Mostra el registre d'ítem simple
Deadline constrained prediction of job resource requirements to manage high-level SLAs for SaaS cloud providers
dc.contributor.author | Reig Ventura, Gemma |
dc.contributor.author | Alonso López, Javier |
dc.contributor.author | Guitart Fernández, Jordi |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.date.accessioned | 2010-05-10T09:43:58Z |
dc.date.available | 2010-05-10T09:43:58Z |
dc.date.issued | 2010-04-28 |
dc.identifier.uri | http://hdl.handle.net/2117/7138 |
dc.description.abstract | For a non IT expert to use services in the Cloud is more natural to negotiate the QoS with the provider in terms of service-level metrics –e.g. job deadlines– instead of resourcelevel metrics –e.g. CPU MHz. However, current infrastructures only support resource-level metrics –e.g. CPU share and memory allocation– and there is not a well-known mechanism to translate from service-level metrics to resource-level metrics. Moreover, the lack of precise information regarding the requirements of the services leads to an inefficient resource allocation –usually, providers allocate whole resources to prevent SLA violations. According to this, we propose a novel mechanism to overcome this translation problem using an online prediction system which includes a fast analytical predictor and an adaptive machine learning based predictor. We also show how a deadline scheduler could use these predictions to help providers to make the most of their resources. Our evaluation shows: i) that fast algorithms are able to make predictions with an 11% and 17% of relative error for the CPU and memory respectively; ii) the potential of using accurate predictions in the scheduling compared to simple yet well-known schedulers. |
dc.format.extent | 6 p. |
dc.language.iso | eng |
dc.relation.ispartofseries | UPC-DAC-RR-2010-9 |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors |
dc.subject.lcsh | Cloud computing |
dc.title | Deadline constrained prediction of job resource requirements to manage high-level SLAs for SaaS cloud providers |
dc.type | External research report |
dc.subject.lemac | Arquitectura de computadors |
dc.contributor.group | Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
dc.relation.publisherversion | http://gsi.ac.upc.edu/reports/2010/9/greigNCA10.pdf |
dc.rights.access | Open Access |
local.identifier.drac | 2319586 |
dc.description.version | Preprint |
local.personalitzacitacio | true |
Fitxers d'aquest items
Aquest ítem apareix a les col·leccions següents
-
Reports de recerca [58]
-
Reports de recerca [181]