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.     

VMMB: Virtual Machine Memory Balancing for Unmodified Operating Systems

Virtualization technology has been widely adopted in Internet hosting centers and cloud-based computing services, since it reduces the total cost of ownership by sharing hardware resources among virtual machines (VMs). In a virtualized system, a virtual machine monitor (VMM) is responsible for allocating physical resources such as CPU and memory to individual VMs. Whereas CPU and I/O devices can be shared among VMs in a time sharing manner, main memory is not amendable to such multiplexing. Moreover, it is often the primary bottleneck in achieving higher degrees of consolidation. In this paper, we present VMMB (Virtual Machine Memory Balancer), a novel mechanism to dynamically monitor the memory demand and periodically re-balance the memory among the VMs. VMMB accurately measures the memory demand with low overhead and effectively allocates memory based on the memory demand and the QoS requirement of each VM. It is applicable even to guest OS whose source code is not available, since VMMB does not require modifying guest kernel. We implemented our mechanism on Linux and experimented on synthetic and realistic workloads. Our experiments show that VMMB can improve performance of VMs that suffers from insufficient memory allocation by up to 3.6 times with low performance overhead (below 1%) for monitoring memory demand.     

云计算环境中的虚拟机同驻安全问题综述

在云计算环境中,为了实现资源共享,不同租户的虚拟机可能运行在同一台物理机器上,即虚拟机同驻,这将带来新的安全问题。为此,文章重点讨论同驻虚拟机所面临的一些新的安全威胁,包括资源干扰、隐蔽通道/侧信道、拒绝服务与虚拟机负载监听等,介绍现有虚拟机同驻探测方法,总结针对虚拟机同驻威胁的四种防御思路,并分析未来的研究趋势。     

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.

     

支持随机服务请求的云虚拟机按需物理资源分配方法

本文针对云平台按负载峰值需求配置处理机资源、提供单一的服务应用和资源需求动态变化导致资源利用率低下的问题,采用云虚拟机中心来同时提供多种服务应用.利用灰色波形预测算法对未来时间段内到达虚拟机的服务请求量进行预测,给出兼顾资源需求和服务优先等级的虚拟机服务效用函数,以最大化物理机的服务效用值为目标,为物理机内的各虚拟机动态配置物理资源.通过同类虚拟机间的全局负载均衡和多次物理机内各虚拟机的物理资源再分配,进一步增加服务请求量较大的相应类型的虚拟机的物理资源分配量.最后,给出了虚拟机中心基于灰色波形预测的按需资源分配算法ODRGWF.模拟实验表明所提算法能够有效提高云平台中处理机的资源利用率,对提高用户请求完成率以及服务质量都具有实际意义.     

Mapping of time-consuming multitask applications on a cloud system by multiobjective Differential Evolution

Cloud computing is on-demand provisioning of virtual resources aggregated together so that by specific contracts users can lease access to their combined power.Here we hypothesize a new form of service contract by means of which users do not explicitly require resources, but simply supply information about their time-consuming multitask applications and specify their needs through some quality of service (QoS) parameters. The individuation of the virtual machines (VMs) onto which map and execute them is left to the cloud manager. Unfortunately the task/node mapping, already known as NP-hard for conventional parallel systems, becomes more challenging when application tasks must be run on VMs hosted on heterogeneous and shared cloud nodes, and when it must comply with QoS requests too. To support this new cloud service, a novel mapper tool, based on a multiobjective Differential Evolution algorithm, is proposed. Such a tool defines the mapping of the tasks on the VMs with the aim to exploit as much as possible the available cloud resources without penalizing the execution time of the submitted applications and, at the same time, to respect users’ QoS requests.To reveal the robustness of this evolutionary tool, an experimental analysis on artificial time-consuming parallel applications, modeled as task interaction graphs, has been effected.     

云环境中基于cache共享的虚拟机同驻检测方法

云计算是一种新型计算模型,按需提供外包计算和存储服务,具有资源共享、多租户服务等特性.但是,它也面临着新的安全威胁,例如侧通道攻击.通过侧通道攻击,恶意用户可以突破虚拟机隔离性,以一种隐蔽的方式获取其他用户的私密信息.现有侧通道攻击方法缺乏对其他同驻虚拟机干扰的分析.然而,这种干扰在多租户云环境中是不可避免的.针对该问题,提出一种基于cache侧通道攻击的虚拟机同驻检测方法.该方法基于期望和熵分析了cache负载特征,采用基于聚类的方法提取cache负载特征,通过同驻检测策略实现虚拟机同驻检测.实验结果表明,该方法能够有效地提取cache负载特征,并以较高的成功率实现虚拟机同驻检测.同时进一步表明,侧通道攻击是云计算面临的一种重要安全挑战.     

