Abstract:
Cloud data centers offer utility-oriented IT
services to users worldwide. The nature of
resource demand of cloud data centers is elastic
as the overall workloads are always changing.
For handling dynamic workload nature ahead of
the needs, elastic resource demand prediction is
the key issue in cloud data centers. If the cloud
provider does not ensure they have enough
resources to meet demand which will lead to
under or over provisioning of resources. In this
paper, integrated elastic resource prediction
system is proposed by combining signaturebased
prediction and state-based prediction
approahces. The workload nature of the cloud
data centers are both repeating pattern and nonrepeating
pattern workload. Signature-based
prediction is used to predict the repeating
pattern workload and state-based prediction is
used to predict the non-repeating pattern
workload. Integrated Elastic Resource
Prediction (IERP) system is used to predict the
mixed workload pattern. Feature selection is
conducted first to reduce processing overheads
while achieving high prediction accuracy. The
proposed predictors are implemented and
evaluated with real world workload traces which
show that they achieve high resource prediction accuracy with above 95%.
