Research Article
Hybrid Approach for Resource Allocation in Cloud Infrastructure Using Random Forest and Genetic Algorithm
Table 1
Classification of different approaches in existing literature.
| Reference | Technique/algorithm used | Energy consumption | Resource utilization | Load balance | SLA | No. of hosts shutdown/no. of active PM | Execution time/elapsed/simulation time |
| [9] | Metaheuristic (genetic and tabu search algorithm) | ✓ | | ✓ | | | ✓ | [10] | Optimization (permutation-based genetic algorithm and multidimensional resource-aware best fit) | ✓ | ✓ | | | | ✓ | [11] | Heuristic/Weighted PageRank | | ✓ | | | ✓ | | [12] | Metaheuristic/stochastic VM placement algorithm | ✓ | | | | | | [13] | Heuristic/binary gravitational search algorithm | ✓ | | | | | | [14] | Optimization/evolutionary approach | ✓ | | | | | ✓ | [15] | Machine learning/reinforcement learning | | ✓ | ✓ | | ✓ | ✓ | [16] | Machine learning and metaheuristic/(Naive Bayesian Classifier and Random Key Cuckoo Search) | ✓ | | | ✓ | ✓ | | [17] | Heuristic/HeporCloud | ✓ | | | | | ✓ | [18] | Heuristic | ✓ | | | | | ✓ | [19] | Optimization/game theory | ✓ | | | | | ✓ |
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