  This paper investigates the energy-aware virtual machine (VM) allocation
problems in clouds along characteristics: multiple resources, fixed interval
time and non-preemption of virtual machines. Many previous works have been
proposed to use a minimum number of physical machines, however, this is not
necessarily a good solution to minimize total energy consumption in the VM
placement with multiple resources, fixed interval time and non-preemption. We
observed that minimizing the sum of total busy time of all physical machines
implies minimizing total energy consumption of physical machines. In addition
to, if mapping of a VM onto physical machines have the same total busy time
then the best mapping has physical machine's remaining available resource
minimizing. Based on these observations, we proposed heuristic-based EM
algorithm to solve the energy-aware VM allocation with fixed starting time and
duration time. In addition, this work studies some heuristics for sorting the
list of virtual machines (e.g., sorting by the earliest starting time, or
latest finishing time, or the longest duration time first, etc.) to allocate
VM. We evaluate the EM using CloudSim toolkit and jobs log-traces in the
Feitelson's Parallel Workloads Archive. Simulation's results show that all of
EM-ST, EM-LFT and EM-LDTF algorithms could reduce total energy consumption
compared to state-of-the-art of power-aware VM allocation algorithms. (e.g.
Power-Aware Best-Fit Decreasing (PABFD) [7])).
