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Advances, Systems and Applications

Table 1 Task scheduling in fog and cloud computing

From: An efficient population-based multi-objective task scheduling approach in fog computing systems

Ref. Cloud/ Fog Optimization method Optimization criteria Mobility
[19] Cloud GA Makespan and load balancing No
[20] Cloud GA and PSO + (SJFP) Makespan No
[21] Cloud Canonical PSO and fully-informed PSO Throughput and delay No
[22] Cloud ABC + (FCFS, SJF, LJF) Makespan and load balancing No
[23] Cloud IWC (Improved WOA) Execution time, system load, and price cost No
[24] Cloud WOA Makespan, deadline, and resource utilization No
[25] Fog Moth-flame Execution time, transfer time, and makespan No
[26] Fog Knapsack + symbiotic organisms search Energy consumption, execution cost, total network usage, and sensor lifetime No
[27] Fog Hybrid heuristic (IACO + IPSO) Completion time, energy consumption, and reliability No
[28] Fog NSGA-II Service latency and stability No
[29] Fog Lyapunov optimization Service delay and energy consumption No
[30] Fog Ant Colony Energy consumption, resource consumption, and total completion task No
[31] Fog GA Execution time and operating cost No
[32] Fog Bees Life Execution time and allocated memory No
[33] Fog GA + Gaussian Mixture Model Temperature of CDC, latency, energy, and bandwidth No
[34] Fog Deep Reinforcement learning Energy consumption, response time, migration time, SLA violations and cost No
[35] Fog Deep Reinforcement learning Average energy consumption of IoT device and response delay No
[36] Fog Lyapunov optimization + drift-plus-penalty Time-average and energy consumption No
[37] Fog Adaptive GA Offloading success rate and energy consumption Yes
[38] Fog - Energy consumption Yes
[39] Fog - Delay and power consumption Yes
Our work Fog WOA+OB learning + chaos Response delay and energy consumption No