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.     

基于数据中心的两阶段虚拟机能效优化部署算法

针对数据中心在虚拟机动态部署过程中的高能耗问题,提出了面向数据中心的两阶段虚拟机能效优化部署算法--DVMP_VMMA。第一阶段为初始部署,提出了动态虚拟机部署(DVMP)算法限定主机最优部署数量,降低了闲置能耗;同时,为了应对负载的动态变化,第二阶段提出迁移约束的虚拟机迁移算法(VMMA)对初始部署方案作进一步优化,这样不仅得到的系统能耗更低,而且还能保证应用服务质量。与满载算法(FL)、基于固定门限值的部署算法(FT),绝对中位差部署算法(MAD)、四分位差部署算法(QD)、迁移周期最优算法(MTM)、最小占用率迁移算法(MIU)进行的比较实验结果表明:DVMP_VMMA不仅考虑了系统能耗优化,使运行时资源利用率更高;而且还可以避免VM频繁迁移完成对性能的提升,其在优化数据中心能耗、SLA违例、VM迁移量的控制及性能损失等指标上均有较好效果,其综合性能优于对比算法。     

资源调度等待开销感知的虚拟机整合

近年来,数据中心庞大的能源开销问题引起广泛关注.虚拟化管理平台可以通过虚拟机迁移技术将虚拟机整合到更少的服务器上,从而提高数据中心能源有效性.对面向数据中心节能的虚拟机整合研究工作进行调研,并总结虚拟机整合研究存在的3个挑战.针对已有工作未考虑虚拟机等待资源调度带来的服务器资源额外开销这种现象,开展了资源调度等待开销感知的虚拟机整合研究.从理论和实验上证明了在具有实际意义的约束条件下,存在着虚拟机等待资源调度带来的服务器资源额外开销,且随着整合虚拟机数量的增长保持稳定.基于典型工作负载的实验结果表明,这个额外开销平均占据了11.7%的服务器资源开销.此外,提出了资源预留整合（MRC）算法,用于改进已有的虚拟机整合算法.算法模拟实验结果表明,MRC算法相比于常用的虚拟机整合算法FFD（first fit decreasing）,明显降低了服务器资源溢出概率.     

Energy-Aware Virtual Machine Consolidation Algorithm Based on Ant Colony System

Energy consumption has become a critical issue for data centers due to energy-associated costs and environmental effects. In this paper, we propose a new algorithm based on Ant Colony System to solve Virtual Machine Consolidation problem aims to save the energy consumption of cloud data centers. We consider the energy consumption during VMs migration as one of the primary factors which have not considered in the similar conventional algorithms. It significantly reduces the number of migrations and the active physical machines that result in the reduction of total energy consumption of data centers. The simulation results on the random workload in different scenarios demonstrate that the proposed algorithm outperforms the state-of-the-art VM Consolidation algorithm with regards to the number of migrations, number of sleeping PMs, number of SLA Violations, and energy consumption.     

基于动态调整阈值的虚拟机迁移算法

针对当前数据中心服务器能耗优化和虚拟机迁移时机合理性问题,提出一种基于动态调整阈值（DAT）的虚拟机迁移算法。该算法首先通过统计分析物理机历史负载数据动态地调整虚拟机迁移的阈值门限,然后通过延时触发和预测物理机的负载趋势确定虚拟机迁移时机。最后将该算法应用到实验室搭建的数据中心平台上进行实验验证,结果表明基于DAT的虚拟机迁移算法比静态阈值法关闭的物理机数量更多,云数据中心能耗更低。基于DAT的虚拟机迁移算法能根据物理机的负载变化动态迁移虚拟机,达到提高物理机资源利用率、降低数据中心能耗、提高虚拟机迁移效率的目的。     

虚拟云下满足多重约束的时限敏感任务调度算法

目前以虚拟云服务平台作为强大计算平台的虚拟云环境下,许多现存调度方法致力于合并虚拟机以减少物理机数目,从而达到减少能源消耗的目的,但会引入高额虚拟机迁移成本;此外,现存方法也没有考虑导致用户高额支付成本的成本因子影响。以减少云服务提供者能源消耗和云服务终端用户支付成本为目标,同时保障用户任务的时限要求,提出一种能源与时限可感知的非迁移调度（EDA-NMS）算法。EDA-NMS利用任务时限的松弛度,延迟宽松时限任务的执行从而无需唤醒新的物理机,更无需引入虚拟机动态迁移成本,以达到减少能源消耗的目的。多重扩展实验结果表明,EDA-NMS采用成本和能耗有效的虚拟机实例类型组合方案,与主动及响应式调度（PRS）算法相比,在减少静态能耗的同时,能更有效地满足用户关键任务的敏感时限并确保用户支付成本最低。     

