Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Table 5

Work summary of scientific workflow execution in cloud computing.

Problems	Solutions	Literatures	Advantages
VM deployment	A resource allocation method named EnRealan	[76]	Performs scientific workflows based on energy perception across cloud platforms
Workflow scheduling	A scheduling method based on energy perception	[77]	Achieves a high parallelism without huge energy consumption and minimizes the total consumption of energy and execution time of workflows
	A workflow scheduling method with several objects and hybrid particle swarm optimization algorithm	[78]	Makes the processors work at any voltage level, minimizes the energy consumption in the process of workflow scheduling, and studies the scheduling problem of workflows on heterogeneous systems
	A scheduling algorithm based on various applications	[79]	Enables service providers to gain high profits and reduces user overhead at the same time
Cost reduction	A scheduling algorithm based on energy perception	[80, 81]	Minimizes the cost of performing workflows while meeting the time constraint
Effective implementation	A flexible resource allocation and job scheduling mechanism	[82]	Implements scientific workflows within prescribed budgets and deadlines