Abstract:
Resource allocation plays an important role in
Virtualized Data Center (VDC). The applications
running in VDC are mostly business critical
applications with Quality-of-Service (QoS)
requirements. Moreover, dynamic resource allocation
and real time monitoring of the resource usage of VMs
are also needed to reduce under resource utilization and
over resource utilization. Therefore, resource usage
prediction is required for dynamic resource allocation
systems. In efficient dynamic resource allocation, the
resources are allocated to a VM while meeting their
Service Level Agreement (SLA). The main contribution
of this work is two-fold. The first is the generation of
CPU usage prediction models by applying different
powerful machine learning techniques. The second is
SLA evaluation on predicted value by using proposed
SLA metric. To evaluate the efficiency of these models,
experiments are carried out by using CPU profiles from
real world data centre. According to the experiments,
proposed resource prediction models have promising
accuracy.
