DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model

Live Virtual Machine (VM) consolidation is an effective method of improving energy-efficiency level in green data centers. Currently, to evaluate energy consumption in green data centers, energy-efficiency evaluation model with CPU utilization rate has been proposed. However, it is not suitable for data-intensive computing due to great energy consumption by GPU-intensive processing. In this paper, we have proposed a new energy evaluation model with CPU and GPU utilization rates. There are two kinds of policies in live VM consolidation: one for VM selection and the other for VM allocation. Some researchers have proposed their solutions based on VM selection policy or VM allocation policy respectively. However, it will be a better energy-efficiency VM consolidation policy if these two polices are integrated together. Based on these two policies, a fast Artificial Bee Colony (ABC) based energy-efficiency live VM consolidation policy with data-intensive energy model, named as DataABC, is proposed. DataABC adopts the idea of Artificial Bee Colony algorithm to get a fast and global optimized decision of VM consolidation. Compared with two state-of-art policies of PS-ABC and PS-ES, the total energy consumption of DataABC evidently drop by 9.72% and 5.84% respectively. As a result, based on the ESV metric, the DataABC approach has proved that (a) the energy-efficiency evaluation model with data-intensive computing is valid and that (b) DataABC can save energy with a good Quality of Service (QoS) in green data centers.     

OpenStack Neat: a framework for dynamic and energy‐efficient consolidation of virtual machines in OpenStack clouds

Dynamic consolidation of virtual machines (VMs) is an efficient approach for improving the utilization of physical resources and reducing energy consumption in cloud data centers. Despite the large volume of research published on this topic, there are very few open‐source software systems implementing dynamic VM consolidation. In this paper, we propose an architecture and open‐source implementation of OpenStack Neat, a framework for dynamic VM consolidation in OpenStack clouds. OpenStack Neat can be configured to use custom VM consolidation algorithms and transparently integrates with existing OpenStack deployments without the necessity of modifying their configuration. In addition, to foster and encourage further research efforts in the area of dynamic VM consolidation, we propose a benchmark suite for evaluating and comparing dynamic VM consolidation algorithms. The proposed benchmark suite comprises OpenStack Neat as the base software framework, a set of real‐world workload traces, performance metrics and evaluation methodology. As an application of the proposed benchmark suite, we conduct an experimental evaluation of OpenStack Neat and several dynamic VM consolidation algorithms on a five‐node testbed, which shows significant benefits of dynamic VM consolidation resulting in up to 33% energy savings. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

Energy-Efficient Thermal-Aware Autonomic Management of Virtualized HPC Cloud Infrastructure

Virtualized datacenters and clouds are being increasingly considered for traditional High-Performance Computing (HPC) workloads that have typically targeted Grids and conventional HPC platforms. However, maximizing energy efficiency and utilization of datacenter resources, and minimizing undesired thermal behavior while ensuring application performance and other Quality of Service (QoS) guarantees for HPC applications requires careful consideration of important and extremely challenging tradeoffs. Virtual Machine (VM) migration is one of the most common techniques used to alleviate thermal anomalies (i.e., hotspots) in cloud datacenter servers as it reduces load and, hence, the server utilization. In this article, the benefits of using other techniques such as voltage scaling and pinning (traditionally used for reducing energy consumption) for thermal management over VM migrations are studied in detail. As no single technique is the most efficient to meet temperature/performance optimization goals in all situations, an autonomic approach that performs energy-efficient thermal management while ensuring the QoS delivered to the users is proposed. To address the problem of VM allocation that arises during VM migrations, an innovative application-centric energy-aware strategy for Virtual Machine (VM) allocation is proposed. The proposed strategy ensures high resource utilization and energy efficiency through VM consolidation while satisfying application QoS by exploiting knowledge obtained through application profiling along multiple dimensions (CPU, memory, and network bandwidth utilization). To support our arguments, we present the results obtained from an experimental evaluation on real hardware using HPC workloads under different scenarios.     

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.     

