# A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
## 4 Experiments and Results
### Experiment Results and Analysis  
The simulations have been run on 10 h of each workload category to determine the algorithm that delivers the best energy consumption, SLA violation and number of VM migrations over different workload types. The optimization algorithms have been run on each iteration of the simulation time. The results are shown in Figure 3 as below:  
From the simulation results in Figure 3, we can see that when the entire system is under low workload or high workload, our proposed algorithm is superior to the other four algorithms in energy consumption, SLA violation rate, and VM migrations. When the overall system load is medium workload, the consumption of energy and VM migrations of this algorithm are not much different from that of other algorithms, but the rate of SLA violation is obviously lower.  
In terms of energy saving, when the system is under a low workload, the DMT strategy improves the energy saving on about 41.9% (3.1 KWh) over the MAD algorithm and improves 25.8% (4.5 KWh) over the LiRCUP when the system is at a high load. For SLA violation rate, when the system is at a high load, DMT algorithm and DMT (MW) algorithm both effectively reduce the violation rate of the overall system in almost 2%. While for VM migration costs, the strategy of this article results in fewer VM migrations, and the DMT (MW) strategy further reduces the number of virtual machine migrations and thus reduces migration costs.  
Figure 4 shows the number of active hosts at each moment (that is, the number of PMs that still service VM tasks). When VMs are consolidated by using different strategies, the original hosts with low loads will migrate out of all VMs they own and will be set to sleep mode or shutdown.  
Figure 3: Three aspects of performance on each VM consolidation strategy over different workload types. (**a**) energy consumption. (**b**) SLA violation. (**c**) number of VM migrations.  
Thus, counting the current number of active hosts can effectively reflect the effectiveness of the VM consolidation strategy.  
It can be clearly seen from Figure 4 that when the whole system is under low workload (a), our proposed VM consolidation strategy can shut down a larger number of hosts and can reach a steady state faster than any other strategies within less than 5 h; when the system workload is moderate (b), the number of shut-down hosts among these six strategies is not much different, with LiRCUP having the lowest number of active hosts; when the overall system is at high load (c), all these algorithms can only shut down a few hosts. Although our proposed algorithm cannot reach a stable state quickly in this situation, it still has the least number of active hosts, which effectively reduces the energy consumption of the system.  
Therefore, our proposed VM consolidation strategy based on DMT has more obvious advantages in case of high workload and low workload, especially when it is combined with MW-MVM and MW-BF algorithm on MW-DMT. This result is also consistent with the conclusion in Figure 1, which further demonstrates the superiority of our proposed VM consolidation strategy based on the dynamic multi-thresholds.