Cloud computing has revolutionized the way software, platforms, and
infrastructure can be acquired by making them available as on-demand
services that can be accessed from anywhere via a web browser. Due to
its ubiquitous nature Cloud data centers continuously experience
fluctuating workloads which demands for dynamic resource
provisioning. These workloads are either placed on Virtual Machines
(VMs) or containers which abstract the underlying physical resources
deployed at the data center. A proactive or reactive method can be used
to allot required resources to the workload. Reactive approaches tend
to be inefficient as it takes a significant amount of time to configure the
resources to meet the change in demands. A proactive approach for
resource management is better in meeting workload demands as it
makes an appropriate number of resources available in advance to
cater to the fluctuations in workload. The success of such an approach
relies on the ability of the resource management module of a data
center to accurately predict future workloads. Machine Learning (ML)
has already proven itself to be very effective in performing prediction
in various domains. In this work, we propose an ML meta-classifier
based on stacked generalization for predicting future workloads
utilising the past workload trends which are recorded as event logs at
Cloud data centers. The proposed model showed a prediction accuracy
of 98.5% indicating its applicability for the Cloud environment where
SLA requirements must be closely adhered to.