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In the context of GreenPAD project it is important to predict the energyconsumption of individual (and mixture of) VMs / workload for optimalscheduling (running those VMs which require higher energy when there is moregreen energy available and vice-versa) in order to maximize green energyutilization.  For this we execute the following experiments on an Openstack cloud testbedconsisting of Fujitsu servers: VM energy measurement for differentconfigurations (flavor + workload) and VM energy prediction for a newconfiguration. The automation framework for running these experiments uses bashscripts 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 onregression. We first collect the resource usage (using collected) and theassociated power usage (using IPMI) for different VM configurations and usethis to build a (multi-) regression model (between resource usage and VM energyconsumption). Then we use the information about the resource usage patterns ofthe new workload to predict the power usage. For predicting power for mix ofworkloads we execute (build a regression model based on) experiments withrandom workloads. We observe the highest energy usage for CPU-intensiveworkloads followed by memory-intensive workloads.

VM Power Prediction in Distributed Systems for Maximizing Renewable Energy Usage