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.     

An energy-aware virtual machines consolidation method for cloud computing: Simulation and verification

Cloud systems have become an essential part of our daily lives owing to various Internet-based services. Consequently, their energy utilization has also become a necessary concern in cloud computing systems increasingly. Live migration, including several virtual machines (VMs) packed on in minimal physical machines (PMs) as virtual machines consolidation (VMC) technique, is an approach to optimize power consumption. In this article, we have proposed an energy-aware method for the VMC problem, which is called energy-aware virtual machines consolidation (EVMC), to optimize the energy consumption regarding the quality of service guarantee, which comprises: (1) the support vector machine classification method based on the utilization rate of all resource of PMs that is used for PM detection in terms of the amount' load; (2) the modified minimization of migration approach which is used for VM selection; (3) the modified particle swarm optimization which is implemented for VM placement. Also, the evaluation of the functional requirements of the method is presented by the formal method and the non-functional requirements by simulation. Finally, in contrast to the standard greedy algorithms such as modified best fit decreasing, the EVMC decreases the active PMs and migration of VMs, respectively, 30%, 50% on average. Also, it is more efficient for the energy 30% on average, resources and the balance degree 15% on average in the cloud.     

Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system

In this paper, we present a novel multi-objective ant colony system algorithm for virtual machine (VM) consolidation in cloud data centres. The proposed algorithm builds VM migration plans, which are then used to minimise over-provisioning of physical machines (PMs) by consolidating VMs on under-utilised PMs. It optimises two objectives that are ordered by their importance. The first and foremost objective in the proposed algorithm is to maximise the number of released PMs. Moreover, since VM migration is a resource-intensive operation, it also tries to minimise the number of VM migrations. The proposed algorithm is empirically evaluated in a series of experiments. The experimental results show that the proposed algorithm provides an efficient solution for VM consolidation in cloud data centres. Moreover, it outperforms two existing ant colony optimization-based VM consolidation algorithms in terms of number of released PMs and number of VM migrations.     

iMeter: An integrated VM power model based on performance profiling

The unprecedented burst in power consumption encountered by contemporary datacenters continually boosts the development of energy efficient techniques from both hardware and software perspectives to alleviate the energy problem. The most widely adopted power saving solutions in datacenters that deliver cloud computing services are power capping and VM consolidation. However, without the capability to track the VM power usage precisely, the combined effect of the above two techniques could cause severe performance degradation to the consolidated VMs, thus violating the user service level agreements. In this paper, we propose an integrated VM power model called iMeter, which overcomes the drawbacks of overpresumption and overapproximation in segregated power models used in previous studies. We leverage the kernel-based performance counters that provide accurate performance statistics as well as high portability across heterogeneous platforms to build the VM power model. Principal component analysis is applied to identify performance counters that show strong impact on the VM power consumption with mathematical confidence. We also present a brief interpretation of the first four selected principal components on their indications of VM power consumption. We demonstrate that our approach is independent of underlying hardware and virtualization configurations with clustering analysis. We utilize the support vector regression to build the VM power model predicting the power consumption of both a single VM and multiple consolidated VMs running various workloads. The experimental results show that our model is able to predict the instantaneous VM power usage with an average error of 5% and 4.7% respectively against the actual power measurement.     

Server consolidation techniques in virtualized data centers of cloud environments: A systematic literature review

At the virtualized data centers, services are presented by active virtual machines (VMs) in physical machines (PMs). The manner in which VMs are mapped to PMs affects the performance of data centers and the energy efficiency. By employing the server consolidation technique, it is possible to configure the VMs on a smaller number of PMs, while the quality of service is guaranteed. In this way, the rate of active PM utilization increases and fewer active PMs would be required. Moreover, the server consolidation technique reacts to the management of underloaded and overloaded PMs by using the VM migration technology. Considering the capabilities of the server consolidation technique and its role in developing the cloud computing infrastructure, many researches have been conducted in this context. Still, a comprehensive and systematic study has not yet been performed on various consolidation techniques to check the capabilities, advantages, and disadvantages of current approaches. In this paper, a systematic study is conducted on a number of credible researches related to server consolidation techniques. In order to do so and by studying the selected works, proposed solutions are categorized based on the type of decision for running the consolidation algorithm in 4 groups of static method, dynamic method, prediction‐based dynamic method, and hybrid method. Thereafter, the advantages and disadvantages of suggested approaches are studied and compared in each research by specifying the technique and idea applied therein. In addition, by categorizing aims of researches and specifying assessment parameters, optimization approaches and type of architecture, a possibility has been provided to get familiarized with the views of the researchers.     

