  Workloads in modern cloud data centers are becoming increasingly complex. The
number of workloads running in cloud data centers has been growing
exponentially for the last few years, and cloud service providers (CSP) have
been supporting on-demand services in real-time. Realizing the growing
complexity of cloud environment and cloud workloads, hardware vendors such as
Intel and AMD are increasingly introducing cloud-specific workload acceleration
features in their CPU platforms. These features are typically targeted towards
popular and commonly-used cloud workloads. Nonetheless, uncommon,
customer-specific workloads (unknown workloads), if their characteristics are
different from common workloads (known workloads), may not realize the
potential of the underlying platform. To address this problem of realizing the
full potential of the underlying platform, we develop a machine learning based
technique to characterize, profile and predict workloads running in the cloud
environment. Experimental evaluation of our technique demonstrates good
prediction performance. We also develop techniques to analyze the performance
of the model in a standalone manner.
