策略主要在网格资源静态能力特征的基础上,以作业等待时间作为决策依据,缺乏对资源动态服务能力的有效评估,无法保证科学应用的截止时间需求。本文提出了一种混合环境下的科学工作流执行系统架构并对其核心组件进行了阐述。针对其中的工作流调度问题,利用随机服务模型建模已有网格系统中的资源的动态服务能力,以任务违约风险作为是否租借外部虚拟资源的判断指标,提出了一个科学工作流调度算法HCA_SASWD。实验结果表明,HCA_SASWD相对于其他算法,能有效保证用户的截止时间要求,为需要提供截止时间保障的系统架构提供了参考。     

Software consolidation as an efficient energy and cost saving solution

Virtual machines (VM) are used in cloud computing environments to isolate different software. They also support live migration, and thus dynamic VM consolidation. This possibility can be used to reduce power consumption in the cloud. However, consolidation in cloud environments is limited due to reliance on VMs, mainly due to their memory overhead. For instance, over a 4-month period in a real cloud located in Grenoble (France), we observed that 805 VMs used less than 12% of the CPU (of the active physical machines). This paper presents a solution introducing dynamic software consolidation. Software consolidation makes it possible to dynamically collocate several software applications on the same VM to reduce the number of VMs used. This approach can be combined with VM consolidation which collocates multiple VMs on a reduced number of physical machines. Software consolidation can be used in a private cloud to reduce power consumption, or by a client of a public cloud to reduce the number of VMs used, thus reducing costs. The solution was tested with a cloud hosting JMS messaging and Internet servers. The evaluations were performed using both the SPECjms2007 benchmark and an enterprise LAMP benchmark on both a VMware private cloud and Amazon EC2 public cloud. The results show that our approach can reduce the energy consumed in our private cloud by about 40% and the charge for VMs on Amazon EC2 by about 40.5%.     

一种基于资源预分配的虚拟机软实时调度方法

虚拟机技术作为云计算的重要技术之一,近年来得到广泛关注,但是由于虚拟机管理层的存在,导致语义鸿沟,使得实时应用程序、并发程序等在虚拟机上的运行性能受到影响。分析和研究了Xen虚拟机管理器的Credit调度算法,针对其在并发调度和软实时调度方面存在的不足,提出了改进调度算法,实现了算法的调度器原型。新的调度算法对软实时虚拟机进行Credit比例预分配,采用动态调度时间片机制,以non-work-conserving方式实现软实时任务周期调度,保障调度周期满足运行周期要求。通过区分并发和非并发软实时虚拟机,采取不同的调度策略,在满足资源利用率的基础上,确保实时任务的顺利运行。测试结果表明,该调度算法在对并发和非并发软实时任务调度上,具有良好的表现,较好满足了软实时应用调度需求。     

iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator

Simulation techniques have become a powerful tool for deciding the best starting conditions on pay-as-you-go scenarios. This is the case of public cloud infrastructures, where a given number and type of virtual machines (in short VMs) are instantiated during a specified time, being this reflected in the final budget. With this in mind, this paper introduces and validates iCanCloud, a novel simulator of cloud infrastructures with remarkable features such as flexibility, scalability, performance and usability. Furthermore, the iCanCloud simulator has been built on the following design principles: (1) it’s targeted to conduct large experiments, as opposed to others simulators from literature; (2) it provides a flexible and fully customizable global hypervisor for integrating any cloud brokering policy; (3) it reproduces the instance types provided by a given cloud infrastructure; and finally, (4) it contains a user-friendly GUI for configuring and launching simulations, that goes from a single VM to large cloud computing systems composed of thousands of machines.     

vSpec: workload-adaptive operating system specialization for virtual machines in cloud computing云计算环境下负载自适应的虚拟机操作系统定制化

In general, operating systems (OSs) are designed to mediate access to device hardware by applications. They process different kinds of system calls using an indiscriminate kernel with the same configuration. Applications in cloud computing platforms are constructed from service components. Each of the service components is assigned separately to an individual virtual machine (VM), which leads to homogeneous system calls on each VM. In addition, the requirements for kernel function and configuration of system parameters from different VMs are different. Therefore, the suit-to-all design incurs an unnecessary performance overhead and restricts the OS’s processing capacity in cloud computing. In this paper, we propose an adaptive model for cloud computing to resolve the conflict between generality and performance. Our model adaptively specializes the OS of a VM according to the resource-consuming characteristics of workloads on the VM. We implement a prototype of the adaptive model, vSpec. There are five classes of VM: CPU-intensive, memory-intensive, I/O-intensive, networkintensive and compound, according to the resource-consuming characteristics of the workloads running on the VMs. vSpec specializes the OS of a VM according to the VM class. We perform comprehensive experiments to evaluate the effectiveness of vSpec on benchmarks and real-world applications.     

