Abstract
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. Unfortunately, many parameters are uncertain or very difficult to predict precisely in the real world. As a consequence, a once calculated solution may be highly infeasible in practice. In this paper, we use methods from robust optimization theory in order to quantify the impact of uncertainty in modern data centers. We study the impact of different parameter uncertainties on the energy efficiency and overbooking ratios such as e.g. VM resource demands, migration related overhead or the power consumption model of the servers used. 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. We show that, by using our model, Cloud operators can calculate a more robust migration schedule leading to higher total energy consumption. A more risky operator may well choose a more opportunistic schedule leading to lower energy consumption but also higher risk of SLA violation.
Highlights
Energy efficiency is an increasingly important concern for datacenter operators due to both cost and environmental issues
(d) Uncertain additional overhead for migrating VM2. It is very difficult for a cloud provider to decide about the proper capacity management because, on one side, the energy consumption decreases by packing more virtual machine (VM) on a given physical machines (PMs) as less PMs need to be powered on; on the other hand, there is a higher risk of potential Service Level Agreements (SLA) violations due to performance degradation if all the VMs are running on their peak
Unlike them, we take a range of values for the parameter Pmax and Pidle in order to present PMs which are invariably heterogeneous in terms of their power consumption
Summary
Energy efficiency is an increasingly important concern for datacenter operators due to both cost and environmental issues. Finding an optimal allocation of VMs to support their resource demands on the given set of physical servers in order to minimize e.g. energy consumption is a very hard computational problem and has led to a number of interesting mathematical modeling approaches in recent years [6, 19, 29, 32, 41]. Common to all those models is the assumption that input data that drives those models is known precisely, which is very difficult to achieve in practice.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.