Performance analysis and models for collocated VMs running on multi-core physical machines

Next generation high performance computers will massively use virtualization as a way to share hardware resources between multiple applications and to provide flexible mechanisms for fault tolerance and energy optimisation. In this context, understanding the performance behavior of virtual machines and the interference between them is a major scientific challenge that will allow a more efficient usage of resources. The first step is to characterize CPU usage sharing and to propose a performance model for virtual machines. Nonetheless, focusing on the sharing of a single CPU core is no more possible as next generation high performance machines will contain a large number of cores. Moreover, as these cores share micro-architectural resources e.g. caches, using a single core performance model is not sufficient as inter-core interference can happen. Finally, to be able to use such a model in large scale infrastructures as Clouds or high performance computers, the model must be lightweight to simulate the behavior of tens of thousands physical machines hosting hundreds of thousands virtual machines (VMs). In this paper, we present an in-depth analysis of the performance of collocated VMs. By running our experiments on the Grid'5000 testbed, we were able to evaluate 2 processor families for a total of 6 different processor models. We have systematically explored the effect of collocation by testing all the different VCPU to CPU mapping while taking into account micro-architectural components (shared caches and NUMA nodes). We also explored the effect of multi-core virtual machines. Based on these experiments, we evaluate 8 lightweight performance models and observe that the virtual machine performance can be accurately predicted using a model that takes into account the number of VMs on the core and on the related NUMA node (with less than 8% error). Finally, we validate our models on several processors and on both single and multi-(virtual)-cores VM. Using this model, we can increase the accuracy of the virtualization layer of the general purpose distributed system simulator SimGrid and improve it's usability to simulate (HPC) Clouds. These results may also be used to improve VM placement algorithms.La prochaine génération d'ordinateur haute performance (HPC) utilisera massivement la virtualisation comme un moyen pour partager les ressources matérielles entre plusieurs applications et également pour fournir des mécanismes flexibles pour la tolérance aux fautes ainsi que l'optimisation énergétique. Dans ce contexte, comprendre comment se comporte la performance des machines virtuelles et les interférences entre elles est un défi scientifique majeur qui permettra d'aller vers un utilisation plus efficace des ressources. La première étape est la caractérisation du partage de processeur et d'en proposer un modèle de performance pour les machines virtuelles. Mais, se concentrer sur le partage d'un processeur unique n'est plus possible. En effet, les prochaines générations d'ordinateur haute performance contiendront un très large ensemble de coeurs. De plus, comme ces coeurs partages des ressources micro-architecturales, e.g. caches, utilisé un modéle de performance d'un coeur unique n'est pas suffisant car il ne capturerai pas les interferences entre coeurs. Finalement, pour pouvoir utiliser un tel modèle à l'échelle d'infrastructures large échelle tel que les Clouds ou les ordinateurs HPC, le modèle doit être légé pour pouvoir simuler des dizaines de milliers de machines physiques hébergeant des centaines de milliers de machines virtuelles. Dans ce papier, nous présentons une analyse en profondeur des performances de machines virtuelles colocalisées i.e. s'exécutant sur la même machine physique. En exécutant nos expérimentations sur le banc d'essai Grid'5000, nous avons pu évaluer 2 familles de processeurs pour un total de 6 différents modèles de processeurs. Nous avons exploré systématiquement l'effet de la colocalisation en testant tous les différentes placements VCPU vers CPU tout en prenant en compte les composants micro-architecturaux (caches partagés et NUMA nodes). Nous avons également exploré l'effet des machines virtuelles qui ont plusieurs coeurs. En se basant sur ces expérimentations, nous avons évalué 8 modèles légés de performance et observé que la performance d'une machine virtuelle peut être précisément prédite en utilisant un modèle qui prend en compte le nombre de machines virtuelles sur le coeur et le nombre de machines virtuelles sur la NUMA Node (avec un taux d'erreur inférieur à 8%). Finalement, nous avons évalué nos modèles sur plusieurs processeurs et sur des machines virtuelles avec un ou plusieurs processeurs virtuels. En utilisant ce modèle, nous pourrons accroître la précision de la couche de virtualisation du simulateur générique pour les systèmes distribués, SimGrid mais également sa capacité à simuler des Clouds (haute performance). Nos résultats peuvent également être utilisés pour améliorer les algorithmes de placement de machines virtuelles

SimGrid VM: Virtual Machine Support for a Simulation Framework of Distributed Systems

International audienceAs real systems become larger and more complex, the use of simulator frameworks grows in our research community. By leveraging them, users can focus on the major aspects of their algorithm, run in-siclo experiments (i.e., simulations), and thoroughly analyze results, even for a large-scale environment without facing the complexity of conducting in-vivo studies (i.e., on real testbeds). Since nowadays the virtual machine (VM) technology has become a fundamental building block of distributed computing environments, in particular in cloud infrastructures, our community needs a full-fledged simulation framework that enables us to investigate large-scale virtualized environments through accurate simulations. To be adopted, such a framework should provide easy-to-use APIs as well as accurate simulation results. In this paper, we present a highly-scalable and versatile simulation framework supporting VM environments. By leveraging SimGrid, a widely-used open-source simulation toolkit, our simulation framework allows users to launch hundreds of thousands of VMs on their simulation programs and control VMs in the same manner as in the real world (e.g., suspend/resume and migrate). Users can execute computation and communication tasks on physical machines (PMs) and VMs through the same SimGrid API, which will provide a seamless migration path to IaaS simulations for hundreds of SimGrid users. Moreover, SimGrid VM includes a live migration model implementing the precopy migration algorithm. This model correctly calculates the migration time as well as the migration traffic, taking account of resource contention caused by other computations and data exchanges within the whole system. This allows user to obtain accurate results of dynamic virtualized systems. We confirmed accuracy of both the VM and the live migration models by conducting several micro-benchmarks under various conditions. Finally, we conclude the article by presenting a first use-case of one consolidation algorithm dealing with a significant number of VMs/PMs. In addition to confirming the accuracy and scalability of our framework, this first scenario illustrates the main interest of SimGrid VM: investigating through in-siclo experiments pros/cons of new algorithms in order to limit expensive in-vivo experiments only to the most promising ones

