Deadline constrained prediction of job resource requirements to manage high-level SLAs for SaaS cloud providers
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/7138
Tipus de documentReport de recerca
Data publicació2010-04-28
Condicions d'accésAccés obert
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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.
Forma partUPC-DAC-RR-2010-9
URL repositori externhttp://gsi.ac.upc.edu/reports/2010/9/greigNCA10.pdf
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greigNCA10.pdf | 270,7Kb | Visualitza/Obre |