An efficient job management of computing service using integrated idle VM resources for high-performance computing based on OpenStack

In recent years, various studies on OpenStack-based high-performance computing have been conducted. OpenStack combines off-the-shelf physical computing devices and creates a resource pool of logical computing. The configuration of the logical computing resource pool provides computing infrastructure according to the user’s request and can be applied to the infrastructure as a service (laaS), which is a cloud computing service model. The OpenStack-based cloud computing can provide various computing services for users using a virtual machine (VM). However, intensive computing service requests from a large number of users during large-scale computing jobs may delay the job execution. Moreover, idle VM resources may occur and computing resources are wasted if users do not employ the cloud computing resources. To resolve the computing job delay and waste of computing resources, a variety of studies are required including computing task allocation, job scheduling, utilization of idle VM resource, and improvements in overall job’s execution speed according to the increase in computing service requests. Thus, this paper proposes an efficient job management of computing service (EJM-CS) by which idle VM resources are utilized in OpenStack and user’s computing services are processed in a distributed manner. EJM-CS logically integrates idle VM resources, which have different performances, for computing services. EJM-CS improves resource wastes by utilizing idle VM resources. EJM-CS takes multiple computing services rather than single computing service into consideration. EJM-CS determines the job execution order considering workloads and waiting time according to job priority of computing service requester and computing service type, thereby providing improved performance of overall job execution when computing service requests increase.

     

Coalition theory based task scheduling algorithm using DLFC-NN model

Resource management and job scheduling are essential in today's cloud computing world. Due to task scheduling and users' diverse submission of large-scale requests, co-located VM instances negatively impacted the performance of leased VM instances. This workload further led to resource rivalry across co-located VMs. In order to address the aforementioned problems, numerous strategies have been presented, however, they fail to take the asynchronous nature of the cloud environment into account. To address this issue, a novel “CTA using DLFC-NN model” is proposed. This proposed approach combines the coalition theory and DLFC-NN techniques by including IRT-OPTICS for task size clustering, digital metrology based on ionized information (DMBII) for defect detection in virtue machines (VM), and the dynamic levy flight hamster optimization algorithm for processing time optimization of the clusters. However, the implementation of task scheduling in an online environment is limited by a number of presumptions or oversimplifications made by current scheduling systems. As a result, a unique coalition theory is applied to efficiently schedule activities. In addition, the DLFC-NN model is used to reduce resource consumption, span time, and be highly accurate and energy-efficient when working on both online and offline jobs. Nevertheless, while optimizing the clusters' overall execution time, earlier approaches only decreased the make-span time for task scheduling. However, the DLFC-NN model solves the computation problem by using a fully weighted bipartite graph and the pseudo method to determine the fitness of the least makespan time. The enhanced methodology used in this study reduces the scheduling cost and minimizes job completion times according to different task counts when compared to the existing techniques.     

Efficient and dynamic scaling of fog nodes for IoT devices

It is predicted by the year 2020, more than 50 billion devices will be connected to the Internet. Traditionally, cloud computing has been used as the preferred platform for aggregating, processing, and analyzing IoT traffic. However, the cloud may not be the preferred platform for IoT devices in terms of responsiveness and immediate processing and analysis of IoT data and requests. For this reason, fog or edge computing has emerged to overcome such problems, whereby fog nodes are placed in close proximity to IoT devices. Fog nodes are primarily responsible of the local aggregation, processing, and analysis of IoT workload, thereby resulting in significant notable performance and responsiveness. One of the open issues and challenges in the area of fog computing is efficient scalability in which a minimal number of fog nodes are allocated based on the IoT workload and such that the SLA and QoS parameters are satisfied. To address this problem, we present a queuing mathematical and analytical model to study and analyze the performance of fog computing system. Our mathematical model determines under any offered IoT workload the number of fog nodes needed so that the QoS parameters are satisfied. From the model, we derived formulas for key performance metrics which include system response time, system loss rate, system throughput, CPU utilization, and the mean number of messages request. Our analytical model is cross-validated using discrete event simulator simulations.     

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.     