Resource allocation algorithms for virtualized service hosting platforms

Commodity clusters are used routinely for deploying service hosting platforms. Due to hardware and operation costs, clusters need to be shared among multiple services. Crucial for enabling such shared hosting platforms is virtual machine (VM) technology, which allows consolidation of hardware resources. A key challenge, however, is to make appropriate decisions when allocating hardware resources to service instances. In this work we propose a formulation of the resource allocation problem in shared hosting platforms for static workloads with servers that provide multiple types of resources. Our formulation supports a mix of best-effort and QoS scenarios, and, via a precisely defined objective function, promotes performance, fairness, and cluster utilization. Further, this formulation makes it possible to compute a bound on the optimal resource allocation. We propose several classes of resource allocation algorithms, which we evaluate in simulation. We are able to identify an algorithm that achieves average performance close to the optimal across many experimental scenarios. Furthermore, this algorithm runs in only a few seconds for large platforms and thus is usable in practice.     

An intelligent water drops-based workflow scheduling for IaaS cloud

Cloud computing is an emerging technology in a distributed environment with a collection of large-scale heterogeneous systems. One of the challenging issues in the cloud data center is to select the minimum number of virtual machine (VM) instances to execute the tasks of a workflow within a time limit. The objectives of such a strategy are to minimize the total execution time of a workflow and improve resource utilization. However, the existing algorithms do not guarantee to achieve high resource utilization although they have abilities to achieve high execution efficiency. The higher resource utilization depends on the reusability of VM instances. In this work, we propose a new intelligent water drops based workflow scheduling algorithm for Infrastructure-as-a-Service (IaaS) cloud. The objectives of the proposed algorithm are to achieve higher resource utilization and minimize the makespan within the given deadline and budget constraints. The first contribution of the algorithm is to find multiple partial critical paths (PCPs) of a workflow which helps in finding suitable VM instances. The second contribution is a scheduling strategy for PCP-VM assignment for assigning the VM instances. The proposed algorithm is evaluated through various simulation runs using synthetic datasets and various performance metrics. Through comparison, we show the superior performance of the proposed algorithm over the existing ones.     

云环境中多目标优化的虚拟机放置算法

虚拟机放置（VMP）是虚拟机整合的核心,是一个多资源约束的多目标优化问题。高效的VMP算法不仅能显著地降低云数据中心能耗、提高资源利用率,还能保证服务质量（QoS）。针对数据中心能耗高和资源利用率低的问题,提出了基于离散蝙蝠算法的虚拟机放置（DBA-VMP）算法。首先,把最小化能耗和最大化资源利用率作为优化目标,建立多目标约束的VMP优化模型;然后,通过效仿人工蚁群在觅食过程中共享信息素的机制,将信息素反馈机制引入蝙蝠算法,并对经典蝙蝠算法进行离散化改进;最后,用改进的离散蝙蝠算法求解模型的Pareto最优解。实验结果表明,与其他多目标优化的VMP算法相比,所提算法在使用不同数据集的情况下都能有效降低能耗,提高资源利用率,实现了在保证QoS的前提下的降低能耗和提高资源利用率两者之间的优化平衡。     

面向数据中心的虚拟机部署及优化算法

虚拟机动态配置是解决数据中心能耗低效的有效方法。针对动态配置过程中的虚拟机部署及优化问题展开研究,提出一种新的面向系统能耗的虚拟机部署算法以及基于主动迁移的优化策略。为了降低系统能耗,新算法采用基于服务器利用率的最佳适配降序算法求解虚拟机部署方案;同时为了适应应用负载的动态变化,新算法启动主动迁移策略对部署方案进行优化,即通过启发式算法在当前部署的基础上搜索使系统能耗更低的优化方案,并根据新部署对虚拟机执行主动迁移。考虑到迁移会导致应用服务质量降级和额外能耗,新算法通过在优化策略中设置基于服务器利用率的启动门限,对虚拟机主动迁移频率进行控制。仿真实验表明,所提算法在系统能耗、虚拟机迁移频率、服务器状态切换频率以及服务质量等多项性能指标上均有显著提高     

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.     

