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Research Article

Applying the Cheetah Algorithm to optimize resource allocation in the fog computing environment

, &
Article: 2349982 | Received 22 Jan 2024, Accepted 26 Apr 2024, Published online: 14 May 2024

Figures & data

Figure 1. The IoT layer with integrated fog computing and cloud computing (Ma et al. Citation2019).

Figure 1. The IoT layer with integrated fog computing and cloud computing (Ma et al. Citation2019).

Figure 2. Random ovipositional in ELR, the central red star is the cuckoo’s primary habitat with five eggs.

Figure 2. Random ovipositional in ELR, the central red star is the cuckoo’s primary habitat with five eggs.

Figure 3. Cuckoo algorithm flowchart (Shair et al. Citation2015).

Figure 3. Cuckoo algorithm flowchart (Shair et al. Citation2015).

Figure 4. The algpseudocode of GWO (Jokar, Mosleh, and Kheyrandish Citation2022).

Figure 4. The algpseudocode of GWO (Jokar, Mosleh, and Kheyrandish Citation2022).

Table 1. The characteristics of virtual machines in the FC.

Table 2. The dataset considered for conducting the tasks.

Figure 5. Comparison between the selected models in terms of convergence.

Figure 5. Comparison between the selected models in terms of convergence.

Figure 6. Comparison between the selected models in terms of task and VM.

Figure 6. Comparison between the selected models in terms of task and VM.

Figure 7. Comparison between the selected models in terms of objective value.

Figure 7. Comparison between the selected models in terms of objective value.

Figure 8. Comparison between the selected models in terms of speed.

Figure 8. Comparison between the selected models in terms of speed.

Data Availability Statement

This research did not involve the use of specific datasets. The findings and conclusions presented in this study are based on analysis, interpretation, and synthesis of existing literature, theoretical frameworks, and/or simulations. As no primary data were collected, there are no additional datasets associated with this research.