Integrating QoS awareness with virtualization in cloud computing systems for delay-sensitive applications

Cloud computing provides scalable computing and storage resources over the Internet. These scalable resources can be dynamically organized as many virtual machines (VMs) to run user applications based on a pay-per-use basis. The required resources of a VM are sliced from a physical machine (PM) in the cloud computing system. A PM may hold one or more VMs. When a cloud provider would like to create a number of VMs, the main concerned issue is the VM placement problem, such that how to place these VMs at appropriate PMs to provision their required resources of VMs. However, if two or more VMs are placed at the same PM, there exists certain degree of interference between these VMs due to sharing non-sliceable resources, e.g. I/O resources. This phenomenon is called as the VM interference. The VM interference will affect the performance of applications running in VMs, especially the delay-sensitive applications. The delay-sensitive applications have quality of service (QoS) requirements in their data access delays. This paper investigates how to integrate QoS awareness with virtualization in cloud computing systems, such as the QoS-aware VM placement (QAVMP) problem. In addition to fully exploiting the resources of PMs, the QAVMP problem considers the QoS requirements of user applications and the VM interference reduction. Therefore, in the QAVMP problem, there are following three factors: resource utilization, application QoS, and VM interference. We first formulate the QAVMP problem as an Integer Linear Programming (ILP) model by integrating the three factors as the profit of cloud provider. Due to the computation complexity of the ILP model, we propose a polynomial-time heuristic algorithm to efficiently solve the QAVMP problem. In the heuristic algorithm, a bipartite graph is modeled to represent all the possible placement relationships between VMs and PMs. Then, the VMs are gradually placed at their preferable PMs to maximize the profit of cloud provider as much as possible. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed heuristic algorithm by comparing with other VM placement algorithms.     

A thermal-aware VM consolidation mechanism with outage avoidance

Efficient energy and temperature management techniques are essential elements for operators of cloud data centers. Dynamic virtual machine (VM) consolidation using live migration techniques presents a great opportunity for cloud service providers to adaptively reduce energy consumption and optimize their resource utilization. In recent studies, power consumption readings of individual physical hosts were chosen as the main monitoring parameters in their allocation policies, whereas very few have considered host temperature, which has shown to have a negative impact on server reliability, as a migration criterion. In this work, a thermal-aware VM consolidation mechanism is proposed for resource allocation optimization and server reliability assurance. We consider the variability in host temperature as a migration criterion to avoid outage incidents via having better VM consolidations. Extensive simulation results obtained from CloudSim show the promising performance of the proposed mechanism in energy saving while reducing the number of server outage incidents due to fluctuations in host temperature.     

分层云不确定推理系统及其逼近性能分析

为了解决目前基于云模型的智能控制和预测中规则数目随系统变量的个数呈指数增长的问题,设计分层云不确定性推理系统,并证明该系统的逼近性能。采用基于云理论的新的不确定性推理模型来设计分层云不确定性推理系统并给出解析表达式。证明分层云不确定性推理系统对致密集上函数的逼近能力。结果表明：分层云不确定性推理系统的输出结果计算式满足Stone-Weirstrass定理的3个假设条件,具有万能逼近性质。     

Cost-effective resource provisioning for multimedia cloud-based e-health systems

Recently, multimedia cloud is being considered as a new effective serving mode in e-Health area that meets the requirement of scalable and economic multimedia service for e-health. It can provide a flexible stack of powerful Virtual Machine (VM) resources of cloud like CPU, memory, storage, network bandwidth etc. on demand to manage e-health media services and applications (e.g. medical image/video retrieval, health video transcoding, streaming, video rendering, sharing and delivery) at lower cost. However, one major issue here is how to efficiently allocate VM resources dynamically based on e-health applications’ QoS demands and support energy and cost savings by optimizing the number of servers in use. In order to solve this problem, we propose a cost effective and dynamic VM allocation model based on Nash bargaining solution. With extensive simulations it is shown that the proposed mechanism can reduce the overall cost of running servers while at the same time guarantee QoS demand and maximize resource utilization in various dimensions of server resources.     

Utilization of workflow management system for virtual machine instance management on cloud

The concept of virtualization is one of the most important technologies to construct a cloud service, and especially hardware virtualization is indispensable for infrastructure as a service (IaaS) where the cloud offering, infrastructure, is usually provided as a pool of virtual machine (VM) instances. For that reason, many public IaaS clouds like Amazon Web Service and private cloud toolkits such as Eucalyptus and OpenStack provide users with methods for managing VM instances via APIs, command‐line tools, web services, and so on. These are, however, not easy to use or customize for the average end users, especially for those in scientific research areas who just want to perform their work on a cloud and do not need to know the underlying technologies that much. Utilizing workflow management systems (WfMSs) in managing VMs on a cloud can alleviate these difficulties. Users only need to describe parameters needed for VMs and enact the workflow on a workflow enactment engine using user‐friendly interfaces. We propose a management scheme for VM instances on a cloud with the WfMS in this paper. We present a preliminary study on integrating cloud and WfMS focusing on management of VM instances and show an early implementation for a proof of concept with detailed explanations and possible usage scenarios. Copyright © 2015 John Wiley & Sons, Ltd.     