Developing resource consolidation frameworks for moldable virtual machines in clouds

This paper considers the scenario where multiple clusters of Virtual Machines (i.e., termed Virtual Clusters) are hosted in a Cloud system consisting of a cluster of physical nodes. Multiple Virtual Clusters (VCs) cohabit in the physical cluster, with each VC offering a particular type of service for the incoming requests. In this context, VM consolidation, which strives to use a minimal number of nodes to accommodate all VMs in the system, plays an important role in saving resource consumption. Most existing consolidation methods proposed in the literature regard VMs as “rigid” during consolidation, i.e., VMs’ resource capacities remain unchanged. In VC environments, QoS is usually delivered by a VC as a single entity. Therefore, there is no reason why VMs’ resource capacity cannot be adjusted as long as the whole VC is still able to maintain the desired QoS. Treating VMs as “moldable” during consolidation may be able to further consolidate VMs into an even fewer number of nodes. This paper investigates this issue and develops a Genetic Algorithm (GA) to consolidate moldable VMs. The GA is able to evolve an optimized system state, which represents the VM-to-node mapping and the resource capacity allocated to each VM. After the new system state is calculated by the GA, the Cloud will transit from the current system state to the new one. The transition time represents overhead and should be minimized. In this paper, a cost model is formalized to capture the transition overhead, and a reconfiguration algorithm is developed to transit the Cloud to the optimized system state with low transition overhead. Experiments have been conducted to evaluate the performance of the GA and the reconfiguration algorithm.     

基于自适应过载阈值选择的虚拟机动态整合方法

针对云环境下动态工作负载的不确定性,提出了基于自适应过载阈值选择的虚拟机动态整合方法。为了权衡数据中心能源有效性与服务质量间的关系,将自适应过载阈值的选择问题建模为马尔可夫决策过程,计算过载阈值的最优选择策略,并根据系统能效和服务质量调整阈值。通过过载阈值检测过载物理主机,然后根据最小迁移时间原则以及最小能耗增加放置原则确定虚拟机的迁移策略,最后切换轻负载物理主机至休眠状态完成虚拟机整合。仿真实验结果表明,所提出的方法在减少虚拟机迁移次数方面效果显著,在节约数据中心能源开销与保证服务质量方面表现良好,在能源的有效性与云服务质量二者之间取得了比较理想的平衡。     

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%.     

Efficient consolidation-aware VCPU scheduling on multicore virtualization platform

Multicore processors are widely used in today’s computer systems. Multicore virtualization technology provides an elastic solution to more efficiently utilize the multicore system. However, the Lock Holder Preemption (LHP) problem in the virtualized multicore systems causes significant CPU cycles wastes, which hurt virtual machine (VM) performance and reduces response latency. The system consolidates more VMs, the LHP problem becomes worse. In this paper, we propose an efficient consolidation-aware vCPU (CVS) scheduling scheme on multicore virtualization platform. Based on vCPU over-commitment rate, the CVS scheduling scheme adaptively selects one algorithm among three vCPU scheduling algorithms: co-scheduling, yield-to-head, and yield-to-tail based on the vCPU over-commitment rate because the actions of vCPU scheduling are split into many single steps such as scheduling vCPUs simultaneously or inserting one vCPU into the run-queue from the head or tail. The CVS scheme can effectively improve VM performance in the low, middle, and high VM consolidation scenarios. Using real-life parallel benchmarks, our experimental results show that the proposed CVS scheme improves the overall system performance while the optimization overhead remains low.     

Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center

Dynamic consolidation of virtual machines (VMs) in a cloud data center can be used to minimize power consumption. Beloglazov et al. have proposed the MM (Minimization of Migrations) heuristic for selecting the VMs to migrate from under- or over-utilized hosts, as well as the MBFD (Modified Best Fit Decreasing) heuristic for deciding the placement of the migrated VMs. According to their simulation results, these heuristics work very well in practice. In this paper, we investigate what performance guarantees can be rigorously proven for the heuristics. In particular, we establish that MM is optimal with respect to the number of selected VMs of an over-utilized host and it is a 1.5-approximation with respect to the decrease in utilization. On the other hand, we show that the result of MBFD can be arbitrarily far from the optimum. Moreover, we show that even if both MM and MBFD deliver optimal results, their combination does not necessarily result in optimal VM consolidation, but approximation results can be proven under suitable technical conditions. To the best of our knowledge, these are the first rigorously proven results on the effectiveness of also practically useful heuristic algorithms for the VM consolidation problem.     

Distributed virtual machine consolidation: A systematic mapping study

Background:Virtual Machine (VM) consolidation is an effective technique to improve resource utilization and reduce energy footprint in cloud data centers. It can be implemented in a centralized or a distributed fashion. Distributed VM consolidation approaches are currently gaining popularity because they are often more scalable than their centralized counterparts and they avoid a single point of failure.Objective:To present a comprehensive, unbiased overview of the state-of-the-art on distributed VM consolidation approaches.Method:A Systematic Mapping Study (SMS) of the existing distributed VM consolidation approaches.Results:19 papers on distributed VM consolidation categorized in a variety of ways. The results show that the existing distributed VM consolidation approaches use four types of algorithms, optimize a number of different objectives, and are often evaluated with experiments involving simulations.Conclusion:There is currently an increasing amount of interest on developing and evaluating novel distributed VM consolidation approaches. A number of research gaps exist where the focus of future research may be directed.     

Power efficient server consolidation for Cloud data center

Cloud computing has become an essential part of the global digital economy due to its extensibility, flexibility and reduced costs of operations. Nowadays, data centers (DCs) contain thousands of different machines running a huge number of diverse applications over an extended period. Resource management in Cloud is an open issue since an efficient resource allocation can reduce the infrastructure running cost. In this paper, we propose a snapshot-based solution for server consolidation problem from Cloud infrastructure provider (CIP) perspective. Our proposed mathematical formulation aims at reducing power cost by employing efficient server consolidation, and also considering the issues such as (i) mapping incoming and failing virtual machines (VMs), (ii) reducing a total number of VM migrations and (iii) consolidating running server workloads. We also compare the performance of our proposed model to the well-known Best Fit heuristics and its extension to include server consolidation via VM migration denoted as Best Fit with Consolidation (BFC). Our proposed mathematical formulation allows us to measure the solution quality in absolute terms, and it can also be applicable in practice. In our simulations, we show that relevant improvements (from 6% to 15%) over the widely adopted Best Fit algorithm achieved in a reasonable computing time.     

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.     

Simulation of power consumption of cloud data centers

Energy efficiency of cloud data centers received significant attention recently as data centers often consume significant resources in operation. Most of the existing energy-saving algorithms focus on resource consolidation for energy efficiency. This paper proposes a simulation-driven methodology with the accurate energy model to verify its performance, and introduces a new resource scheduling algorithm Best-Fit-Decreasing-Power (BFDP) to improve the energy efficiency without degrading the QoS of the system. Both the model and the resource algorithm have been extensively simulated and validated, and results showed that they are effective. In fact, the proposed model and algorithm outperforms the existing resource scheduling algorithms especially under light workloads.     

基于线性函数的QoS路由预计算算法

多度量的服务质量路由(QoSR)作为下一代互联网的一个重要难题，具有NPC的复杂度．作者设计了启发式算法(LFP)使用线性函数将两个度量转化成单一函数值，进而通过多个不同线性函数实现了与服务质量请求无关的QoSR预计算方式．文章分析了线性函数对算法性能的影响，给出了服务质量约束的可行区域和不可行区域的线性函数判定方法．实验结果表明，算法使用少量均匀分布的线性函数，即可产生具有较高路由性能的QoSR路由表，在可扩展性和路由性能等方面均明显优于现有算法。