Performance-aware server architecture recommendation and automatic performance verification technology on IaaS cloud

In this paper, we propose a server architecture recommendation and automatic performance verification technology, which recommends and verifies appropriate server architecture on Infrastructure as a Service (IaaS) cloud with bare metal servers, container-based virtual servers and virtual machines. Recently, cloud services are spread, and providers provide not only virtual machines but also bare metal servers and container-based virtual servers. However, users need to design appropriate server architecture for their requirements based on three types of server performances, and users need much technical knowledge to optimize their system performance. Therefore, we study a technology that satisfies users’ performance requirements on these three types of IaaS cloud. Firstly, we measure performance and start-up time of a bare metal server, Docker containers, KVM (Kernel-based Virtual Machine) virtual machines on OpenStack with changing number of virtual servers. Secondly, we propose a server architecture recommendation technology based on the measured quantitative data. A server architecture recommendation technology receives an abstract template of OpenStack Heat and function/performance requirements and then creates a concrete template with server specification information. Thirdly, we propose an automatic performance verification technology that executes necessary performance tests automatically on provisioned user environments according to the template. We implement proposed technologies, confirm performance and show the effectiveness.     

A Cost Model for IaaS Clouds Based on Virtual Machine Energy Consumption

Cloud Computing has revolutionized the software, platform and infrastructure provisioning. Infrastructure-as-a-Service (IaaS) providers offer on-demand and configurable Virtual Machine (VMs) to tenants of cloud computing services. A key consolidation force that widespread IaaS deployment is the use of pay-as-you-go and pay-as-you-use cost models. In these models, a service price can be composed of two dimensions: the individual consumption, and a proportional value charged for service maintenance. A common practice for public providers is to dilute both capital and operational costs on predefined pricing sheets. In this context, we propose PSVE (Proportional-Shared Virtual Energy), a cost model for IaaS providers based on CPU energy consumption. Aligned with traditional commodity prices, PSVE is composed of two key elements: an individualized cost accounted from CPU usage of VMs (e.g., processing and networking), and a shared cost from common hypervisor management operations, proportionally distributed among VMs.     

Determining Server Locations in Server Migration Service to Minimize Monetary Penalty of Dynamic Server Migration

In this paper, we propose a new class of service called server migration service (SMS) to augment the existing IaaS (Infrastructure as a Service). SMS allows servers (server-side processes of a network application) to dynamically and automatically migrate as their clients (client-side processes of a network application) change their locations in order to reduce the total monetary penalty that the SMS provider pays to its SMS subscribers when failing to provide them with the guaranteed level of QoS. In this paper, we consider the monetary impact that arises from QoS degradation due to server migration and build an integer programming model to determine when and to which location servers should migrate to minimize the total monetary penalty incurred by the SMS provider. Numerical examples show that SMS achieves up to 96% lower total monetary penalty compared to that without server migration. Numerical examples also show that the integer programming model developed in this paper requires reasonable computation time under realistic parameter settings.     

Multiperiod robust optimization for proactive resource provisioning in virtualized data centers

Energy management has become a significant concern in data centers for reducing operational costs. Using virtualization allows server consolidation, which increases server utilization and reduces energy consumption by turning off idle servers. This needs to consider the power state change overhead. In this paper, we investigate proactive resource provisioning in short-term planning for performance and energy management. To implement short-term planning based on workload prediction, this requires dealing with high fluctuations that are inaccurately predictable by using single value prediction. Unlike long-term planning, short-term planning can not depend on periodical patterns. Thus, we propose an adaptive range-based prediction algorithm instead of a single value. We implement and extensively evaluate the proposed range-based prediction algorithm with different days of real workload. Then, we exploit the range prediction for implementing proactive provisioning using robust optimization taking into consideration uncertainty of the demand. We formulate proactive VM provisioning as a multiperiod robust optimization problem. To evaluate the proposed approach, we use several experimental setups and different days of real workload. We use two metrics: energy savings and robustness for ranking the efficiency of different scenarios. Our approach mitigates undesirable changes in the power state of servers. This enhances servers’ availability for accommodating new VMs, its robustness against uncertainty in workload change, and its reliability against a system failure due to frequent power state changes.     

Flexible service consolidation with nested virtualization and library operating systems

In Infrastructure-as-a-service (IaaS) clouds, users can reduce costs by scale-in or scale-down when running services are underutilized. Since these optimizations of instance deployment require at least one minimum instance even for running an underutilized service, cost reduction is limited. For further optimization, multiple services can be consolidated into one instance. However, services have to be stopped temporarily at the consolidation time, and isolation between services becomes weaker after the consolidation. To solve these problems, this paper proposes FlexCapsule, which enables seamless and secure service consolidation in existing IaaS clouds. FlexCapsule runs each service in a lightweight virtual machine (VM) called an app VM, using a library operating system. An app VM runs inside an instance using a technique called nested virtualization. FlexCapsule can optimize instance deployment with negligible downtime by flexibly migrating app VMs. Due to strong isolation provided by app VMs, it can guarantee security between consolidated services. In addition, FlexCapsule provides multiprocess support using app VMs by emulating process fork and process pools. We have implemented FlexCapsule in Xen using both fully virtualized OS^v and paravirtualized MiniOS. Then, we examined the effectiveness of FlexCapsule using several applications. Due to the premature implementation of nested virtualization in Xen, the performance of app VMs largely degraded, but we believe that the performance could be improved using several existing optimizations.     

