Virtual machine (VM) consolidation is one of the key mechanisms of designing an energy-efficient dynamic Cloud resource management system. It is based on the premise that migrating VMs into fewer number of Physical Machines (PMs) can achieve both optimization objectives, increasing the utilization of Cloud servers while concomitantly reducing the energy consumption of the Cloud data center. However, packing more VMs into a single server may lead to poor Quality of Service (QoS), since VMs share the underlying physical resources of the PM. To address this, VM Consolidation (VMC) algorithms are designed to dynamically select VMs for migration by considering the impact on QoS in addition to the above-mentioned optimization objectives. VMC is a NP-hard problem and hence, a wide range of heuristic and meta-heuristic VMC algorithms have been proposed that aim to achieve near-optimality. Since, VMC is highly popular research topic and plethora of researchers are presently working in this area, the related literature is extremely broad. Hence, it is a non-trivial research work to cover such extensive literature and find strong distinguishing aspects based on which VMC algorithms can be classified and critically compared, as it is missing in existing surveys. In this chapter, we have classified and critically reviewed VMC algorithms from multitude of viewpoints so that the readers can be truly benefitted. Finally, we have concluded with valuable future directions so that it would pave the way of fellow researchers to further contribute in this area.
|Title of host publication||Sustainable Cloud and Energy Services|
|Subtitle of host publication||Principles and Practice|
|Place of Publication||Cham Switzerland|
|Number of pages||31|
|Publication status||Published - 2018|