Planning Live-Migrations to Prepare Servers for Maintenance

International audienceIn a virtualized data center, server maintenance is a common but still critical operation. A prerequisite is indeed to relocate elsewhere the Virtual Machines (VMs) running on the production servers to prepare them for the maintenance. When the maintenance focuses several servers, this may lead to a costly relocation of several VMs so the migration plan must be chose wisely. This however implies to master numerous human, technical, and economical aspects that play a role in the design of a quality migration plan. In this paper, we study migration plans that can be decided by an operator to prepare for an hardware upgrade or a server refresh on multiple servers. We exhibit performance bottleneck and pitfalls that reduce the plan efficiency. We then discuss and validate possible improvements deduced from the knowledge of the environment peculiarities

Dynamic Consolidation of Highly Available Web Applications

Datacenters provide an economical and practical solution for hosting large scale n-tier Web applications. When scalability and high availability are required, each tier can be implemented as multiple replicas, which can absorb extra load and avoid a single point of failure. Realizing these benefits in practice, however, requires that replicas be assigned to datacenter nodes according to certain placement constraints. To provide the required quality of service to all of the hosted applications, the datacenter must consider of all of their specific constraints. When the constraints are not satisfied, the datacenter must quickly adjust the mappings of applications to nodes, taking all of the applications' constraints into account. This paper presents Plasma, an approach for hosting highly available Web applications, based on dynamic consolidation of virtual machines and placement constraint descriptions. The placement constraint descriptions allow the data- center administrator to describe the datacenter infrastructure and each appli- cation administrator to describe his requirements on the VM placement. Based on the descriptions, Plasma continuously optimizes the placement of the VMs in order to provide the required quality of service. Experiments on simulated configurations show that the Plasma reconfiguration algorithm is able to man- age a datacenter with up to 2000 nodes running 4000 VMs with 800 placement constraints. Real experiments on a small cluster of 8 working nodes running 3 instances of the RUBiS benchmarks with a total of 21 VMs show that con- tinuous consolidation is able to reach 85% of the load of a 21 working nodes cluster.Externaliser l'hébergement d'une application Web n-tiers virtualisée dans un centre de données est une solution économiquement viable. Lorsque l'administrateur de l'application considère les problèmes de haute disponibilité tels que le passage à l'échelle et de tolérance aux pannes, chaque machine virtuelle (VM) embarquant un tiers est répliquée plusieurs fois pour absorber la charge et éviter les points de défaillance. Dans la pratique, ces VM doivent être placées selon des contraintes de placement précises. Pour fournir une qualité de service à toutes les applications hébergées, l'administrateur du centre de données doit considérer toutes leurs contraintes. Lorsque des contraintes de placement ne sont plus satisfaites, les VM alors doivent être ré-agencées au plus vite pour retrouver un placement viable. Ce travail est complexe dans un environnement consolidé où chaque nœud peut héberger plusieurs VM. Cet article présente Plasma, un système autonome pour héberger les VM des applications Web haute-disponibilité dans un centre de données utilisant la consolidation dynamique. Par l'intermédiaire de scripts de configuration, les administrateurs des applications décrivent les contraintes de placement de leur VM tandis que l'administrateur système décrit l'infrastructure du centre de données. Grâce à ces descriptions, Plasma optimise en continu le placement des VM pour fournir la qualité de service attendue. Une évaluation avec des données simulées montre que l'algorithme de reconfiguration de Plasma permet de superviser 2000 nœuds hébergeant 4000 VM selon 800 contraintes de placement. Une évaluation sur une grappe de 8 nœuds exécutant 3 instances de l'application RUBiS sur 21 VM montre que la consolidation fournit par Plasma atteint 85% des performances d'une grappe de 21 nœuds

Power-Thermal Modeling and Control of Energy-Efficient Servers and Datacenters

Recently, the energy-efficiency constraints have become the dominant limiting factor for datacenters due to their unprecedented increase of growing size and electrical power demands. In this chapter we explain the power and thermal modeling and control solutions which can play a key role to reduce the power consumption of datacenters considering time-varying workload characteristics while maintaining the performance requirements and the maximum temperature constraints. We first explain simple-yet-accurate power and temperature models for computing servers, and then, extend the model to cover computing servers and cooling infrastructure of datacenters. Second, we present the power and thermal management solutions for servers manipulating various control knobs such as voltage and frequency of servers, workload allocation, and even cooling capability, especially, flow rate of liquid cooled servers). Finally, we present the solution to minimize the server clusters of datacenters by proposing a solution which judiciously allocates virtual machines to servers considering their correlation, and then, the joint optimization solution which enables to minimize the total energy consumption of datacenters with hybrid cooling architecture (including the computing servers and the cooling infrastructure of datacenters)

FLA-SLA aware cloud collation formation using fuzzy preference relationship multi-decision approach for federated cloud

Cloud Computing provides a solution to enterprise applications in resolving their services at all level of Software, Platform, and Infrastructure. The current demand of resources for large enterprises and their specific requirement to solve critical issues of services to their clients like avoiding resources contention, vendor lock-in problems and achieving high QoS (Quality of Service) made them move towards the federated cloud. The reliability of the cloud has become a challenge for cloud providers to provide resources at an instance request satisfying all SLA (Service Level Agreement) requirements for different consumer applications. To have better collation among cloud providers, FLA (Federated Level Agreement) are given much importance to get consensus in terms of various KPI’s (Key Performance Indicator’s) of the individual cloud providers. This paper proposes an FLA-SLA Aware Cloud Collation Formation algorithm (FS-ACCF) considering both FLA and SLA as major features affecting the collation formation to satisfy consumer request instantly. In FS-ACCF algorithm, fuzzy preference relationship multi-decision approach was used to validate the preferences among cloud providers for forming collation and gaining maximum profit. Finally, the results of FS-ACCF were compared with S-ACCF (SLA Aware Collation Formation) algorithm for 6 to 10 consecutive requests of cloud consumers with varied VM configurations for different SLA parameters like response time, process time and availability

Allocation of Virtual Machines in Cloud Data Centers - A Survey of Problem Models and Optimization Algorithms

Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and also environmental impact. Therefore, cloud providers must optimize the usage of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This paper surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, also pointing out the areas that need further research in the future