# Task-Driven Virtual Machine Optimization Placement Model and Algorithm
## 5 Experiment Results and Analysis
### Evaluation of the CE-VMP Method  
#### 5.4.1 Effectiveness  
We adopt the classic evaluation metrics employed in [7], including: service level agreement violation rate (SLAV), a low value of SLAV represents fewer violations of the service level agreement, which ensures the quality of service. The number of virtual machine migrations (VMMs), a low number of VMMs indicates fewer service suspensions caused by VM migrations, which in turn guarantees the quality of service. Performance degradation due to VM migrations (PDM), the lower the PDM value, the less resource shortage due to VM migration. SLA violation time of a single active PM (SLATAH), which reflects the SLA violation due to resource shortage during the activity of PMs.  
The proposed CE-VMP method is compared with the PABFD-VMP [7], FFD-VMP [8], COFFGA-VMP [9] and EEACS-VMP [10] methods. To ensure the fairness of the experiment evaluation, all methods are combined with the Inter Quartile Range (IQR) [44] host overload detection algorithm and the Minimum Migration Time (MMT) VM selection algorithm for the experiments. Different numbers of tasks are selected for the experiments: 200, 400, 600, 1000, 1200, and 2000.  
Figure 5a shows the experimental results for the SLAV metric. For example, with 1000 tasks, the SLAV value for the CE-VMP is reduced by 41.78% to 74.01% compared with the other compared algorithms. This is because the CE-VMP considered the impact of task loading and matched resource supply more accurately based on actual task requirements, thereby reducing SLAV. Figure 5b shows the VMM results. When the number of tasks is high, the CE-VMP performs better because the CE-NSGAIII initially places VMs on PMs that can satisfy their resource needs, thereby avoiding unnecessary VM migrations. However, when the number of tasks is low, the results are similar to those of the other algorithms because the VM placement issue is relatively simple, and all algorithms quickly find effective solutions, resulting in fewer VM migration occurrences. Thus, the CE-VMP is not particularly advantageous in this case. Figure 5c displays the PDM metric results. When the number of tasks is low, the CE-VMP performs moderately. This is because when fewer VMs and PMs exist, the algorithm has a smaller search space, and the use of chaotic mapping leads to over-exploration, thereby reducing the algorithm's convergence. Consequently, less-than-optimal placement solutions that do not effectively match the VM requirements with PM resources are obtained. The PABFD-VMP is ranked second, as it sorts VMs according to their CPU resource requirements, ensuring that crucial VMs receive sufficient resources first, thereby reducing resource shortages caused by migrations.  
Figure 5d shows the SLATAH results. For example, with 600 tasks, the SLATAH value for the CE-VMP decreased by 20.05% to 39.46% compared with those of the other algorithms. The VM placement scheme that considers task loading ensures more reasonable resource allocation and reduces instances where VM resource requests exceed allocated resources.