Optimizing Resource allocation while handling SLA violations in Cloud Computing platforms
EYRAUD-DUBOIS, Lionel
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
LARCHEVÊQUE, Hubert
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
EYRAUD-DUBOIS, Lionel
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
LARCHEVÊQUE, Hubert
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
< Réduire
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Langue
en
Communication dans un congrès
Ce document a été publié dans
IPDPS - 27th IEEE International Parallel & Distributed Processing Symposium, 2013-05, Boston. 2013
Résumé en anglais
In this paper we study a resource allocation problem in the context of Cloud Computing, where a set of Virtual Machines (VM) has to be placed on a set of Physical Machines (PM). Each VM has a given demand (e.g. CPU demand), ...Lire la suite >
In this paper we study a resource allocation problem in the context of Cloud Computing, where a set of Virtual Machines (VM) has to be placed on a set of Physical Machines (PM). Each VM has a given demand (e.g. CPU demand), and each PM has a capacity. However, each VM only uses a fraction of its demand. The aim is to exploit the difference between the demand of the VM and its real utilization of the resources, to exploit the capacities of the PMs as much as possible. Moreover, the real consumption of the VMs can change over time (while staying under its original demand), implying sometimes expensive ''SLA violations'', corresponding to some VM's consumption not satisfied because of overloaded PMs. Thus, while optimizing the global resource utilization of the PMs, it is necessary to ensure that at any moment a VM's need evolves, a few number of migrations (moving a VM from PM to PM) is sufficient to find a new configuration in which all the VMs' consumptions are satisfied. We modelize this problem using a fully dynamic bin packing approach and we present an algorithm ensuring a global utilization of the resources of 66%. Moreover, each time a PM is overloaded at most one migration is necessary to fall back in a configuration with no overloaded PM, and only 3 different PMs are concerned by required migrations that may occur to keep the global resource utilization correct. This allows the platform to be highly resilient to a great number of changes.< Réduire
Mots clés en anglais
cloud computing
resource allocation
server consolidation
Project ANR
Simulation de systèmes de prochaine génération - ANR-11-INFR-0013
Origine
Importé de halUnités de recherche