Virtual machine power measuring technique with bounded error in cloud environments

In virtualized datacenters, accurately measuring the power consumption of virtual machines (VMs) is the prerequisite to achieve the goal of fine-grained power management. However, existing VM power models can only provide power measurements with empirical accuracy and unbounded error. In this paper, we firstly formulize the co-relation between utilization and accuracy of power model, and compare two classes of VM power models; then we propose a novel VM power model which is based on a conception named relative performance monitoring counter (PMC); finally, based on the relative PMC power model, we propose a novel VM scheduling algorithm which uses the information of relative PMC to compensate the recursive power consumption. Theoretical analysis indicates that the proposed algorithm can provide bounded error when measuring per-VM power consumption. Extensive experiments are conducted by using various benchmarks on different platforms, and the results show that the error of per-VM power measurement can be significantly reduced. In addition, the proposed algorithm is effective to improve the power efficiency of a server when its virtualization ratio is high.     

Batch scheduling of consolidated virtual machines based on their workload interference model

The use of virtualization technology (VT) has become widespread in modern datacenters and Clouds in recent years. In spite of their many advantages, such as provisioning of isolated execution environments and migration, current implementations of VT do not provide effective performance isolation between virtual machines (VMs) running on a physical machine (PM) due to workload interference of VMs. Generally, this interference is due to contention on physical resources that impacts performance in different workload configurations. To investigate the impacts of this interference, we formalize the concept of interference for a consolidated multi-tenant virtual environment. This formulation, represented as a mathematical model, can be used by schedulers to estimate the interference of a consolidated virtual environment in terms of the processing and networking workloads of running VMs, and the number of consolidated VMs. Based on the proposed model, we present a novel batch scheduler that reduces the interference of running tenant VMs by pausing VMs that have a higher impact on proliferation of the interference. The scheduler achieves this by selecting a set of VMs that produce the least interference using a 0–1 knapsack problem solver. The selected VMs are allowed to run and other VMs are paused. Users are not troubled by the pausing and resumption of VMs for a short time because the scheduler has been designed for the execution of batch type applications such as scientific applications. Evaluation results on the makespan of VMs executed under the control of our scheduler have shown nearly 33% improvement in the best case and 7% improvement in the worst case compared to the case in which all VMs are running concurrently. In addition, the results show that our scheduling algorithm outperforms serial and random scheduling of VMs as well.     

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.     

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。     

Towards a green cluster through dynamic remapping of virtual machines

Since power is one of the major limiting factors for a data center or for large cluster growth, the objective of this study is to minimize the power consumption of the cluster without violating the performance constraints of the applications. We propose a runtime virtual machine (VM) mapping framework in a cluster or data center to save energy. The new framework can make reconfiguration decisions on time with the consideration of a low influence on the performance. In the GreenMap framework, one probabilistic, heuristic algorithm is designed for the optimization problem: mapping VMs onto a set of physical machines (PMs) under the constraint of multi-dimensional resource consumptions. Experimental measurements show that the new method can reduce the power consumption by up to 69.2% over base, with few performance penalties. The effectiveness and performance insights are also analytically verified.     

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%.     

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.     

Power-efficient assignment of virtual machines to physical machines

Motivated by current trends in cloud computing, we study a version of the generalized assignment problem where a set of virtual processors has to be implemented by a set of identical processors. For literature consistency, we say that a set of virtual machines (VMs) is assigned to a set of physical machines (PMs). The optimization criterion is to minimize the power consumed by all the PMs. We term the problem Virtual Machine Assignment (VMA). Crucial differences with previous work include a variable number of PMs, that each VM must be assigned to exactly one PM (i.e., VMs cannot be implemented fractionally), and a minimum power consumption for each active PM. Such infrastructure may be strictly constrained in the number of PMs or in the PMs’ capacity, depending on how costly (in terms of power consumption) it is to add a new PM to the system or to heavily load some of the existing PMs. Low usage or ample budget yields models where PM capacity and/or the number of PMs may be assumed unbounded for all practical purposes. We study four VMA problems depending on whether the capacity or the number of PMs is bounded or not. Specifically, we study hardness and online competitiveness for a variety of cases. To the best of our knowledge, this is the first comprehensive study of the VMA problem for this cost function.     

A cost-effective power-aware approach for scheduling cloudlets in cloud computing environments

On a cloud platform, the user requests are managed through workload units called cloudlets which are assigned to virtual machines through cloudlet scheduling mechanism that mainly aims at minimizing the request processing time by producing effective small length schedules. The efficient request processing, however, requires excessive utilization of high-performance resources which incurs large overhead in terms of monetary cost and energy consumed by physical machines, thereby rendering cloud platforms inadequate for cost-effective green computing environments. This paper proposes a power-aware cloudlet scheduling (PACS) algorithm for mapping cloudlets to virtual machines. The algorithm aims at reducing the request processing time through small length schedules while minimizing energy consumption and the cost incurred. For allocation of virtual machines to cloudlets, the algorithm iteratively arranges virtual machines (VMs) in groups using weights computed through optimization and rescaling of parameters including VM resources, cost of utilization of resources, and power consumption. The experiments performed with a diverse set of configurations of cloudlets and virtual machines show that the PACS algorithm achieves a significant overall performance improvement factor ranging from 3.80 to 23.82 over other well-known cloudlet scheduling algorithms..

