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| References | Objectives | Techniques | Achievements | Limitations |
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| [2] | Auto-scaling of VM and VM-to-PM packing. | Approach based on shadow routing. | Less no. of hosting PMs by intelligently packing VMs-into-PM. | There is a chance of congestion when PM is fully packed. Due to congestion there will be more delay. |
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| [3] | Balance the load of network resources. | Layered virtual machine migration. | Effective management of the physical and network resources and high performance in balancing the bandwidth utilization rate of hosts. | The migration cost is high. |
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| [4] | Minimize resource consumption and dynamic traffic. | Cluster-aware VM collaborative migration scheme for media cloud. | An ideal migration by using clustering algorithm and placement algorithm and effective migration of VM media servers. | The proposed scheme does not optimize the VM migration in media cloud. Migration cost is very high. |
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| [5] | Reduce energy consumption with great migration cost. | An improved grouping genetic algorithm (IGGA). | Optimizes the consolidation score and reduces energy consumption with high consolidation score. | The migration cost is still high because of migration of one VM at a time. |
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| [6] | Minimize energy consumption and great migration cost. | Ant colony system (ACO). | Minimizes energy consumption by decreasing number of active PMs and ensures the SLA based on quality of services requirement. | The migration cost is still high because of migrating of one VM at a time. |
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| [7] | Lessen energy consumption and great migration cost. | Firefly optimization approach. | Energy-aware VM migration technique for cloud computing and migrates the overloaded VMs to the normal PMs. | The load on cloud data center is still there because by migration we can only achieve high utilization rate of network resources. |
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