Profilebased static virtual machine placement for energyefficient data center The energy consumption of a data center and hence the carbon footprint from it largely depends on the energy consumption by its active Physical Machines (PMs). Researchers have taken many attempts to minimize the data center energy consumption through the Virtual Machines (VMs) allocation into a minimal number of PMs of homogeneous types.  However, the current VM placement strategies do not consider the fluctuations of resource requirements of a VM through its lifetime. To resolve the this issue, this paper introduces a novelty of profilebased VM assignment algorithm for minimizing the energy consumption in data center. Our algorithm considers the subsequent time intervals of data center based on profiling of VMs and PMs. An algorithm has been proposed and developed for finding near optimal solution for VMs placement with the objective of minimizing data center energy consumption. Our algorithm has been compared with a bin packing algorithm, FirstFit Decreasing (FFD), and experimental results have shown that our algorithm can reduce more energy consumption than the FFD algorithm and is scalable for larger test problems.

Monte Carlo Based Server Consolidation for Energy Efficient Cloud Data Centers The growing energy consumption of data centers is a compelling global problem and effective server consolidation is at the heart of energy efficient cloud data centers. A variant of bin packing can be used to model the server consolidation problem, where the constraints are multidimensional and heterogeneous vectors rather than scalars and the goal is to satisfy the requested resource allocation using the minimum number physical servers. Since bin packing is NPhard, we rely on heuristics for practical solutions. Variations of First Fit Decreasing (FFD) based heuristics have been shown to be effective both in theory and practice for the one dimensional homogeneous case. However, the multidimensional and heterogeneous aspects of the server consolidation problem make it more complicated, requiring additional research to adapt FFD to the server consolidation problem. In this paper, we present a new FFDbased server consolidation technique using a Monte Carlo method and Shannon entropy, which considers resource bottlenecks and dynamically adjusts to variance in the utilization of different resources. The proposed heuristic outperforms existing techniques in all scenarios, achieving within 25% of optimal on average for medium to high variance in resource utilization, and within 10% worse than optimal on average for all scenarios.