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.     

Effects of Reducing VMs Management Times on Elastic Applications

Cloud infrastructures provide computing resources to applications in the form of Virtual Machines (VMs). Many applications deployed in cloud resources have an elastic behavior, that is, they change the number of servers (VMs) dynamically, adapting the application to the workload. Scaling-out and scaling-in operations are managed by an auto-scaler module, which can be reactive (adapting the number of VMs to the current workload) or proactive (adapting to the expected future workload). The cloud infrastructure provides a management interface to create (deploy) and destroy (shutdown) server instances, operations that require some time to complete. In this work we evaluate to what extent the reduction of the time required by VM management operations, namely deployment and shutdown, impacts the performance of applications and the behavior of reactive and proactive auto-scaling policies. After establishing several ideal boundaries on the use of resources, we carry out a set of experiments that show how short management times drastically reduce the use of resources, while allowing the application to operate within the required performance bounds.     

企业虚拟化服务器架设的研究

服务器虚拟化架设将服务器物理资源抽象成逻辑资源,让一台服务器变成几台相互隔离的虚拟服务器,再在虚拟服务器部署各应用以共享一台服务器的资源,这样我们将不再受限于物理上的界限,而是让CPU、内存、磁盘等硬件变成可以动态管理的“资源池”,从而提高资源的利用率,简化系统管理,实现服务器整合,以达到资源充分利用的最终目的。     

共享式Web应用服务器集群的资源整合方法研究

PaaS(platform as a service)平台是一种重要的云计算服务类型,但传统的Web应用服务器集群管理方式无法为PaaS平台多承租和动态资源管理提供有效支持。针对上述问题,提出了一种基于进程隔离的共享式集群拓扑结构,支持多个Web应用服务器集群共享相同的物理服务器环境。同时给出了针对此共享集群的服务器整合方法,以满足租户应用的性能需求和提高服务器的有效资源使用率。实验证明,平台能够根据当前共享集群的状态选择适当数量的服务器,服务器整合方法有效可行。     

Application-Layer Protocol for Collaborative Multimedia Presentations

Many multimedia presentation applications involve retrieval of objects from more than one collaborating server. Presentations of objects from different collaborating servers might be interdependent. For instance, we can consider distributed video servers where blocks of movies are distributed over a set of servers. Here, blocks of a movie from different video servers have to be retrieved and presented continuously without any gaps in the presentation. Such applications first need an estimate of the available network resources to each of the collaborating server in order to identify a schedule for retrieving the objects composing the presentation. A collaborating server can suggest modifications of the retrieval schedule depending on its load. These modifications can potentially affect the retrieval schedule for other collaborating applications. Hence, a sequence of negotiations have to be carried out with the collaborating servers in order to commit for a retrieval schedule of the objects composing the multimedia presentation. In this paper, we propose an application sub-layer protocol, Resource Lock Commit Protocol (RLCP), for handling the negotiation and commitment of the resources required for a collaborative multimedia presentation application.     

Power-efficient distributed scheduling of virtual machines using workload-aware consolidation techniques

There is growing demand on datacenters to serve more clients with reasonable response times, demanding more hardware resources, and higher energy consumption. Energy-aware datacenters have thus been amongst the forerunners to deploy virtualization technology to multiplex their physical machines (PMs) to as many virtual machines (VMs) as possible in order to utilize their hardware resources more effectively and save power. The achievement of this objective strongly depends on how smart VMs are consolidated. In this paper, we show that blind consolidation of VMs not only does not reduce the power consumption of datacenters but it can lead to energy wastage. We present four models, namely the target system model, the application model, the energy model, and the migration model, to identify the performance interferences between processor and disk utilizations and the costs of migrating VMs. We also present a consolidation fitness metric to evaluate the merit of consolidating a number of known VMs on a PM based on the processing and storage workloads of VMs. We then propose an energy-aware scheduling algorithm using a set of objective functions in terms of this consolidation fitness metric and presented power and migration models. The proposed scheduling algorithm assigns a set of VMs to a set of PMs in a way to minimize the total power consumption of PMs in the whole datacenter. Empirical results show nearly 24.9% power savings and nearly 1.2% performance degradation when the proposed scheduling algorithm is used compared to when other scheduling algorithms are used.     