基于线索化方法的嵌入式Java虚拟机性能优化技术研究

随着计算机的不断发展，逐渐呈现出了普适计算的模式．普遍认为，Java是适应普适计算的关键技术．分析了解释运行中利用线索化方法进行性能优化的技术，实现了基于直接线索化方法的嵌入式Java虚拟机的解释器性能优化方案，并对嵌入式Java虚拟机的参考实现和基于直接线索化的优化方案进行了性能对比．     

Priority scheduling with consolidation based backfilling algorithm in cloud

In cloud computing, scheduling plays an eminent role while processing enormous jobs. The paralle jobs utmost need parallel processing capabilities which leads to CPU underutilization mainly due to synchronization and communication among parallel processes. Researchers introduced several algorithms for scheduleing parallel jobs namely, Conservative Migration Consolidation supported Backfilling (CMCBF) and Aggressive Migration Consolidation supported Backfilling (AMCBF). The greatest challenge of a existing scheduling algorithm is to improve the data center utilization without affecting job responsiveness. Hence, this work proposes an Effective Multiphase Scheduling Approach (EMSA) to process the jobs. In EMSA, the jobs are initially preprocessed and batched together to avoid starvation and to mitigate unwanted delay. Later, an Associate Priority Method has been proposed which prioritizes the batch jobs to minimize the number of migrations. Finally, the prioritized jobs are scheduled using Priority Scheduling with BackFilling algorithm to utilize the intermediate idle nodes. Moreover, the virtualization technology partitions the computing capacity of the Virtual Machine (VM) into two-tier VM as foreground VM (FVM) and Background VM (BVM) to improve node utilization. Hence, Priority Scheduling with Consolidation based BackFilling algorithm has been deployed in a two-tier VM that processes the jobs by utilizing the VMs effectively. Experimental results show that the performance of the proposed work performs better than other existing algorithms by increasing the resource utilization by 8%.     

虚拟机迁移中一种新的物理主机异常状态检测算法

提出了一种新的物理主机异常状态检测算法PHSDA(Physical host status anomalous detection algorithm)。PHSDA算法包括两个阶段;在超负载检测中,采用一种迭代权重线性回归方法来预测物理资源的使用效率情况;在低负载检测中,利用多维物理资源的均方根来确定其资源使用阈值下限,避免异常状态的物理主机数量的增加; PHSDA检测算法配合迁移过程中后续的虚拟机选择策略和虚拟机放置策略,就可以形成一个全新的虚拟机迁移模型PHSDA-MMT-BFD。以CloudSim模拟器作为PHSDA的仿真环境。经PHSDA策略优化过后的新虚拟机迁移实验表明：比近几年的BenchMark迁移模型比较起来,可以很好的降低云数据中心的能量消耗,虚拟机迁移次数减少,云服务质量明显提高。     

公有云存储系统性能评测方法研究

随着云存储系统的迅速发展和广泛使用,许多企业开发者和个人用户将其应用从传统存储迁移至公有云存储系统,因此,云存储系统性能成为企业开发者和个人用户关注的焦点。由于传统测试难以模拟足够多的用户同时访问云存储系统;测试环境构建复杂,测试时间长,准备测试环境成本高;受网络因素及外界其他因素影响,评测结果不稳定。针对以上所述云存储系统性能评测的重点和难点,提出一种“云测试云”的公有云存储系统性能评测方法,该方法通过在云计算平台动态申请足够数量的实例,对公有云存储系统性能进行评测。首先,构建通用的性能评测框架,可动态伸缩申请实例,自动化部署评测工具及负载,控制并发访问云存储系统,自动释放实例及收集并反馈评测结果;其次,提出多维度的性能评测指标,涵盖不同典型应用、不同云存储接口;最后,提出一种可扩展通用的性能评测模型,该模型可以评测常见典型应用的性能,分析云存储性能影响因素,可适用于任何的公有云存储平台。为了验证该方法的可行性、合理性、通用性和可扩展性,利用所提方法对Amazon S3云存储系统进行性能评测,并使用s3cmd验证评测结果的准确性。实验结果表明,评测结果可以为企业开发者和个人用户提供参考意见。