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Summary: Prediction of Job Resource Requirements for
Deadline Schedulers to Manage High-Level SLAs
on the Cloud
Gemma Reig, Javier Alonso, and Jordi Guitart
Universitat Polit`ecnica de Catalunya (UPC) and Barcelona Supercomputing Center (BSC)
Barcelona, Spain
Email: {greig, alonso, jguitart}@ac.upc.edu
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 resource-
level 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
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