Abstract-Cloud Computing Datacenters host millions of virtual machines (VMs) on real world scenarios. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering also the large number of possible optimization criteria and different formulations that could be studied. VMP literature include relevant topics such as energy-efficiency, Service Level Agreements (SLA), cloud service markets, Quality of Service (QoS) and carbon dioxide emissions, all of them with high economical and ecological impact. This work presents an extensive up-to-date review of the most relevant VMP literature in order to identify research opportunities. The process of selecting which virtual machines (VMs) should be located (i.e. executed) at each physical machine (PM) of a datacenter is known as Virtual Machine Placement (VMP). The VMP problem has been extensively studied in cloud computing literature and several surveys have already been presented. Existing surveys focus on specific issues such as: (1) energy-efficient techniques applied to the problem [5], [70] , (2) particular architectures where the VMP problem is applied, specifically federated clouds [27] and (3) methods for comparing performance of placement algorithms in large ondemand clouds [63] . Beloglazov et al. presented in [5] a survey of energy-aware resource allocation policies and scheduling algorithms considering QoS. The following open challenges were identified considering energy-aware management of cloud computing datacenters: (1) development of fast energy-efficient algorithms for the VMP, considering multiple resources for largescale systems with the ability to predict workload peaks to prevent performance degradation, (2) energy-aware optimization of virtual network topologies between VMs for optimal placement in order to reduce network traffic and thus energy consumed by the network infrastructure, (3) development of new thermal management algorithms to appropriately control temperature and energy consumption, (4) development of workload-aware resource allocation algorithms, considering that current approaches assume a uniform workload, and (5) decentralization and distributed approaches to provide scalability and fault-tolerance to the VMP problem resolution. Salimian et al. presented a review of different selection and placement algorithms for energy-efficient management of cloud computing datacenters [70] . Approaches for virtual and physical resources modeling, applied techniques and future work were identified for each studied article. Most relevant future work include: (1) investigation of VMP for multi-core architectures considering multiple resources, (2) consideration of dynamic thresholds for QoS and (3) development of intelligent schemes according to workload and considering live migration. Gahlawat et al. proposed in [27] a brief survey of the main cloud federation architectures and approaches considered for VMP problem formulation in this particular scenario. It is important to remember that cloud federation is the practice of voluntarily interconnecting cloud infrastructures of different Cloud Service Providers (CSPs), mostly to respond to workload peaks. Finally, Mills et al. presented in [63] an objective method to compare performance of placement algorithms in large ondemand clouds, where 18 different algorithms for VMP were compared considering 39 variables such as: reallocation rate, user request rate, allocation rate and disk space utilization, just for cite a few. The above mentioned surveys and research articles focused into specific issues related to the VMP problem. To the best of the author's knowledge, there is not existing research work that presents a general and extensive study of a large part of the VMP literature. This work presents an extensive up-todate review of the most relevant VMP literature in order to identify research opportunities on this important and promising research area. A selection process of current research work was defined and performed in order to study a large part of the most relevant VMP literature for this survey. This section presents a detailed description of the literature selection process which is summarized in Figure 1 . The selection process of relevant articles started with a search of research articles from Google Scholar database [scholar.google.com] with at least one of the following selected keywords in the article title: (1) virtual machine placement, (2) vm placement, (3) virtual machine consolidation, (4) vm consolidation or (5) server consolidation. This keywords search step results in 446 research articles. A detailed list of the 446 resulting articles could be found in [57] , specifically in the worksheet named keywords search. Considering the large number of results from keywords search step, the literature selection process focused in research articles published in the following relevant publishers: (1) ACM, (2) IEEE, (3) Elsevier and (4) Springer. Percentage of articles per publisher in the studied universe is summarized in Figure 2 . This publisher filtering step results in a reduction from 446 to 172 (38.6%) research articles of the literature. A detailed list of the 172 resulting articles could be found in [57] , specifically in the worksheet named publisher filtering. Considering the 172 resulting articles from the publisher filtering step, an abstract reading was performed in order to identify only the most relevant articles that specifically study the VMP problem. After the abstract reading, 93 research articles were selected from the VMP literature. A detailed list of the 93 resulting articles could be found in [57] , specifically in the worksheet named abstract reading. Finally, short papers (i.e. research articles with less than 6 pages) were removed from the selected literature, resulting in 84 selected articles of the VMP literature for the detailed study presented in this survey. Consolidating multiple underutilized servers into a fewer number of non-dedicated servers can respond to the common problem of server sprawl in datacenters [34] . The consolidation can be modeled as a variant of the bin packing problem (items to be packed are the VMs and bins are the PMs) in order to help resource management considering that until 2008, server consolidation exercise was primarily a manual process. A manual process of this nature is time consuming and depends on the subjective assessment of the decision maker. Particularly, in [34] it is studied the server consolidation problem considering technically deprecated PMs into technically superior PMs, but this problem could also be
Some of the important issues addressed in literature are energy efficiency, specific architectures and performance of placement algorithms comparing with different methods REF .
Virtual Machine Placement Literature Review
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Computer Science