基于虚拟机调度的数据中心节能优化

随着数据中心的快速发展,其能耗问题已经愈发突出,数据中心节能机制已成为研究热点;但大多节能机制并未充分考虑数据中心的异构性,如不同时间购置的服务器之间存在差异。为此引入代表服务器能耗效率的能效比(Performance/Power)作为参数,提出一种基于虚拟机调度的节能算法PVMAP,动态整合虚拟机时优先充分使用能效比高的服务器,从而尽量减少虚拟机迁移次数和同时运行的服务器数量。仿真实验结果表明,算法能够在节能的同时保证服务质量(QoS),比其他算法具有更好的稳定性和可扩展性。     

Heuristics based server consolidation with residual resource defragmentation in cloud data centers

Server Consolidation is one of the foremost concerns associated with the effective management of a Cloud Data Center as it has the potential to accomplish significant reduction in the overall cost and energy consumption. Most of the existing works on Server Consolidation have focused only on reducing the number of active physical servers (PMs) using Virtual Machine (VM) Live Migration. But, along with reducing the number of active PMs, if a consolidation approach reduces residual resource fragmentation, the residual resources can be efficiently used for new VM allocations, or VM reallocations, and some future migrations can also be reduced. None of the existing works have explicitly focused on reducing residual resource fragmentation along with reducing the number of active PMs to the best of our knowledge. We propose RFAware Server Consolidation, a heuristics based server consolidation approach which performs residual resource defragmentation along with reducing the number of active PMs in cloud data centers.     

云数据中心基于遗传算法的虚拟机迁移模型

提出云数据中心中基于遗传算法的虚拟机迁移模型GA-VMM（genetic algorithm based virtual machine migration）。GA-VMM在虚拟机迁移的时刻考虑的问题维度优于常见的策略,使虚拟机的分配与迁移更加合理与公平。建立了云端能量消耗与在线虚拟机迁移时间消耗数学模型,通过全局遗传算法来优化虚拟机迁移和放置策略。利用某个企业的大数据中心作为云端测试环境,对比测试GA-VMM迁移模型与已有的虚拟机迁移策略的性能。测试结果表明,GA-VMM迁移模型能够更好地减少物理主机的使用数量和虚拟机的迁移次数,SLA（service level agreement violation）违规基本处于稳定状态;GA-VMM可以降低数据中心能耗,性能优于已有的迁移策略。     

Decentralized cloud datacenter reconsolidation through emergent and topology-aware behavior

Consolidation of multiple applications on a single Physical Machine (PM) within a cloud data center can increase utilization, minimize energy consumption, and reduce operational costs. However, these benefits come at the cost of increasing the complexity of the scheduling problem.In this paper, we present a topology-aware resource management framework. As part of this framework, we introduce a Reconsolidating PlaceMent scheduler (RPM) that provides and maintains durable allocations with low maintenance costs for data centers with dynamic workloads. We focus on workloads featuring both short-lived batch jobs and latency-sensitive services such as interactive web applications. The scheduler assigns resources to Virtual Machines (VMs) and maintains packing efficiency while taking into account migration costs, topological constraints, and the risk of resource contention, as well as the variability of the background load and its complementarity to the new VM.We evaluate the model by simulating a data center with over 65,000 PMs, structured as a three-level multi-rooted tree topology. We investigate trade-offs between factors that affect the durability and operational cost of maintaining a near-optimal packing. The results show that the proposed scheduler can scale to the number of PMs in the simulation and maintain efficient utilization with low migration costs.     

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%.     

面向大规模云资源调度的可扩展分布式调度方法

云数据中心异构物理服务器的能耗优化资源分配问题是NP难的组合优化问题,当资源分配问题规模较大时,求解的空间比较大,很难在合理时间内求得最优解。基于分而治之的思想,从调度模式方面提出可扩展分布式调度方法,即当云数据中心待调度的物理服务器的数量比较大时,将待调度的服务器划分为若干个服务器集群,然后在每个服务器集群建立能耗优化的资源分配模型,并利用约束编程框架Choco求解模型,获得能耗最优的资源分配方式。将提出的基于可扩展分布式调度方法的能耗优化云资源调度算法与非扩展调度算法进行实验比较,实验结果表明,提出的基于可扩展分布式调度方法的能耗优化云资源调度算法在大规模云资源分配上有明显的性能优势。     

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%.     

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.     