     

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。     

Many-Objective Virtual Machine Placement

The process of selecting which virtual machines (VMs) should be executed at each physical machine (PM) of a virtualized infrastructure is commonly known as Virtual Machine Placement (VMP). This work presents a general many-objective optimization framework that is able to consider as many objective functions as needed when solving a VMP problem in a pure multi-objective context. As an example of utilization of the proposed framework, a formulation of a many-objective VMP problem (MaVMP) is proposed, considering the simultaneous optimization of the following five objective functions: (1) power consumption, (2) network traffic, (3) economical revenue, (4) quality of service and (5) network load balancing. To solve the formulated MaVMP problem, an interactive memetic algorithm is proposed. Experimental results prove the correctness of the proposed algorithm, its effectiveness converging to a manageable number of solutions and its capabilities to solve problem instances with large numbers of PMs and VMs.     

Virtual machine selection and placement for dynamic consolidation in Cloud computing environment

Dynamic consolidation of virtual machines (VMs) in a data center is an effective way to reduce the energy consumption and improve physical resource utilization. Determining which VMs should be migrated from an overloaded host directly influences the VM migration time and increases energy consumption for the whole data center, and can cause the service level of agreement (SLA), delivered by providers and users, to be violated. So when designing a VM selection policy, we not only consider CPU utilization, but also define a variable that represents the degree of resource satisfaction to select the VMs. In addition, we propose a novel VM placement policy that prefers placing a migratable VM on a host that has the minimum correlation coefficient. The bigger correlation coefficient a host has, the greater the influence will be on VMs located on that host after the migration. Using CloudSim, we run simulations whose results let draw us to conclude that the policies we propose in this paper perform better than existing policies in terms of energy consumption, VM migration time, and SLA violation percentage.     

SAVE: self-adaptive consolidation of virtual machines for energy efficiency of CPU-intensive applications in the cloud

The Journal of Supercomputing - In virtualized data centers, consolidation of virtual machines (VMs) on minimizing the number of total physical machines (PMs) has been recognized as a very...     

A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters

Information and communication technology (ICT) has a profound impact on environment because of its large amount of CO₂ emissions. In the past years, the research field of “green” and low power consumption networking infrastructures is of great importance for both service/network providers and equipment manufacturers. An emerging technology called Cloud computing can increase the utilization and efficiency of hardware equipment. The job scheduler is needed by a cloud datacenter to arrange resources for executing jobs. In this paper, we propose a scheduling algorithm for the cloud datacenter with a dynamic voltage frequency scaling technique. Our scheduling algorithm can efficiently increase resource utilization; hence, it can decrease the energy consumption for executing jobs. Experimental results show that our scheme can reduce more energy consumption than other schemes do. The performance of executing jobs is not sacrificed in our scheme. We provide a green energy-efficient scheduling algorithm using the DVFS technique for Cloud computing datacenters.     

Bayes Theorem Based Virtual Machine Scheduling for Optimal Energy Consumption

This paper proposes an algorithm for scheduling Virtual Machines (VM) with energy saving strategies in the physical servers of cloud data centers. Energy saving strategy along with a solution for productive resource utilization for VM deployment in cloud data centers is modeled by a combination of “Virtual Machine Scheduling using Bayes Theorem” algorithm (VMSBT) and Virtual Machine Migration (VMMIG) algorithm. It is shown that the overall data center’s consumption of energy is minimized with a combination of VMSBT algorithm and Virtual Machine Migration (VMMIG) algorithm. Virtual machine migration between the active physical servers in the data center is carried out at periodical intervals as and when a physical server is identified to be under-utilized. In VM scheduling, the optimal data centers are clustered using Bayes Theorem and VMs are scheduled to appropriate data center using the selection policy that identifies the cluster with lesser energy consumption. Clustering using Bayes rule minimizes the number of server choices for the selection policy. Application of Bayes theorem in clustering has enabled the proposed VMSBT algorithm to schedule the virtual machines on to the physical server with minimal execution time. The proposed algorithm is compared with other energy aware VM allocations algorithms viz. “Ant-Colony” optimization-based (ACO) allocation scheme and “min-min” scheduling algorithm. The experimental simulation results prove that the proposed combination of ‘VMSBT’ and ‘VMMIG’ algorithm outperforms other two strategies and is highly effective in scheduling VMs with reduced energy consumption by utilizing the existing resources productively and by minimizing the number of active servers at any given point of time.