HPC Cloud环境中基于网络Ⅰ/O负载的虚拟机放置算法

随着虚拟化技术和云计算技术的发展,越来越多的高性能计算应用运行在云计算资源上.在基于虚拟化技术的高性能计算云系统中,高性能计算应用运行在多个虚拟机之中,这些虚拟机可能放置在不同的物理节点上.若多个通信密集型作业的虚拟机放置在相同的物理节点上,虚拟机之间将竞争物理节点的网络Ⅰ/O资源,如果虚拟机对网络Ⅰ/O资源的需求超过物理节点的网络Ⅰ/O带宽上限,将严重影响通信密集型作业的计算性能.针对虚拟机对网络Ⅰ/O资源的竞争问题,提出一种基于网络Ⅰ/O负载均衡的虚拟机放置算法NLPA,该算法采用网络Ⅰ/O负载均衡策略来减少虚拟机对网络Ⅰ/O资源的竞争.实验表明,与贪心算法进行比较,对于同样的高性能计算作业测试集,NLPA算法在完成作业的计算时间、系统中的网络Ⅰ/O负载吞吐率、网络Ⅰ/O负载均衡3个方面均有更好的表现.     

基于拍卖的虚拟机动态供应和分配算法

当前云计算供应商通过定价算法或类似拍卖的算法来分配他们的虚拟机（VM）实例。然而,这些算法大多要求虚拟机静态供应,无法准确预测用户需求,导致资源未得到充分利用。为此,提出了一种基于组合拍卖的虚拟机动态供应和分配算法,在做出虚拟机供应决策时考虑用户对虚拟机的需求。该算法将可用的计算资源看成是“流体”资源,且这些资源根据用户请求可分为不同数量、不同类型的虚拟机实例。然后可根据用户的估价决定分配策略,直到所有资源分配完毕。基于Parallel Workload Archive（并行工作负载存档）的真实工作负载数据进行了仿真实验,结果表明该方法可保证为云供应商带来更高收入,提高资源利用率。     

Virtual machine cluster mobility in inter-cloud platforms

Modern cloud computing applications developed from different interoperable services that are interfacing with each other in a loose coupling approach. This work proposes the concept of the Virtual Machine (VM) cluster migration, meaning that services could be migrated to various clouds based on different constraints such as computational resources and better economical offerings. Since cloud services are instantiated as VMs, an application can be seen as a cluster of VMs that integrate its functionality. We focus on the VM cluster migration by exploring a more sophisticated method with regards to VM network configurations. In particular, networks are hard to managed because their internal setup is changed after a migration, and this is related with the configuration parameters during the re-instantiation to the new cloud platform. To address such issue, we introduce a Software Defined Networking (SDN) service that breaks the problem of network configuration into tractable pieces and involves virtual bridges instead of references to static endpoints. The architecture is modular, it is based on the SDN OpenFlow protocol and allows VMs to be paired in cluster groups that communicate with each other independently of the cloud platform that are deployed. The experimental analysis demonstrates migrations of VM clusters and provides a detailed discussion of service performance for different cases.     

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.     

Performance evaluation of cloud service considering fault recovery

Cloud computing is a recent trend in IT, which has attracted lots of attention. In cloud computing, service reliability and service performance are two important issues. To improve cloud service reliability, fault tolerance techniques such as fault recovery may be used, which in turn has impact on cloud service performance. Such impact deserves detailed research. Although there exist some researches on cloud/grid service reliability and performance, very few of them addressed the issues of fault recovery and its impact on service performance. In this paper, we conduct detailed research on performance evaluation of cloud service considering fault recovery. We consider recovery on both processing nodes and communication links. The commonly adopted assumption of Poisson arrivals of users’ service requests is relaxed, and the interarrival times of service requests can take arbitrary probability distribution. The precedence constraints of subtasks are also considered. The probability distribution of service response time is derived, and a numerical example is presented. The proposed cloud performance evaluation models and methods could yield results which are realistic, and thus are of practical value for related decision-makings in cloud computing.     

云计算环境下的虚拟机快速克隆技术

虚拟机克隆技术是指在云计算环境下快速复制出多个虚拟机（VM）并将这些VM分发到多台物理主机上,克隆出来的VM共享相同的初始状态然后独立运行提供服务。虚拟机克隆使得云计算提供商能够快速有效地部署系统资源。给出了一种虚拟机快速克隆方法,利用写时拷贝技术来创建虚拟磁盘和内存状态的快照,然后用按需分配内存技术和多点传送技术来请求和传输这些状态信息。在C3云平台上的实验表明,此方法在不中断源虚拟机中运行服务的情况下,实现了云计算中的快速虚拟机克隆。     

Novel resource allocation algorithms to performance and energy efficiency in cloud computing

The rapid growth in demand for computational power has led to a shift to the cloud computing model established by large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy. Cloud providers must ensure that their service delivery is flexible to meet various consumer requirements. However, to support green computing, cloud providers also need to minimize the cloud infrastructure energy consumption while conducting the service delivery. In this paper, for cloud environments, a novel QoS-aware VMs consolidation approach is proposed that adopts a method based on resource utilization history of virtual machines. Proposed algorithms have been implemented and evaluated using CloudSim simulator. Simulation results show improvement in QoS metrics and energy consumption as well as demonstrate that there is a trade-off between energy consumption and quality of service in the cloud environment.     

Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers

Cloud computing aims to provide dynamic leasing of server capabilities as scalable virtualized services to end users. However, data centers hosting cloud applications consume vast amounts of electrical energy, thereby contributing to high operational costs and carbon footprints. Green cloud computing solutions that can not only minimize the operational costs but also reduce the environmental impact are necessary. This study focuses on the Infrastructure as a Service model, where custom virtual machines (VMs) are launched in appropriate servers available in a data center. A complete data center resource management scheme is presented in this paper. The scheme can not only ensure user quality of service (through service level agreements) but can also achieve maximum energy saving and green computing goals. Considering that the data center host is usually tens of thousands in size and that using an exact algorithm to solve the resource allocation problem is difficult, the modified shuffled frog leaping algorithm and improved extremal optimization are employed in this study to solve the dynamic allocation problem of VMs. Experimental results demonstrate that the proposed resource management scheme exhibits excellent performance in green cloud computing.     

面向应用服务级目标的虚拟化资源管理

在虚拟环境中实现应用服务级目标,是当前数据中心系统管理的关键问题之一.解决该问题有两个方面的要求:一方面,在虚拟化层次和范围内,能够动态和分布式地按需调整虚拟机资源分配;另一方面,在虚拟化范围之外,能够控制由于虚拟机对非虚拟化资源的竞争所导致的性能干扰,实现虚拟机性能隔离.然而,已有工作不适用于虚拟化数据中心场景.提出一种面向应用服务级目标的虚拟化资源管理方法.首先,该方法基于反馈控制理论,通过动态调整虚拟机资源分配来实现每个应用的服务器目标;同时,还设计了一个两层结构的自适应机制,使得应用模型能够动态地捕捉虚拟机资源分配与应用性能的时变非线性关系;最后,该方法通过仲裁不同应用的资源分配请求来控制虚拟机在非虚拟化资源上的竞争干扰.实验在基于Xen的机群环境中检验了该方法在RUBiS系统和TPC-W基准上的效果.实验结果显示,该方法的应用服务级目标实现率比两种对比方法平均高29.2%,而应用服务级目标平均偏离率比它们平均低50.1%.另一方面,当RUBiS系统和TPC-W基准竞争非虚拟化的磁盘I/O资源时,该方法通过抑制TPC-W基准28.7%的处理器资源需求来优先满足RUBiS系统的磁盘I/O需求.     

Energy-efficient sender-initiated threshold-based load balancing (e-STLB) in cloud computing environment

Cloud computing has transformed service delivery through its pay-per-use model, supporting diverse users with multiple heterogeneous Virtual Machines (VMs). However, energy consumption has emerged as a critical concern, necessitating cloud resource optimization for environment-friendly practices. This research paper presents an innovative energy-efficient threshold-based sender-initiated load-balancing strategy (e-STLB) to address this concern. The approach employs threshold values to trigger task migration between VMs, ensuring optimal performance while maximizing energy efficiency. The proposed strategy significantly reduces Makespan and increases Resource Utilization in an energy-conscious manner. Experimental evaluations using Cloudsim 3.0 demonstrate that the e-STLB outperforms other state-of-the-art solutions, offering a compelling approach to sustainable cloud computing.     

Cloud resource allocation schemes: review,taxonomy, and opportunities

Cloud computing has emerged as a popular computing model to process data and execute computationally intensive applications in a pay-as-you-go manner. Due to the ever-increasing demand for cloud-based applications, it is becoming difficult to efficiently allocate resources according to user requests while satisfying the service-level agreement between service providers and consumers. Furthermore, cloud resource heterogeneity, the unpredictable nature of workload, and the diversified objectives of cloud actors further complicate resource allocation in the cloud computing environment. Consequently, both the industry and academia have commenced substantial research efforts to efficiently handle the aforementioned multifaceted challenges with cloud resource allocation. The lack of a comprehensive review covering the resource allocation aspects of optimization objectives, design approaches, optimization methods, target resources, and instance types has motivated a review of existing cloud resource allocation schemes. In this paper, current state-of-the-art cloud resource allocation schemes are extensively reviewed to highlight their strengths and weaknesses. Moreover, a thematic taxonomy is presented based on resource allocation optimization objectives to classify the existing literature. The cloud resource allocation schemes are analyzed based on the thematic taxonomy to highlight the commonalities and deviations among them. Finally, several opportunities are suggested for the design of optimal resource allocation schemes.