A Unified Monitoring Framework for Energy Consumption and Network Traffic

International audienceProviding experimenters with deep insight about the effects of theirexperiments is a central feature of testbeds. In this paper, wedescribe Kwapi, a framework designed in the context of the Grid'5000testbed, that unifies measurements for both energy consumption andnetwork traffic. Because all measurements are taken at theinfrastructure level (using sensors in power and network equipment),using this framework has no dependencies on the experiments themselves.Initially designed for OpenStack infrastructures, the Kwapi framework allowsmonitoring and reporting of energy consumption of distributed platforms. Inthis article, we present the extension of Kwapi to network monitoring, andoutline how we overcame several challenges: scaling to a testbed the size ofGrid'5000 while still providing high-frequency measurements; providing long-termloss-less storage of measurements; handling operational issues when deployingsuch a tool on a real infrastructure

Kwapi: A Unified Monitoring Framework for Energy Consumption and Network Traffic

International audienceShort version of the TRIDENTCOM’2015 talk about Kwap

Rhéologie visqueuse anisotrope des matériaux géophysiques et applications à la dynamique des manteaux planétaires

PARIS-BIUSJ-Sci.Terre recherche (751052114) / SudocSudocFranceF

Adding a Live Migration Model Into SimGrid, One More Step Toward the Simulation of Infrastructure-as-a-Service Concerns

International audienceVirtual machine (VM) placement problem has been active research area over the past decade. The research community needs an open simulation framework that can accurately and scalably simulate virtual machine operations including live migrations. However, existing cloud simulation frameworks cannot reproduce live migration behaviors correctly. A naive migration model, not considering memory update operations nor resource sharing contention, can drastically underestimate the duration of a live migration and the size of migration traffic. In this paper, we propose a simulation framework of virtualized distributed systems with the first class support of live migration operations. We developed a resource share calculation mechanism for VMs and a live migration model implementing the precopy migration algorithm of Qemu/KVM. We extended a widely-used simulation toolkits, SimGrid, which allows users to simulate large-scale distributed systems by using user friendly programming API. Through experiments, we confirmed that our simulation framework correctly reproduced live migration behaviors of the real world under various conditions. Through a first use case, we also confirmed that it is possible to conduct large scale simulations of complex virtualized workloads upon hundred thousands of VMs upon thousands of physical machines (PMs)

Adding a Live Migration Model Into SimGrid, One More Step Toward the Simulation of Infrastructure-as-a-Service Concerns

International audienceVirtual machine (VM) placement problem has been active research area over the past decade. The research community needs an open simulation framework that can accurately and scalably simulate virtual machine operations including live migrations. However, existing cloud simulation frameworks cannot reproduce live migration behaviors correctly. A naive migration model, not considering memory update operations nor resource sharing contention, can drastically underestimate the duration of a live migration and the size of migration traffic. In this paper, we propose a simulation framework of virtualized distributed systems with the first class support of live migration operations. We developed a resource share calculation mechanism for VMs and a live migration model implementing the precopy migration algorithm of Qemu/KVM. We extended a widely-used simulation toolkits, SimGrid, which allows users to simulate large-scale distributed systems by using user friendly programming API. Through experiments, we confirmed that our simulation framework correctly reproduced live migration behaviors of the real world under various conditions. Through a first use case, we also confirmed that it is possible to conduct large scale simulations of complex virtualized workloads upon hundred thousands of VMs upon thousands of physical machines (PMs)

Anisotropic rheology of a cubic medium and implications for geological materials

International audienceDislocation creep, which is the dominant deformation mechanism in the upper mantle, results in a non-Newtonian anisotropic rheology. The implication of non-Newtonian rheology has been quite extensively studied in geodynamic models but the anisotropic aspect remains poorly investigated. In this paper, we propose to fill this gap by (1) introducing a simple mathematical description of anisotropic viscosity and (2) illustrating the link between plastic crystal deformation and bulk material rheology. The study relies on the highest symmetry of the anisotropic tensor, a cubic symmetry, for which anisotropy is characterized by one parameter only, δ. Firstorder implications of anisotropy are quantitatively explored as a function of δ. The effective rheology of the material is described as a function of the orientation of the crystals and of the imposed stress and the validity of the isotropic approximation is discussed. The model, applied to ringwoodite, a cubic crystal with spinel-type structure, predicts that the dynamics of the transition zone in the Earth's mantle is going to be strongly affected by mechanical anisotrop

Using the EXECO toolbox to perform automatic and reproducible cloud experiments

International audiencehis paper describes EXECO, a library that provides easy and efficient control of local or remote, standalone or parallel, processes execution, as well as tools designed for scripting distributed computing experiments on any computing platform. After discussing the EXECO internals, we illustrate its interest by presenting two experiments dealing with virtualization technologies on the Grid’5000 testbed

Suivi Lagrangien de gouttelettes et de leur évaporation dans un écoulement turbulent : de l’observation à la statistique

National audienc