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

Memory and Network Aware Scheduling of Virtual Machine Migrations

International audienceLive-migration has become a common operation on virtualized infrastructures. Indeed, it is widely used by resource management algorithms to distribute the load between servers and to reduce energy consumption. Operators rely also on migrations to prepare production servers for critical maintenance by relocating their running VMs elsewhere. To apply new VM placement decisions, live-migrations must be scheduled by selecting for each migration the moment to start and the bandwidth to allocate. Long migrations violate SLAs and reduce the practical benefits of placement algorithms. The VMs should then be migrated as fast as possible. To do so, the migration scheduler must be able to predict accurately the migration durations and schedule them accordingly. Dynamic VM placement algorithms focus extensively on computing a placement of quality. Their practical reactivity is however lowered by restrictive assumptions that underestimate the migration durations. For example, Entropy supposes a non-blocking homogeneous network coupled with a null dirty page rate and we already demonstrated that the network topology but also the workload live memory usage are dominating factors. Recently, some migration models have been developed and integrated into simulators to evaluate VM placement algorithms properly. While these models reproduce migrations finely, they are only devoted to simulation purpose and not used to compute scheduling decisions. We propose here a migration scheduler that considers the network topology, the migration routes, the VM memory usage and the dirty page rates, to compute precise migration durations and infer better schedules. We implemented our scheduler on top of BtrPlace, an extensible version of Entropy that allows to enrich the scheduling decision capabilities through plug-ins. To assess the flexibility of our scheduler, we also implemented constraints to synchronize migrations, to establish precedence rules, to respect power budgets and an objective that minimizes energy consumption. We evaluated our model accuracy and its resulting benefits by executing migration scenarios on a real testbed including a blocking network, mixed VM memory workloads and collocation settings. Our model predicted the migration durations with a 94% accuracy at minimum and an absolute error of 1 second while BtrPlace vanilla was only 30% accurate. This gain of precision led to wiser scheduling decisions. In practice, the migrations completed on average 3.5 time faster as compared to an execution based on BtrPlace vanilla. Thanks to a better control of migrations and power-switching actions we also reduced the power consumption of a server decommissioning scenario according to different power budgets

Ordonnancement contrôlé de migrations à chaud

National audienceMigrer à chaud une machine virtuelle (VM) est une opération basique dans un centre de don-nées. Tous les jours, des VM sont migrées pour répartir la charge, économiser de l'énergie ou préparer la maintenance de serveurs en production. Bien que les problèmes de placement des VM soient beaucoup étudiés, on observe que la gestion des migrations permettant de transiter vers ces nouveaux placements reste un domaine de second plan. On observe alors des algo-rithmes de placement de qualité, couplés à des algorithmes d'ordonnancement prenant des décisions peu pertinentes causées par des hypothèses irréalistes. Nous présentons dans ce papier mVM, un ordonnanceur de migrations reposant sur un modèle précis du réseau et du protocole de migration à chaud. Cet ordonnanceur a été intégré en place de celui du gestionnaire de VM BtrPlace. Nos premières expérimentations montrent que les durées des migrations sont estimées à 1.5 secondes près. Cette précision a permis de calculer de meilleurs ordonnancements, réduisant la durée des migrations par 3.5 comparée à BtrPlace

Scheduling Live-Migrations for Fast, Adaptable and Energy-Efficient Relocation Operations

International audienceEvery day, numerous VMs are migrated inside a datacenter to balance the load, save energy or prepare production servers for maintenance. Despite VM placement problems are carefully studied, the underlying migration scheduler rely on vague adhoc models. This leads to unnecessarily long and energy-intensive migrations. We present mVM, a new and extensible migration scheduler. mVM takes into account the VM memory workload and the network topology to estimate precisely the migration duration and take wiser scheduling decisions. mVM is implemented as a plugin of BtrPlace and can be customized with additional scheduling constraints to finely control the migrations. Experiments on a real testbed show mVM outperforms schedulers that cap the migration parallelism by a constant to reduce the completion time. Besides an optimal capping, mVM reduces the migration duration by 20.4% on average and the completion time by 28.1%. In a maintenance operation involving 96 VMs to migrate between 72 servers, mVM saves 21.5% Joules against BtrPlace. Finally, its current library of 6 constraints allows administrators to address temporal and energy concerns, for example to adapt the schedule and fit a power budget

Scheduling Live Migration of Virtual Machines

International audienceEvery day, numerous VMs are migrated inside a datacenter to balance the load, save energy or prepare production servers for maintenance. Despite VM placement problems are carefully studied, the underlying migration scheduler relies on vague adhoc models. This leads to unnecessarily long and energy-intensive migrations

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