• Nidhi Singh , Shrisha Rao
The 11th International Conference on Machine Learning and Applications, Boca Raton, Florida, USA, December 12-15, 2012.
Growing scale of server infrastructure in large
datacenters has led to an increased need for effective server
workload prediction mechanisms. Two main challenges faced in
server workload prediction task are lack of large-scale training
data and changes in the underlying distribution of server
workloads in events like change in dominant applications of
servers or change in allocation of servers, etc. In this work, we
propose an online server workload prediction approach based
on ensemble learning which addresses these issues. We evaluate
the proposed approach using real dataset of an enterprise data
center and a synthetic dataset. Experimental results reveal that
the proposed approach achieves accuracy of 87.8% on real dataset and 88.8% on synthetic dataset.
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