Abstract: Energy efficiency in data centers is a very important issue and getting growing attention from researchers. One approach to reduce energy consumption is to allocate tasks to Virtual Machines (VMs) created in Physical Machines (PMs) in such a way that the number of idle PMs is maximized. Approaches of this kind are called VM consolidation methods. Idle PMs can be put into an energy-saving sleep mode in which PMs consume significantly lower energy than in the normal operation mode. But if too many VMs are packed into a single PM, the performance interference among VMs can cause significant slowdown to jobs. When a new job arrives at a cloud, the tasks of the job should be allocated to idle VMs. If there are enough number of idle VMs, we should decide to which idle VMs those tasks should be assigned. If there are not enough idle VMs, we should create necessary number of idle VMs on proper PMs before allocating the tasks to idle VMs. This problem is called the static VM consolidation problem. In this study, we propose four algorithms for this static VM consolidation problem. When we propose algorithms, we take following issues into considerations: imperfect performance isolation of virtualization technology, flexible and efficient proactive VM creation policy, PMs consisting of multiple CPUs each of which consists of multiple cores and VMs which are created with pre-defined machine types. Further, we assume that we do not have the knowledge of the completion time of a job, although, its resource requirements can be known a priori. We analyze the proposed algorithms through simulation with synthetic workloads obtained by analyzing the characteristics of workloads in real data centers. We measure following three metrics and suggest the best algorithm: ratio of idle PMs, service level agreement violation ratio and the total energy consumption in a cloud.