On minimizing total energy consumption in the scheduling of virtual machine reservations

This paper considers the energy-efficient scheduling of virtual machine (VM) reservations in a Cloud Data center. Concentrating on CPU-intensive applications, the objective is to schedule all reservations non-preemptively, subjecting to constraints of physical machine (PM) capacities and running time interval spans, such that the total energy consumption of all PMs is minimized (called MinTEC for abbreviation). The MinTEC problem is NP-complete in general. The best known results for this problem is a 5-approximation algorithm for special instances using First-Fit-Decreasing algorithm and 3-approximation algorithm for general offline parallel machine scheduling with unit demand. By combining the features of optimality and workload in interval spans, we propose a method to find the optimal solution with the minimum number of job migrations, and a 2-approximation algorithm called LLIF for general cases. We then show how our algorithms are applied to minimize the total energy consumption in a Cloud Data center. Our theoretical results are validated by intensive simulation using trace-driven and synthetically generated data.