We analyzed the difference among algorithms in each subsection of section 4. In this section, we focus on the applicability of the above studied solutions to the real environment. In the real environment, there are two main types of IaaS cloud data centres: the public and private clouds.
The public clouds or the commercial clouds provide resources to everyone having ability to afford the cost of resource usage. The trend of IaaS cloud data centers is providing wide range of products over one resource infrastructure. A represent example is Amazone EC2  with spot market VM instances, reserved VM instances and resource on demand VM instances. This policy provides users flexible options of using cloud resources. Thus, it motivates users to transfer from traditional computing to cloud computing. The main goal of public cloud providers is maximizing profit. As discussed in Section 3.2.3, this goal can be gained by selling the same amount of resource with highest price or by maximizing the workload on a fixed amount of resources. For the first approach, algorithms for spot market such as  can be used. The second approach can be realized with energy-efficient algorithms such as [36,37]. It is because energy efficient mechanisms try to use smallest number of PMs to host workload. The literature also showed that the initial placement mechanism makes significant progress to reach the goal of the Cloud data centre compared with the VM migration mechanism. With the energy efficient initial placement algorithm, the VM migration algorithm increases the efficiency only few percent.
There should be no problem with the wide range of products policy if the total demand is smaller than the available capacity. The situation becomes more complicated if the total demand is larger than the available capacity. In this situation, how to allocate resources among many products with profit optimization is still an open issue. Another situation is that there is a peak of demand in a short period. Reserving enough resources to deal with this situation may lead to inefficient resource usage. An initial possible solution to this issue is proposed in  with the addition of bets-effort VMs. However, a detail study for a robust scenario like Amazone  is still necessary.
The private clouds provide resources to users inside the border of an organization. Depending on the policy of the organization, the goal of the private clouds may be different. With each goal, the system can apply different solutions. In general, the set of resource allocation algorithms for both initial VMs placement and VM migration can be divided into two classes: simple heuristics and application of complex algorithms. For example, to make initial VMs placement with the goal of load balance, the system can use simple heuristic such as Round robin [2,4,23,24], random , least connect , weighted selection [23,46] or apply genetic algorithm . The simple heuristics have the advantage of fast execution and easy to implement. The application of complex algorithms usually has better performance. However, they are slower and more complicated to implement. It seems that simple heuristics are preferred in real systems .
Cloud computing is the promising model for delivering IT services as computing utilities. The resource allocation module is an important part of each IaaS Cloud system. In this paper, we have studied and classified different algorithm to map virtual machines to physical machines inside the cloud computing systems. Recent research developments have been discussed and categorized over the execution phases, business models and goals of resource allocation.
Efficient resource allocation in IaaS Cloud computing systems is a well known and extensively studied in the past problem. The allocation decision is made for both homogeneous as well as heterogeneous IaaS cloud infrastructure under different business model such as spot market, game theory, resource reservation and resource on demand. The proposed allocation algorithms range from simple heuristics to applications of well-known methods such as genetic algorithm, Linear Programming, Constrain Satisfaction Programming, etc. We also discussed the applicability of studied solutions to two main Cloud data centre types: public Cloud (or commercial Cloud) and private Cloud. From the analysis, open issues and future direction are stated.
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Dang Minh Quan is a lecturer at the Institute of Information Technology for Economic, National Economics University, VietNam. He received his Ph.D. (2006) from the University of Paderborn, Germany. His current research centers on energy saving for data centers. In particular, he puts special focus on designing energy efficient algorithms for traditional data centers, cloud data centers and HPC data centers.