Advances, Systems and Applications
Table 1 Review of published work on virtual machine sizing and configuration
From: Task grouping and optimized deep learning based VM sizing for hosting containers as a service
Author(s) | Method | Objective | Remark |
|---|---|---|---|
S.F. Piraghaj et al. [8] | Machine Learning | Modeled the VM size selection technique | It failed to reflect on predicting workload for estimating the actual container utilization. |
Lu, C.T. et al. [13] | Markov Transition | Hurst exponent and Markov transition-based VM scaling | Failed to aggregate more data to enhance the strengthening of the transition matrix to improve accuracy |
Sotiriadis S. et al. [14] | Elastic Search cluster | Inter-Cloud Load Balancer (ICLB) for configuring the VM | It failed to tune the method for defining thresholds based on real-time data utilization. |
Guo, Y. et al. [15] | Shadow routing based approach | Modeled the Shadow algorithm for performing the VM auto-scaling | It did not find any ways to scale the resources to minimize the count of tear-downs and VM boots |
Alsadie D., et al. [16] | Clustering, Machine Learning | This method successfully minimized the energy utilization in cloud data centers. | It produced a high overhead with the increased number of VMs. |
Derdus, K.M. et al., [17] | Neural networks, Machine Learning | Introduced the VM right-sizing IaaS multitenant public cloud for sizing the VM | It failed to use a VM allocation algorithm for estimating the peak resources needed before allocating them. |
Kenga, D.M. et al., [10] | Partition Selection Approach | VM sizing in IaaS multitenant public cloud and resource usage prediction. | It was unsuccessful in seeing the deep learning approaches |
Saxena, D. and Singh, A.K., [18] | Machine Learning | Online Multi-Resource Feed-forward Neural Network (OM-FNN). | Failed to minimize the percentage of power saved with the rise in data center size. |