Title :

GenPack: A Generational Scheduler for Cloud Data Centers

Author(s) :

Havet, Aurélien [Auteur]
Institut d'Informatique [Neuchâtel] [IIUN]
Schiavoni, Valerio [Auteur]
Institut d'Informatique [Neuchâtel] [IIUN]
Felber, Pascal [Auteur]
Institut d'Informatique [Neuchâtel] [IIUN]
Colmant, Maxime [Auteur]
Agence de l'Environnement et de la Maîtrise de l'Énergie [ADEME]
Université de Lille, Sciences et Technologies
Self-adaptation for distributed services and large software systems [SPIRALS]
Rouvoy, Romain [Auteur]
Université de Lille, Sciences et Technologies
Self-adaptation for distributed services and large software systems [SPIRALS]
Institut universitaire de France [IUF]
Fetzer, Chistof [Auteur]
Technische Universität Dresden = Dresden University of Technology [TU Dresden]

Scientific editor(s) :

Indranil Gupta
Jiangchuan Liu

Conference title :

5th IEEE International Conference on Cloud Engineering (IC2E)

City :

Vancouver

Country :

Canada

Start date of the conference :

2017-04-04

Journal title :

Proceedings of the 5th IEEE International Conference on Cloud Engineering (IC2E)

Publisher :

IEEE

Publication date :

2017-04-04

English keyword(s) :

virtual machine
scheduler
docker
container
profiling
energy
cloud
data center

HAL domain(s) :

Informatique [cs]/Génie logiciel [cs.SE]
Informatique [cs]/Web
Informatique [cs]/Système d'exploitation [cs.OS]

English abstract : [en]

Cloud data centers largely rely on virtualization to provision resources and host services across their infrastructure. The scheduling problem has been widely studied and is well understood when the resource requirements ...
Show more >Cloud data centers largely rely on virtualization to provision resources and host services across their infrastructure. The scheduling problem has been widely studied and is well understood when the resource requirements and the expected lifetime of services are known beforehand. In contrast, when workloads are not known in advance, effective scheduling of services, and more generally system containers, becomes much more complex. In this paper, we propose GENPACK, a framework for system containers scheduling in cloud data centers that leverages principles from generational garbage collection (GC). It combines runtime monitoring of system containers to learn their requirements and properties, and a scheduler that manages different generations of servers. The population of these generations may vary over time depending on the global load, hence they are subject to being shut down when idle to save energy. We implemented GENPACK and tested it in a dedicated data center, showing that it can be up to 23% more energy-efficient that SWARM’s built-in scheduling policies on a real-world trace.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

SAT comme Service

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL