As estimated by Amazon, the monetary costs of a datacenter related to energy consumption is approximately 42% which include both direct power consumption (approximately 19%) and the cooling infrastructure (approximately 23%). Clearly, reducing the energy consumption of a datacenter is an economic incentive for datacenter operators, and would also lead towards a more sustainable environment as it helps to reduce the global CO 2 footprint. A more risky operator may well choose a more opportunistic schedule leading to lower energy consumption but also higher risk of SLA violation.Įnergy efficiency is an increasingly important concern for datacenter operators due to both cost and environmental issues. We show that, by using our model, Cloud operators can calculate a more robust migration schedule leading to higher total energy consumption. We also show that setting aside additional resource to cope with uncertainty of workload influences the overbooking ration of the servers and the energy consumption. VM resource demands, migration related overhead or the power consumption model of the servers used. We study the impact of different parameter uncertainties on the energy efficiency and overbooking ratios such as e.g. In this paper, we use methods from robust optimization theory in order to quantify the impact of uncertainty in modern data centers. As a consequence, a once calculated solution may be highly infeasible in practice. Unfortunately, many parameters are uncertain or very difficult to predict precisely in the real world. Energy efficient virtual machine (VM) consolidation in modern data centers is typically optimized using methods such as Mixed Integer Programming, which typically require precise input to the model.
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