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. 2021 Mar 5;21(5):1832. doi: 10.3390/s21051832

Table 2.

Evaluation framework for applied algorithms in fog–cloud and edge scenarios.

Resource Management Techniques in Fog/Cloud Edge Scenarios
Author & Year Algorithm Deployment Classification Resource Management Additional Classification Environment
Resource
Allocation
Workload
Balance
Resource
Provisioning
Task
Scheduling
QoS Energy
Management
Javaid, S. et al., (2018) [19] RR Simulation (Cloud Analyst) Discovery Cloud–Fog
Javaid, S. et al., (2018) [19] ESCE Simulation (Cloud Analyst) Discovery Cloud–Fog
Javaid, S. et al., (2018) [19] SJF Simulation (Cloud Analyst) Discovery Cloud–Fog
Da Silva, R.A.C. et al., (2018) [20] GPRFCA Simulation iFogSim [12] Discovery & Load-balancing Cloud–Fog
Wang, T. et al., (2019) [21] RSYNC Experiments in different conditions, two situations of synchronization Discovery & Off-loading Fog
Wang, T. et al., (2019) [21] FSYNC Experiments in different conditions, two situations of synchronization Off-loading Fog
Wang, T. et al., (2019) [21] RS - FSYNC Experiments in different conditions, two situations of synchronization Off-loading Fog
Xu et al., (2018) [22] DRAM Evaluation done with three different types of computing nodes Load-balancing Fog
Agarwal et al., (2016) [16] ERA Simulation (Cloud Analyst) Load-balancing Cloud–Fog
Savani et al., (2014) [23] PBSA Simulation (CloudSim 3.0.3) Load-balancing Cloud
Taneja et al., (2017) [24] Iterative Algorithm Evaluation done in three different topologies with different workloads Placement Cloud–Fog
Arunkumar et al., (2020) [37] FOFSA Simulation iFogSim Load-balancing Fog
Chandak et al., (2018) [38] HCLB Simulation CloudAnalyst tool Load-balancing Cloud–Fog
Manju et al., (2019) [39] ELBA (min-min) Simulation CloudAnalyst tool Load-balancing Cloud–Fog
Téllez et al., (2018) [40] Tabu Search Simulation Cloudlet Tool Load-balancing Cloud–Fog
Jiang et al., (2019) [41] ECFO Cloud server and three Raspberry Pi3 devices Off-loading Fog–Edge