Table 5.

Comparison of resource-scheduling techniques.

Authors	Case Study	Algorithm Used	Performance Measurement	Pros	Cons
Static approaches
Bitam et al. [118]	General	Bees life algorithm	CPU execution time, Allocated memory	-Managing allocated memory -Low CPU execution time	-Static scheduling -Low scalability
Fan et al. [83]	General	Ant colony optimization	Total profit, Guarantee Ratio	Maximizing profits of fog providers	High time complexity
Rahbari et al. [123]	EAHD application, Intelligent surveillance application	Symbiotic organisms search	Energy utilization, Network usage, Cost	-Minimizing energy utilization -Low execution cost	High execution time
Kabirzadeh et al. [125]	Intelligent surveillance application	Hyper-heuristic based	Energy consumption, Execution time, Network usage, Cost	-Minimizing energy consumption -Low cost and low time	Low scalability
Dynamic approaches
Sun et al. [117]	Word count	NSGA-II	Service latency, Stability	-Low execution time -High scalability -Low latency	High cost
Cardellini et al. [119]	Word count, Log stream processing	Adaptive-based	Node utilization, Application latency, Inter-node traffic	-Enhancing runtime scheduling -Low Latency -Low execution time	-Low availability -Low scalability -Centralized topology
Zeng et al. [122]	Image Tasks	Heuristic-based	Task completion time	-Low computation complexity -Low response time	-High memory consumption
Chen et al. [67]	Vehicular cloud application	Heuristic-based	Response time, Queue length	-High dynamic efficiency -Using a formal method -Low time	Simple case study
Urgaonkar et al. [126]	Mobile application	Lyapunov optimization	Queue length, Cost	-Reducing state space -Performing a cost-optimal solution	-High cost -Low scalability
Hybrid approaches
De Benedetti et al. [127]	Distributed robotics application	Adaptive-based	Scalability, Fault tolerance	-High interaction with IoT devices -Low latency -Low execution time	-Low scalability -High cost