Abstract:
Cloud computing offer highly scalable, and
economical infrastructure for promising heterogeneous
platforms and various applications. According to the
growing demand nature of cloud infrastructure
resources, the cloud providers face the challenge of
performing the effective resource management. This
paper presents the development of the CPU resource
demand prediction model for cloud infrastructure to
overcome the critical issue of the cloud providers for
workload forecasting and optimal resource
management. The model is developed based on the
powerful machine learning technique, Random Forests
(RF) algorithm via the real data center workload traces.
To get the best prediction model by RF, the parameter
optimization is performed. Moreover, some features of
workload traces cannot influence in prediction and also
give overheads in model development time. So, the
feature selection is applied to extract the important
features. The performance evaluation of the proposed
model against four workload traces is also presented.
