  In the context of GreenPAD project it is important to predict the energy
consumption of individual (and mixture of) VMs / workload for optimal
scheduling (running those VMs which require higher energy when there is more
green energy available and vice-versa) in order to maximize green energy
utilization.
  For this we execute the following experiments on an Openstack cloud testbed
consisting of Fujitsu servers: VM energy measurement for different
configurations (flavor + workload) and VM energy prediction for a new
configuration. The automation framework for running these experiments uses bash
scripts which call tools like 'stress' (simulating workloads), 'collected'
(resource usage) and 'IPMI' (power measurement).
  We propose a linear model for predicting the power usage of the VMs based on
regression. We first collect the resource usage (using collected) and the
associated power usage (using IPMI) for different VM configurations and use
this to build a (multi-) regression model (between resource usage and VM energy
consumption). Then we use the information about the resource usage patterns of
the new workload to predict the power usage. For predicting power for mix of
workloads we execute (build a regression model based on) experiments with
random workloads. We observe the highest energy usage for CPU-intensive
workloads followed by memory-intensive workloads.
