Affiliations: School of Computing Science and Engineering, VIT University, Chennai, India
Correspondence:
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Corresponding author: K. Dinesh Kumar, School of Computing Science and Engineering, VIT University, Chennai, India. E-mail: kdinesh.kumar2015@vit.ac.in.
Abstract: Nowadays, most of the companies are shifting from desktop PCs application to cloud based
applications deployed on clouds to provide the effective services in the heterogeneous environments.
But, in order to survive in such a cloud competitive market, cloud providers must reach quality of service
(QoS) for their customers, otherwise losing their cloud customers to competitors. In cloud computing,
providing good QoS is a main challenging task because workloads changes over a time. In
Software-as-a-Service (SaaS) model, the workload of the cloud application changes continuously based on
the user requests, and insufficient resource allocation to the application leads to the QoS dropping,
loss of consumers and revenue. On the other side, allocating unnecessary amount of resources to the
application which can lead wastage of cost and energy to maintain the resources such as datacenters,
servers, cooling technology and network bandwidth etc. This issue can be solved with prediction methods,
which can predict the future workload of the cloud application in terms of needed resources and allocate
those resources in advance, and releasing the resources when they are not needed. This paper focuses on
importance of prediction methods for effective resource provisioning system. This paper brings out a
review on the state of the resource provisioning system. Finally, future trends of the prediction model
are discussed.