虚拟环境下Web服务动态负载均衡策略改进

为了提高Web服务集群的伸缩性和自动化能力,从虚拟化和负载均衡两方面研究集群系统,对现有负载采集策略做了改进,设计并实现了一种可根据负载值自动控制集群规模的模型XCluster。新模型运行在Xen提供的虚拟化环境中,实时监视宿主机层和虚拟机层的负载状态,随着集群系统总负载的增长,逐渐引入新的虚拟机来扩大集群规模,同时将任务合理分配到各个虚拟机节点上;当总负载下降时,逐渐关闭虚拟机缩小集群规模,释放出来的硬件资源又可以提供给其他集群系统使用。理论分析和实验结果表明,XCluster只需占用很少的网络通信量完成信息收集和命令下达,能够充分利用虚拟机易于管理的优势完成后端节点的调度,并且在任务总量相同的情况下,使用尽可能少的集群节点来执行任务。     

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.     

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%.     

Toward scalable Web systems on multicore clusters: making use of virtual machines

Limited by the existing design pattern, a lot of existing softwares have not yet taken full use of multicore processing power, incurring low utilization of hardware, even a bottleneck of the whole system. To address this problem, in this paper, we propose a VM-based Web system on multicore clusters. The VM-based Web system is scheduled by Linux Virtual Server (LVS) and we implement the web server with Tomcat. In the mean time, we develop VNIX, a set of VM management toolkit, to facilitate managing VMs on clusters, aiming at improving the usage of multicore CPU power. To reduce resources contention among VMs, we propose to deploy LVS schedulers distributively on different physical nodes. To evaluate our approach, we conduct extensive experiments to compare VM-based Web system with classical physical machine-based Web system. Our experimental results demonstrate that the proposed VM-based Web system can result in throughput improvements of up to three times compared with the same multicore clusters, with an error rate at the server side as low as 20% of that of classic systems.     

基于IaaS云平台的无线传感网实时监测系统

本文基于中科大IaaS云平台设计并实现了一种无线传感网实时监测系统.利用6LoWPAN建立可自组网的无线传感器网络,系统中各个传感器节点将传感器采集的数据发送至汇聚节点,汇聚节点通过网络将数据传送至中科大云平台服务器中进行记录和分析.本文采用Nginx作为Web服务器,uWSGI应用服务器和Tornado应用服务器分别处理用户的非实时数据和实时数据请求.采用WebSocket技术实现实时数据的传输,提高了系统实时性.测试结果表明本文对构建可靠且可灵活部署的实时监测系统具有指导意义.     

VM consolidation: A real case based on OpenStack Cloud

In recent years, Cloud computing has been emerging as the next big revolution in both computer networks and Web provisioning. Because of raised expectations, several vendors, such as Amazon and IBM, started designing, developing, and deploying Cloud solutions to optimize the usage of their own data centers, and some open-source solutions are also underway, such as Eucalyptus and OpenStack. Cloud architectures exploit virtualization techniques to provision multiple Virtual Machines (VMs) on the same physical host, so as to efficiently use available resources, for instance, to consolidate VMs in the minimal number of physical servers to reduce the runtime power consumption. VM consolidation has to carefully consider the aggregated resource consumption of co-located VMs, in order to avoid performance reductions and Service Level Agreement (SLA) violations. While various works have already treated the VM consolidation problem from a theoretical perspective, this paper focuses on it from a more practical viewpoint, with specific attention on the consolidation aspects related to power, CPU, and networking resource sharing. Moreover, the paper proposes a Cloud management platform to optimize VM consolidation along three main dimensions, namely power consumption, host resources, and networking. Reported experimental results point out that interferences between co-located VMs have to be carefully considered to avoid placement solutions that, although being feasible from a more theoretical viewpoint, cannot ensure VM provisioning with SLA guarantees.     

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.     

Performance models of storage contention in cloud environments

We propose simple models to predict the performance degradation of disk requests due to storage device contention in consolidated virtualized environments. Model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same server. We first propose a trace-driven approach that evaluates a queueing network with fair share scheduling using simulation. The model parameters consider Virtual Machine Monitor level disk access optimizations and rely on a calibration technique. We further present a measurement-based approach that allows a distinct characterization of read/write performance attributes. In particular, we define simple linear prediction models for I/O request mean response times, throughputs and read/write mixes, as well as a simulation model for predicting response time distributions. We found our models to be effective in predicting such quantities across a range of synthetic and emulated application workloads.     

Optimal provisioning of servers for hosting services of multiple types

Services of different types are provided to paying customers by instantiating Virtual Machines on servers hired from a cloud. Different VMs can share a server, subject to one or more resource constraints. Incoming jobs whose resource requirements cannot be satisfied are lost. The objective is to maximize the long-term average profit per unit time. A single-server model is analyzed exactly and the results provide approximations for the system with n servers. The latter is also solved exactly when the servers are dedicated and when the VMs can migrate instantaneously. Numerical examples and comparisons with simulations are presented.