Chaotic Sandpiper Optimization Based Virtual Machine Scheduling for Cyber-Physical Systems

Recently, with the growth of cyber physical systems (CPS), several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively. Besides, the cloud computing (CC) enabled CPS offers huge processing and storage resources for CPS that finds helpful for a range of application areas. At the same time, with the massive development of applications that exist in the CPS environment, the energy utilization of the cloud enabled CPS has gained significant interest. For improving the energy effectiveness of the CC platform, virtualization technologies have been employed for resource management and the applications are executed via virtual machines (VMs). Since effective scheduling of resources acts as an important role in the design of cloud enabled CPS, this paper focuses on the design of chaotic sandpiper optimization based VM scheduling (CSPO-VMS) technique for energy efficient CPS. The CSPO-VMS technique is utilized for searching for the optimum VM migration solution and it helps to choose an effective scheduling strategy. The CSPO algorithm integrates the concepts of traditional SPO algorithm with the chaos theory, which substitutes the main parameter and combines it with the chaos. In order to improve the process of determining the global optimum solutions and convergence rate of the SPO algorithm, the chaotic concept is included in the SPO algorithm. The CSPO-VMS technique also derives a fitness function to choose optimal scheduling strategy in the CPS environment. In order to demonstrate the enhanced performance of the CSPO-VMS technique, a wide range of simulations were carried out and the results are examined under varying aspects. The simulation results ensured the improved performance of the CSPO-VMS technique over the recent methods interms of different measures.     

基于监控器时间开销的虚拟机发现方法

针对传统方法只能发现单一类型虚拟机的缺陷,提出基于虚拟机监控器时间开销的虚拟机发现方法。特定指令能使监控器运行时产生显著的额外开销,该方法能利用监控器执行不同指令序列产生的相对时间开销对虚拟机进行判别。实验结果表明,该方法能够准确发现目前3类主流虚拟机。     

基于无证书环签名的虚拟机可信证明方案

由于虚拟环境的复杂性和动态性,使用传统方法证明其安全状态时会出现运算效率低下的情况;而环签名具有运算效率高、匿名性强的特点,利用无证书公钥系统可解决密钥管理问题。为此,提出一种采用无证书环签名机制的虚拟机可信证明方案。私钥生成中心（PKG）验证平台物理环境的状态可信后,由PKG和虚拟可信平台模块（vTPM）管理器利用无证书算法共同生成vTPM签名密钥,虚拟机对外证明时采用环签名机制,将证明者的信息隐藏在环成员列表中,从而实现虚拟机对外的匿名身份证明和状态证明。在完成证明准备工作后,虚拟机不需要在每次证明和迁移时重复生成虚拟身份证明密钥（vAIK）证书,因此大大提高了证明效率;另外方案具有很强的安全性和匿名性,适用于虚拟机数量巨大的云计算环境。     

可靠性感知下的虚拟数据中心映射算法

介绍现阶段虚拟数据中心(VDC)映射的研究进展,根据租户对VDC可靠性的需求,提出一种可靠性感知下的VDC映射启发式算法。对于每个VDC,该算法通过限制能放置在同一个服务器上的最大虚拟机数目来保证租户VDC可靠性需求,然后以降低数据中心网络带宽消耗和服务器能耗为主要目标进行VDC映射。其具体做法是:首先将相互之间带宽需求量大的虚拟机合并部署来降低数据中心网络带宽的消耗;然后把合并后的虚拟机优先部署到已开启的服务器上,从而减少开启的服务器数目,降低数据中心的服务器能耗。利用基于胖树结构的数据中心拓扑对提出的算法进行了仿真,结果表明,与2EM算法相比,该算法能够满足租户VDC的可靠性需求,能在不增加额外能耗的前提下最多减少数据中心网络约30%的带宽消耗。     

Energy-efficient Tasks Scheduling Heuristics with Multi-constraints in Virtualized Clouds

Reducing energy consumption has become an important task in cloud datacenters. Many existing scheduling approaches in cloud datacenters try to consolidate virtual machines (VMs) to the minimum number of physical hosts and hence minimize the energy consumption. VM live migration technique is used to dynamically consolidate VMs to as few PMs as possible; however, it introduces high migration overhead. Furthermore, the cost factor is usually not taken into account by existing approaches, which will lead to high payment cost for cloud users. In this paper, we aim to achieve energy reduction for cloud providers and payment saving for cloud users, and at the same time, without introducing VM migration overhead and without compromising deadline guarantees for user tasks. Motivated by the fact that some of the tasks have relatively loose deadlines, we can further reduce energy consumption by proactively postponing the tasks without waking up new physical machines (PMs). A heuristic task scheduling algorithm called Energy and Deadline Aware with Non-Migration Scheduling (EDA-NMS) algorithm is proposed, which exploits the looseness of task deadlines and tries to postpone the execution of the tasks that have loose deadlines in order to avoid waking up new PMs. When determining the VM instant types, EDA-NMS selects the instant types that are just sufficient to guarantee task deadline to reduce user payment cost. The results of extensive experiments show that our algorithm performs better than other existing algorithms on achieving energy efficiency without introducing VM migration overhead and without compromising deadline guarantees.