The Adaptive Backtracking method is simulated by using Matlab environment. Hybrid Cloud Environment created by using CloudSim and Computing system has Intel Core i7 GPU Computer, 16 GB RAM, 1 TB HDD and Window 1O OPS. The performance factors are calculated as follows.
Energy consumption factor
The energy efficiency is calculated based on Physical machine resource utilized factor values and number of VM placed,
$$ {\text{ECF }}\left( {{\text{Machine}}_{{\text{P}}} } \right) \, = {\text{ Machine}}_{{\text{P}}}^{{{\text{Idle}}}} + {\text{ Machine}}_{{\text{P}}}^{{{\text{Full}}}} + {\text{ VM}}\left( {\text{n}} \right) \times {\text{Threshold}}. $$
(4)
VM Migration is calculated from using number over and underutilized resource and availability factor. The fitness function gives the results for VM values status. CPU utilization is calculated based on migrated VM results which pull down the performance and execution time. The accuracy factor is measured by using turnaround time of each executed results.
$$ {\text{Accuracy}}\left( {\text{i}} \right) \, = {\text{i}} = {\text{1NVMAvailable}} + {\text{VMEmptyMachineP,}} $$
(5)
$$ {\text{SLAviolation }} = {\text{ Accuracy}} \times {\text{ECF}}. $$
(6)
From the above experiments below Table 7 shows that the result of accuracy and power consumption results based on number of VMs.
The result from above Table 7 the average accuracy in 96% and Energy efficiency is achieved as 0.22. This is very less energy consumed while increased VMs and physical resources in Fig. 3. The above results are compared with existing methods and shown in Table 8.
Average VM consolidation and energy consumption based on iteration results.
This case our proposed method results are compared with existing methods such as Decision tree, K-means, Support vector and TensorFlow graph. The results are compared average cpu utilization, turnaround time and energy efficiency index in Fig. 4. The results show that our proposed methods achieved better results.
Comparison of turnaround time and energy efficiency index.
In this paper we compared the execusion results with some existing VM Scheduling and consolidation methods. The below Table 9 shows that the comparison representation of our proposed method CPU utilization and energy efficiency factors.
From the above Table 9 results, the current research methodologies in cloud computing such as Dynamic Virtual Machine Scheduling Using Residual Optimum Power-Efficiency In The Cloud Data Center, SR-PSO: server residual efficiency-aware particle swarm optimization for dynamic virtual machine scheduling, VMS-MCSA: virtual machine scheduling using a modified clonal selection algorithm results are compared with our proposed method Adaptive Backtracking. Based on the results our proposed method gives 96% accuracy factor (CPU Utilizations) and lesser energy efficiency index.
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