Without the sufficient capacity of all active servers, the system will switch on a new server while reallocating all the applications using the same heuristic in an arbitrary order. The proposed approach is fit for heterogeneous environments; however, it has several shortcomings. First, the approach assumes that all applications’ resource phase 3 requirements are known in advance and constant. Second, performance and energy overhead, which the authors do not take account into, is caused by migration of state-full applications between nodes. The frequent switching servers on/off also generates significant costs which are not negligible for a real-world system.Verma et al. [8] have contributed energy and migration cost-aware application placement by exploiting the energy management capabilities of virtualization.

The authors have designed a new application (virtual machines) placement architecture called pMapper. It consists of three major parts, namely, a performance manager to dynamically resize the VM, an energy manager for CPU throttling, and a migration manager to identify the target host for migration using a knowledge base. They have expounded that for energy-aware scheduling approaches, estimates of energy values are not required, and only if the scheduling algorithm has abilities in finding out which server minimizes the incremental increase in total energy owing to the new VM being placed, it can place the given VM to an appropriate host. In pMapper, two algorithms are implemented. One is First Fit Decreasing (FFD) by which more energy-efficient servers are utilized first without balancing the load.

The other is incremental First Fit Decreasing (iFFD) which considers the fixed target utilization of each server and achieves server consolidation by live VM migration. The proposed pMapper architecture minimizes energy and migration costs while ensuring the performance. Our approach is based on a heuristic approach which exploits the concept of minimizing total increase in the incremental energy due to the new VM migrations. The proposed architecture is simple and does not need any knowledge base to achieve significant reduction in the energy consumption.Li et al. in [9] have proposed an approach named EnaCloud, which enables application of live placement dynamically with consideration of energy efficiency in a cloud platform.

In EnaCloud, they use a virtual machine to encapsulate the application, which supports applications’ scheduling and live migration to minimize the number of running machines, so as to save energy. In particular, the application placement is abstracted as a bin packing problem, and an energy-aware heuristic algorithm is proposed to get an appropriate solution. In GSK-3 addition, an overprovision approach is presented to deal with the varying resource demands of applications. However, the overprovision approach has risk in optimizing this problem.